Step 2a-Approach2.ipynb

{
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     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "##library dependencies\n",
    "\n",
    "from sklearn import neighbors\n",
    "from sklearn.model_selection import train_test_split\n",
    "from numpy import loadtxt\n",
    "from numpy import sort\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import bokeh\n",
    "import sklearn\n",
    "import xgboost as xgb\n",
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "import pandas as pd\n",
    "import matplotlib.mlab as mlab\n",
    "import sqlite3\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approach 2\n",
    "\n",
    "-  In this approach all the preprocessing and imputation is done on properties data to take advantage of all 27 million records and then merge transaction data (90k records) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  Importing properties_2016 which has data about 27 million unique house properties with 58 attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2698: DtypeWarning: Columns (22,32,34,49,55) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "##import properties 2016  data\n",
    "\n",
    "prop2016= pd.read_csv('C:\\\\Users\\\\Dell\\\\Desktop\\\\inf552\\\\Zillow Prize\\\\properties_2016.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  Importing transaction data which has 90k records of the properties sold in the year 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "##import train 2016  data\n",
    "train2016=pd.read_csv('C:\\\\Users\\\\Dell\\\\Desktop\\\\inf552\\\\Zillow Prize\\\\train_2016.csv',parse_dates=[\"transactiondate\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  Renaming attributes names to be meaningful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "code_folding": [
     7
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   "outputs": [],
   "source": [
    "prop2016=prop2016.rename(index=str, columns={\"pooltypeid7\": \"pool_with_spa_tub_no\", \n",
    "                                             \"pooltypeid2\": \"pool_with_spa_tub_yes\"})\n",
    "\n",
    "prop2016=prop2016.rename(index=str, columns={\"hashottuborspa\": \"has_hottub_or_spa\", \n",
    "                                             \"pooltypeid10\": \"just_hottub_or_spa\"})\n",
    "\n",
    "prop2016=prop2016.rename(index=str, \n",
    "                         columns={\"calculatedfinishedsquarefeet\": \"total_finished_living_area_sqft\", \n",
    "                                  \"finishedsquarefeet12\": \"finished_living_area_sqft\",\n",
    "                                  \"lotsizesquarefeet\": \"lot_area_sqft\",\n",
    "                                  \"finishedsquarefeet50\": \"finished_living_area_entryfloor_sqft1\",\n",
    "                                  \"finishedfloor1squarefeet\": \"finished_living_area_entryfloor_sqft2\",\n",
    "                                  \"finishedsquarefeet6\": \"base_unfinished_and_finished_area_sqft\",\n",
    "                                  \"finishedsquarefeet15\": \"total_area_sqft\",\n",
    "                                  \"finishedsquarefeet13\": \"preimeter_living_area_sqft\",\n",
    "                                  \"taxvaluedollarcnt\":\"total_parcel_tax\",\n",
    "                                  \"landtaxvaluedollarcnt\":\"land_tax\",\n",
    "                                  \"taxamount\":\"total_property_tax_2016\",\n",
    "                                  \"structuretaxvaluedollarcnt\":\"structure_tax\",\n",
    "                                  \"garagetotalsqft\":\"garage_sqft\",\n",
    "                                  \"fireplacecnt\":\"fireplace_count\",\n",
    "                                  \"buildingqualitytypeid \":\"building_quality_id\",\n",
    "                                  \"heatingorsystemtypeid\":\"heating_system_id\",\n",
    "                                  \"airconditioningtypeid\":\"ac_id\"\n",
    "                                 \n",
    "                                 \n",
    "                                 \n",
    "                                 })\n",
    "prop2016=prop2016.rename(index=str, columns={\"decktypeid\": \"deck_flag\"})\n",
    "\n",
    "prop2016=prop2016.rename(index=str, columns={\"yardbuildingsqft17\": \"patio_sqft\",\n",
    "                                             \"yardbuildingsqft26\":\"storage_sqft\"})\n",
    "\n",
    "prop2016=prop2016.rename(index=str, columns={\"bathroomcnt\": \"total_bath\",\n",
    "                                             \"fullbathcnt\": \"full_bath\",\n",
    "                                             \"threequarterbathnbr\": \"half_bath\"})\n",
    "\n",
    "prop2016=prop2016.rename(index=str, columns={\"storytypeid\": \"basement_flag\"})\n",
    "\n",
    "prop2016=prop2016.rename(index=str, columns={\"poolsizesum\": \"pool_sqft\"})\n",
    "\n",
    "             "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing Attributes with Missing Values\n",
    "-  Pool_count is a binary variable, hence replace all NULL values with zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "prop2016.loc[prop2016.poolcnt.isnull(),'poolcnt']= 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  pool_with_spa_tub_no & pool_with_spa_tub_yes are again binary variables hence replace all NULL values with zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "prop2016.loc[prop2016.pool_with_spa_tub_no.isnull(),'pool_with_spa_tub_no']=0\n",
    "prop2016.loc[prop2016.pool_with_spa_tub_yes.isnull(),'pool_with_spa_tub_yes']=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fixing contradictions in pool related variables\n",
    "\n",
    "-  When pool is present and if it has tub/spa then just_hottub_or_spa =0\n",
    "\n",
    "-  When there is no pool and if there is tub/spa then just_hottub_or_spa =1\n",
    "\n",
    "-  As they are binary variables convert NaN's to Zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#when poolcnt=1 & has_hottub_or_spa=1 & just_hottub_or_spa is null then just_hottub_or_spa =0\n",
    "#when poolcnt=0, has_hottub_or_spa=1, just_hottub_or_spa =1\n",
    "\n",
    "prop2016.loc[ (prop2016.poolcnt==1) & (prop2016.has_hottub_or_spa==1) & (prop2016.just_hottub_or_spa.isnull()),'just_hottub_or_spa']=0\n",
    "             \n",
    "#has_hottub_or_spa is null and just_hottub_or_spa is null, both has to be zero\n",
    "\n",
    "prop2016.loc[ (prop2016.has_hottub_or_spa.isnull()) & (prop2016.just_hottub_or_spa.isnull()),['has_hottub_or_spa','just_hottub_or_spa']]=0\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  When there is no pool, make poolsize as zero instead of Nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "489574\n"
     ]
    }
   ],
   "source": [
    "prop2016.loc[ prop2016.poolcnt==0,'pool_sqft']=0\n",
    "print(prop2016.pool_sqft.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  basement_flag has values 7 & Null hence convert it to a binary variable with value of zero and 1\n",
    "-  When basement_flag is zero make basement_sqft also zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "prop2016.loc[prop2016.basement_flag.isnull(),'basementsqft']=0\n",
    "prop2016.loc[prop2016.basement_flag.isnull(),'basement_flag']=0\n",
    "prop2016.loc[prop2016.basement_flag==7,'basement_flag']=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  There seems to be inconsistency between the fireplace_flag and fireplace count, Let's fix it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 5163\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#df_train.fireplaceflag.isnull().sum()\n",
    "#df_train.fireplace_count.isnull().sum()\n",
    "prop2016.loc[(prop2016.fireplace_count.isnull()) & (prop2016.fireplaceflag.isnull()),'fireplaceflag'] = False\n",
    "prop2016.loc[(prop2016.fireplace_count.isnull()) & (prop2016.fireplaceflag==False),'fireplace_count'] = 0\n",
    "prop2016.loc[prop2016['fireplace_count']>0,'fireplaceflag']= True\n",
    "\n",
    "print(\"after\",prop2016.fireplace_count.isnull().sum())\n",
    "#print(\"after\",df_train.fireplace_count.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  Garage count and Garage size have same number of missing values. Let's assume this is because when there are properties with no garages then both variables are NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 0\n",
      "after 182804\n"
     ]
    }
   ],
   "source": [
    "\n",
    "prop2016.loc[prop2016.garage_sqft.isnull() & prop2016.garagecarcnt.isnull(),['garagecarcnt','garage_sqft']]=0\n",
    "prop2016.loc[(prop2016.garagecarcnt>0) & (prop2016.garage_sqft==0),'garage_sqft']=np.nan\n",
    "print(\"after\",prop2016.garagecarcnt.isnull().sum())\n",
    "#print(\"after\",df_train.garagecarcnt.value_counts())\n",
    "print(\"after\",prop2016.garage_sqft.isnull().sum())\n",
    "#print(\"after\",df_train.garage_sqft.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  total_bath & calculatedbathnbr are duplicates , and calculatedbathnbr has more nulls, hence we will drop it\n",
    "-  if full_bath is null and half_bath is also null, let's make total_bath=0 (missing values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128076\n",
      "128912\n",
      "128912\n"
     ]
    }
   ],
   "source": [
    "#total_bath & calculatedbathnbr are duplicates , and calculatedbathnbr has more nulls, hence drop it\n",
    "\n",
    "prop2016=prop2016.drop('calculatedbathnbr',axis=1)\n",
    "\n",
    "# full_bath is null & half_bath is null & total_bath=0 (missing values)\n",
    "prop2016.loc[(prop2016.full_bath.isnull()) & (prop2016.half_bath.isnull()) & (prop2016.total_bath==0),'total_bath']=np.nan\n",
    "\n",
    "\n",
    "# when full_bath=total_bath, half_bath=0 \n",
    "\n",
    "prop2016.loc[(prop2016.full_bath==prop2016.total_bath) ,'half_bath']=0\n",
    "\n",
    "# when total_bath is present but full and half bath is null\n",
    "# all 3 are null somemtimes\n",
    "\n",
    "print(prop2016.total_bath.isnull().sum())\n",
    "print(prop2016.half_bath.isnull().sum())\n",
    "print(prop2016.full_bath.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Assuming if these patio and shed variables has null values then there is no shed or patio in yard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "#yardbuildingsqft17-patio in yard\n",
    "#yardbuildingsqft26- storage shed in yard\n",
    "\n",
    "prop2016.loc[prop2016.patio_sqft.isnull() ,'patio_sqft']=0\n",
    "prop2016.loc[prop2016.storage_sqft.isnull() ,'storage_sqft']=0\n",
    "\n",
    "print(prop2016.patio_sqft.isnull().sum())\n",
    "print(prop2016.storage_sqft.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### code fips code with respective county names\n",
    "    -  6037- LA\n",
    "    -  6059- Orange_County\n",
    "    -  6111- Ventura"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11437\n",
      "LA               2009362\n",
      "Orange_County     741565\n",
      "Ventura           222853\n",
      "Name: fips, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#fips\n",
    "prop2016.loc[prop2016.fips==6037 ,'fips']=\"LA\"\n",
    "prop2016.loc[prop2016.fips==6059 ,'fips']=\"Orange_County\"\n",
    "prop2016.loc[prop2016.fips==6111 ,'fips']=\"Ventura\"\n",
    "print(prop2016.fips.isnull().sum())\n",
    "print(prop2016.fips.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### scaling down the latitude and longitide \n",
    "-  Knn imputation takes more time because of the hude numbers, moreover standardizing gives better results on most algorithms\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "prop2016['latitude']=prop2016['latitude'].divide(100000)\n",
    "prop2016['longitude']=prop2016['longitude'].divide(100000)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  deck_flag has only 2 values 66 or null- convert it into binary flag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "prop2016.loc[prop2016.deck_flag==66 ,'deck_flag']=1\n",
    "prop2016.loc[prop2016.deck_flag.isnull() ,'deck_flag']=0\n",
    "print(prop2016.deck_flag.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  Imputing unit count based on property land type (Mode Imputation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2303148\n",
      "2320354\n",
      "1007727\n",
      "11437\n",
      "11437\n"
     ]
    }
   ],
   "source": [
    "#numberofstories & unitcnt &roomcnt\n",
    "\n",
    "prop2016.loc[prop2016.roomcnt==0 ,'roomcnt']=np.nan\n",
    "\n",
    "print(prop2016.numberofstories.isnull().sum())\n",
    "print(prop2016.roomcnt.isnull().sum())\n",
    "print(prop2016.unitcnt.isnull().sum())\n",
    "\n",
    "# propertylandusetypeid  and unitcnt is related \n",
    "#246 -Duplex (2 Units, Any Combination)\n",
    "#247 -Triplex (3 Units, Any Combination)\n",
    "#248 -Quadruplex (4 Units, Any Combination)\n",
    "#260 -Residential General\n",
    "#261 -Single Family Residential\n",
    "#263 -Mobile Home\n",
    "#264 -Townhouse\n",
    "#266 -Condominium\n",
    "#267 -Cooperative\n",
    "#269 -Planned Unit Development\n",
    "#275 -Residential Common Area \n",
    "#31 - Commercial/Office/Residential Mixed Used\n",
    "#47 -Store/Office (Mixed Use)\n",
    "#265 -Cluster Home\n",
    "\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==31) & (prop2016.unitcnt.isnull()),'unitcnt']=2\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==47) & (prop2016.unitcnt.isnull()),'unitcnt']=2\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==246) & (prop2016.unitcnt.isnull()),'unitcnt']=2\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==247) & (prop2016.unitcnt.isnull()),'unitcnt']=3\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==248) & (prop2016.unitcnt.isnull()),'unitcnt']=4\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==260) & (prop2016.unitcnt.isnull()),'unitcnt']=1\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==261) & (prop2016.unitcnt.isnull()),'unitcnt']=1\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==263) & (prop2016.unitcnt.isnull()),'unitcnt']=1\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==264) & (prop2016.unitcnt.isnull()),'unitcnt']=1\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==266) & (prop2016.unitcnt.isnull()),'unitcnt']=1\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==267) & (prop2016.unitcnt.isnull()),'unitcnt']=1\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==269) & (prop2016.unitcnt.isnull()),'unitcnt']=1\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==275) & (prop2016.unitcnt.isnull()),'unitcnt']=1\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==265) & (prop2016.unitcnt.isnull()),'unitcnt']=10\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==270) & (prop2016.unitcnt.isnull()),'unitcnt']=1\n",
    "\n",
    "\n",
    "#typeconstructiontypeid (based on location and year of building)\n",
    "\n",
    "print(prop2016.propertylandusetypeid.isnull().sum())\n",
    "print(prop2016.unitcnt.isnull().sum())\n",
    "#print(prop2016.propertylandusetypeid.value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop2016.loc[prop2016.propertylandusetypeid==270,'unitcnt'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  \"preimeter_living_area_sqft\" and \"total_finished_living_area_sqft\" have the same values except that \"preimeter_living_area_sqft\" has more duplicates, Let's drop it\n",
    "\n",
    "-  \"total_area_sqft\" and \"total_finished_living_area_sqft\" have the same values except that \"total_area_sqft\" has more duplicates, Let's drop it\n",
    "\n",
    "-  \"total_finished_living_area_sqft\" and \"finished_living_area_sqft\" have the same values except that \"finished_living_area_sqft\" has more duplicates, Let's drop it\n",
    "\n",
    "-  \"base_unfinished_and_finished_area_sqft\" and \"total_finished_living_area_sqft\" have the same values except that \"base_unfinished_and_finished_area_sqft\" has more duplicates, Let's drop it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "prop2016=prop2016.drop('preimeter_living_area_sqft', axis=1)\n",
    "prop2016=prop2016.drop('total_area_sqft', axis=1)\n",
    "prop2016=prop2016.drop('finished_living_area_sqft', axis=1)\n",
    "prop2016=prop2016.drop('base_unfinished_and_finished_area_sqft', axis=1)\n",
    "\n",
    "#calculatedfinishedsquarefeet\": \"total_finished_living_area_sqft\", \n",
    "#\"finishedsquarefeet12\": \"finished_living_area_sqft\",\n",
    "#\"lotsizesquarefeet\": \"lot_area_sqft\",\n",
    "#\"finishedsquarefeet50\": \"finished_living_area_entryfloor_sqft1\",\n",
    "#finishedfloor1squarefeet\": \"finished_living_area_entryfloor_sqft2\",\n",
    "#\"finishedsquarefeet6\": \"base_unfinished_and_finished_area_sqft\",\n",
    "#\"finishedsquarefeet15\": \"total_area_sqft\",\n",
    "#\"finishedsquarefeet13\": \"preimeter_living_area_sqft\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  From the analysis different counties follow different land use code, comparing different counties, zillow has created it's own \"propertylandusetypeid\" matching all the counties, to reduce number of levels, hence we can drop \"propertycountylandusecode\"\n",
    "\n",
    "-  Same applies to propertyzoningdesc as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#propertycountylandusecode\n",
    "#print(df_train.propertycountylandusecode.isnull().sum())\n",
    "\n",
    "prop2016=prop2016.drop(['propertycountylandusecode'], axis=1)\n",
    "\n",
    "#propertyzoningdesc, same reason as above applies to this column as well\n",
    "\n",
    "prop2016=prop2016.drop(['propertyzoningdesc'], axis=1)\n",
    "#print(df_train.propertyzoningdesc.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  Most zip id's either invalid or out of city, since enough information about location is given in latitude and longitude let's drop other location related fields\n",
    "\n",
    "    -  regionidcity\n",
    "    -  regionidzip\n",
    "    -  regionidneighborhood\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "prop2016=prop2016.drop(['regionidzip','regionidcity','regionidneighborhood'], axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  assessmentyear has a constant value for all the rows's, Let's drop it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "prop2016.assessmentyear.value_counts()\n",
    "prop2016=prop2016.drop(['assessmentyear'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  if Tax deliquency flag is null, it is safe to assume there is no unpaid tax for the property"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "prop2016.loc[prop2016.taxdelinquencyflag.isnull(),'taxdelinquencyflag'] = \"No\"\n",
    "prop2016.loc[prop2016.taxdelinquencyflag=='Y','taxdelinquencyflag'] = \"Yes\"\n",
    "prop2016.loc[prop2016.taxdelinquencyflag=='No','taxdelinquencyyear'] = 0\n",
    "#print(df_train.taxdelinquencyyear.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0     2928755\n",
      "15.0      26068\n",
      "14.0      16826\n",
      "13.0       4632\n",
      "12.0       3124\n",
      "11.0       2350\n",
      "10.0       1552\n",
      "9.0        1016\n",
      "8.0         501\n",
      "7.0         236\n",
      "6.0          71\n",
      "5.0          23\n",
      "4.0          16\n",
      "3.0           5\n",
      "95.0          5\n",
      "92.0          5\n",
      "99.0          4\n",
      "2.0           4\n",
      "93.0          3\n",
      "98.0          3\n",
      "1.0           2\n",
      "90.0          2\n",
      "91.0          2\n",
      "94.0          2\n",
      "97.0          2\n",
      "79.0          2\n",
      "82.0          1\n",
      "84.0          1\n",
      "86.0          1\n",
      "87.0          1\n",
      "88.0          1\n",
      "96.0          1\n",
      "Name: taxdelinquencyyear, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(prop2016.taxdelinquencyyear.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  Convert yy to yyyy format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==79,'taxdelinquencyyear'] = 1979\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==82,'taxdelinquencyyear'] = 1982\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==84,'taxdelinquencyyear'] = 1984\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==86,'taxdelinquencyyear'] = 1986\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==87,'taxdelinquencyyear'] = 1987\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==88,'taxdelinquencyyear'] = 1988\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==90,'taxdelinquencyyear'] = 1990\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==91,'taxdelinquencyyear'] = 1991\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==92,'taxdelinquencyyear'] = 1992\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==93,'taxdelinquencyyear'] = 1993\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==94,'taxdelinquencyyear'] = 1994\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==95,'taxdelinquencyyear'] = 1995\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==96,'taxdelinquencyyear'] = 1996\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==97,'taxdelinquencyyear'] = 1997\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==98,'taxdelinquencyyear'] = 1998\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==99,'taxdelinquencyyear'] = 1999\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==1,'taxdelinquencyyear'] = 2001\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==2,'taxdelinquencyyear'] = 2002\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==3,'taxdelinquencyyear'] = 2003\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==4,'taxdelinquencyyear'] = 2004\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==5,'taxdelinquencyyear'] = 2005\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==6,'taxdelinquencyyear'] = 2006\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==7,'taxdelinquencyyear'] = 2007\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==8,'taxdelinquencyyear'] = 2008\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==9,'taxdelinquencyyear'] = 2009\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==10,'taxdelinquencyyear'] = 2010\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==11,'taxdelinquencyyear'] = 2011\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==12,'taxdelinquencyyear'] = 2012\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==13,'taxdelinquencyyear'] = 2013\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==14,'taxdelinquencyyear'] = 2014\n",
    "prop2016.loc[prop2016.taxdelinquencyyear==15,'taxdelinquencyyear'] = 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "prop2016.loc[prop2016.taxdelinquencyyear>0,'taxdelinquencyyear'] = 2016 - prop2016['taxdelinquencyyear']\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  rawcensustractandblock has 3 fields concatened together, let's do string split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#rawcensustractandblock\n",
    "s_rawcensustractandblock=prop2016.rawcensustractandblock.apply(lambda x: str(x))\n",
    "\n",
    "prop2016['census_tractnumber']=s_rawcensustractandblock.str.slice(4,11)\n",
    "prop2016['block_number']=s_rawcensustractandblock.str.slice(start=11)\n",
    "prop2016['block_number']=prop2016['block_number'].apply(lambda x: x[:4]+'.'+x[4:]+'0' )\n",
    "prop2016['block_number']=prop2016['block_number'].apply(lambda x: int(round(float(x),0)) )\n",
    "prop2016['block_number']=prop2016['block_number'].apply(lambda x: str(x).ljust(4,'0') )\n",
    "\n",
    "#droping censustractandblock since this is just a duplicate of rawcensustractandblock\n",
    "prop2016=prop2016.drop('censustractandblock', axis=1)\n",
    "\n",
    "# drooping rawcensustractandblock, since it's already stored as substrings in different column names\n",
    "prop2016=prop2016.drop('rawcensustractandblock', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### At this point most of the missing values are logically filled. Even now if some fields are missing more than 95% of values, then it doesn't make sense to try and impute them, because it will only add noise to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['architecturalstyletypeid', 'typeconstructiontypeid', 'buildingclasstypeid']\n"
     ]
    }
   ],
   "source": [
    "missingvalues_prop = (prop2016.isnull().sum()/len(prop2016)).reset_index()\n",
    "missingvalues_prop.columns = ['field','percentage']\n",
    "missingvalues_prop = missingvalues_prop.sort_values(by = 'percentage', ascending = False)\n",
    "#print(missingvalues_prop)\n",
    "missingvaluescols = missingvalues_prop[missingvalues_prop['percentage'] > 0.95].field.tolist()\n",
    "\n",
    "print(missingvaluescols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#analysing the columns with most missing values\n",
    "#buildingclasstypeid', 'architecturalstyletypeid', 'typeconstructiontypeid\n",
    "#not many related columns or correlated columns to impute with, since these columns miss more than 95% of missing rows, imputation\n",
    "#will largely affect the accuracy of algorithms, hence droppping it\n",
    "\n",
    "prop2016 = prop2016.drop(['buildingclasstypeid', 'architecturalstyletypeid', 'typeconstructiontypeid'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#pool related attribute , is related propertylandusetypeid, and only single or duplex houses have pools \n",
    "\n",
    "#pool_sqft  16932\n",
    "poolsizesum_mean = prop2016.loc[prop2016['poolcnt'] > 0, 'pool_sqft'].mean()\n",
    "\n",
    "prop2016.loc[prop2016.pool_sqft.isnull(),'pool_sqft']=poolsizesum_mean\n",
    "print(prop2016.pool_sqft.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31250\n",
      "54982\n",
      "42550\n",
      "67733\n"
     ]
    }
   ],
   "source": [
    "#total_parcel_tax\n",
    "#structure_tax\n",
    "#land_tax\n",
    "#total_property_tax_2016\n",
    "#2)recalculate total_parcel_tax =structure_tax + land_tax\n",
    "\n",
    "# total_parcel_tax =structure_tax + land_tax\n",
    "#->structure_tax=total_parcel_tax -land_tax\n",
    "\n",
    "prop2016.loc[(prop2016.total_parcel_tax>0) & (prop2016.land_tax>0),'structure_tax']=prop2016['total_parcel_tax']-prop2016['land_tax']\n",
    "\n",
    "#structure_tax, i see a lot of structure tax is 0's, those must be NA's\n",
    "\n",
    "prop2016.loc[prop2016.structure_tax==0,'structure_tax']=np.nan\n",
    "\n",
    "print(prop2016.total_property_tax_2016.isnull().sum())\n",
    "print(prop2016.structure_tax.isnull().sum())\n",
    "print(prop2016.total_parcel_tax.isnull().sum())\n",
    "print(prop2016.land_tax.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132916\n"
     ]
    }
   ],
   "source": [
    "#*******************************\n",
    "#bedroomcnt #1421 zero bed room houses ??, observed it's missing all other room count also missing\n",
    "\n",
    "prop2016.loc[prop2016.bedroomcnt==0,'bedroomcnt']= np.nan\n",
    "print(prop2016.bedroomcnt.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128076\n",
      "128912\n",
      "128912\n",
      "132916\n",
      "132562\n"
     ]
    }
   ],
   "source": [
    "#caluculate full bath and half bath again from total bath as, it has few extra columns, (fixes 500 missing values in roomcnt )\n",
    "\n",
    "prop2016.loc[(prop2016.full_bath.notnull()) \n",
    "             & (prop2016.half_bath.notnull()) \n",
    "             & (prop2016.bedroomcnt.notnull()) \n",
    "             & (prop2016.roomcnt.isnull()),['roomcnt']]=prop2016.full_bath + prop2016.half_bath + prop2016.bedroomcnt\n",
    "\n",
    "\n",
    "# most bedroom count and roomcount null are in same place\n",
    "# all column null count 1133 all columns are null\n",
    "\n",
    "print(prop2016.total_bath.isnull().sum())\n",
    "print(prop2016.full_bath.isnull().sum())\n",
    "print(prop2016.half_bath.isnull().sum())\n",
    "print(prop2016.bedroomcnt.isnull().sum())\n",
    "print(prop2016.roomcnt.isnull().sum())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#numberofstories\t69705\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==246) & (prop2016.numberofstories.notnull()),'numberofstories'].mode()\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==246) & (prop2016.numberofstories.isnull()),'numberofstories']=2\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==247) & (prop2016.numberofstories.notnull()),'numberofstories'].mode()\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==247) & (prop2016.numberofstories.isnull()),'numberofstories']=2\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==248) & (prop2016.numberofstories.notnull()),'numberofstories'].mode()\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==248) & (prop2016.numberofstories.isnull()),'numberofstories']=2\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==260) & (prop2016.numberofstories.notnull()),'numberofstories'].mode()\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==260) & (prop2016.numberofstories.isnull()),'numberofstories']=2\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==261) & (prop2016.numberofstories.notnull()),'numberofstories'].mode()\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==261) & (prop2016.numberofstories.isnull()),'numberofstories']=1\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==263) & (prop2016.numberofstories.notnull()),'numberofstories'].mode()\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==263) & (prop2016.numberofstories.isnull()),'numberofstories']=1\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==266) & (prop2016.numberofstories.notnull()),'numberofstories'].mode()\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==266) & (prop2016.numberofstories.isnull()),'numberofstories']=1\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==269) & (prop2016.numberofstories.notnull()),'numberofstories'].mode()\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==269) & (prop2016.numberofstories.isnull()),'numberofstories']=2\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==275) & (prop2016.numberofstories.notnull()),'numberofstories'].mode()\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==275) & (prop2016.numberofstories.isnull()),'numberofstories']=1\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==267) & (prop2016.numberofstories.notnull()),'numberofstories'].mode()\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==267) & (prop2016.numberofstories.isnull()),'numberofstories']=1\n",
    "#https://en.wikipedia.org/wiki/Townhouse , typical town house are usualyy large, and has atleast 6 rooms\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==264) & (prop2016.numberofstories.isnull()),'numberofstories']=2\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==31) & (prop2016.numberofstories.notnull()),'numberofstories'].mode()\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==31) & (prop2016.numberofstories.isnull()),'numberofstories']=2\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==47) & (prop2016.numberofstories.notnull()),'numberofstories'].mode()\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==47) & (prop2016.numberofstories.isnull()),'numberofstories']=1\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==265) & (prop2016.numberofstories.notnull()),'numberofstories'].mode()\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==265) & (prop2016.numberofstories.isnull()),'numberofstories']=1\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==270) & (prop2016.numberofstories.isnull()),'numberofstories']=1\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fireplace_count\n",
    "prop2016.loc[(prop2016.fireplaceflag==1) & (prop2016.fireplace_count.notnull()),'fireplace_count'].mode()\n",
    "prop2016.loc[(prop2016.fireplaceflag==1) & (prop2016.fireplace_count.isnull()),'fireplace_count']=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#location seems to be related to building quality, (knnclassifier)\n",
    "\n",
    "def fillna_knn( df, base, target):\n",
    "    data_colnames = [ target ] + base\n",
    "    #print(\"data_colnames\",data_colnames)\n",
    "    missing_values_boolflag = df[target].isnull() #true for missing rows, false for columns with values\n",
    "    #print(\"miss\",missing_values_boolflag.head())\n",
    "    not_missing_boolflag = ~missing_values_boolflag \n",
    "    #print(\"not miss\",not_missing_boolflag.head())\n",
    "    number_of_missing_val = missing_values_boolflag.sum()\n",
    "    print(\"# of miss\",number_of_missing_val)\n",
    "    not_missing_rows = df.loc[ not_missing_boolflag, data_colnames ]\n",
    "    #print(not_missing_rows.head())\n",
    "    Y = not_missing_rows[target]\n",
    "    X = not_missing_rows[base]\n",
    "    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.20, random_state=3192,stratify=Y)\n",
    "    metrics       = ['euclidean'] \n",
    "    weights       = ['distance'] \n",
    "    numNeighbors  = [10,15,20]\n",
    "    param_grid    = dict(metric=metrics,weights=weights,n_neighbors=numNeighbors)\n",
    "    cv            = StratifiedKFold(n_splits=2,random_state=3192,shuffle=False)\n",
    "    grid = GridSearchCV(neighbors.KNeighborsClassifier(n_jobs=-1),param_grid=param_grid,cv=cv,scoring='f1_weighted',refit=True,return_train_score=True,verbose=1,n_jobs=-1,pre_dispatch='n_jobs')\n",
    "    grid.fit(X_train ,Y_train)\n",
    "    #print(\"grid.cv_results_\",grid.cv_results_)\n",
    "    print(\"grid.best_estimator_\",grid.best_estimator_)\n",
    "    print(\"grid.best_params_\",grid.best_params_)\n",
    "    print(\"grid.scorer_\",grid.scorer_)\n",
    "    #print(\"grid.n_splits_\",grid.n_splits_)\n",
    "    y_true, y_pred = Y_test, grid.predict(X_test)\n",
    "    \n",
    "    Z = grid.predict(df.loc[missing_values_boolflag, base])\n",
    "    #df.loc[ missing_values_boolflag, target ]  = Z\n",
    "    return Z\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1046729\n",
      "(2985217, 44)\n",
      "1035292\n",
      "(2973778, 44)\n",
      "# of miss 1035292\n",
      "Fitting 3 folds for each of 5 candidates, totalling 15 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  15 out of  15 | elapsed:  3.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid.best_estimator_ KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='euclidean',\n",
      "           metric_params=None, n_jobs=-1, n_neighbors=10, p=2,\n",
      "           weights='distance')\n",
      "grid.best_params_ {'metric': 'euclidean', 'n_neighbors': 10, 'weights': 'distance'}\n",
      "grid.scorer_ make_scorer(f1_score, pos_label=None, average=weighted)\n",
      "predicted output shape (1035292,)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(prop2016.buildingqualitytypeid.isnull().sum())\n",
    "print(prop2016.shape)\n",
    "temp=prop2016.copy()\n",
    "temp['buildingqualitytypeid']=temp['buildingqualitytypeid'].fillna(-1)\n",
    "temp=temp.groupby(\"buildingqualitytypeid\").filter(lambda x: x.buildingqualitytypeid.size > 3)\n",
    "temp['buildingqualitytypeid'] = temp['buildingqualitytypeid'].replace(-1,np.nan)\n",
    "temp=temp.loc[(temp.latitude.notnull()) & (temp.longitude.notnull()),temp.columns]\n",
    "print(temp.buildingqualitytypeid.isnull().sum())\n",
    "print(temp.shape)\n",
    "\n",
    "missing_values=fillna_knn(temp,\n",
    "                  base = [ 'latitude', 'longitude' ] ,\n",
    "                  target = 'buildingqualitytypeid')\n",
    "\n",
    "print(\"predicted output shape\",missing_values.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11437\n"
     ]
    }
   ],
   "source": [
    "\n",
    "missing_values_boolflag = prop2016.loc[(prop2016.latitude.notnull()) &(prop2016.longitude.notnull()),'buildingqualitytypeid'].isnull()\n",
    "\n",
    "#missing_values.shape\n",
    "prop2016.loc[ (prop2016.latitude.notnull()) &(prop2016.longitude.notnull()) & (missing_values_boolflag), 'buildingqualitytypeid' ]  = missing_values\n",
    "\n",
    "print(prop2016.buildingqualitytypeid.isnull().sum())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1178816\n",
      "(2985217, 44)\n",
      "1167379\n",
      "(2973776, 44)\n",
      "# of miss 1167379\n",
      "Fitting 3 folds for each of 5 candidates, totalling 15 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  15 out of  15 | elapsed:  2.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid.best_estimator_ KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='euclidean',\n",
      "           metric_params=None, n_jobs=-1, n_neighbors=15, p=2,\n",
      "           weights='distance')\n",
      "grid.best_params_ {'metric': 'euclidean', 'n_neighbors': 15, 'weights': 'distance'}\n",
      "grid.scorer_ make_scorer(f1_score, pos_label=None, average=weighted)\n",
      "predicted output shape (1167379,)\n",
      "11437\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(prop2016.heating_system_id.isnull().sum())\n",
    "print(prop2016.shape)\n",
    "temp=prop2016.copy()\n",
    "temp['heating_system_id']=temp['heating_system_id'].fillna(-1)\n",
    "temp=temp.groupby(\"heating_system_id\").filter(lambda x: x.heating_system_id.size > 3)\n",
    "temp['heating_system_id'] = temp['heating_system_id'].replace(-1,np.nan)\n",
    "temp=temp.loc[(temp.latitude.notnull()) & (temp.longitude.notnull()),temp.columns]\n",
    "print(temp.heating_system_id.isnull().sum())\n",
    "print(temp.shape)\n",
    "\n",
    "missing_values=fillna_knn(temp,\n",
    "                  base = [ 'latitude', 'longitude' ] ,\n",
    "                  target = 'heating_system_id')\n",
    "\n",
    "print(\"predicted output shape\",missing_values.shape)\n",
    "\n",
    "missing_values_boolflag = prop2016.loc[(prop2016.latitude.notnull()) &(prop2016.longitude.notnull()),'heating_system_id'].isnull()\n",
    "\n",
    "#missing_values.shape\n",
    "prop2016.loc[ (prop2016.latitude.notnull()) &(prop2016.longitude.notnull()) & (missing_values_boolflag), 'heating_system_id' ]  = missing_values\n",
    "\n",
    "print(prop2016.heating_system_id.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2173698\n",
      "(2985217, 44)\n",
      "2162261\n",
      "(2973780, 44)\n",
      "# of miss 2162261\n",
      "Fitting 3 folds for each of 5 candidates, totalling 15 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  15 out of  15 | elapsed:  1.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid.best_estimator_ KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='euclidean',\n",
      "           metric_params=None, n_jobs=-1, n_neighbors=15, p=2,\n",
      "           weights='distance')\n",
      "grid.best_params_ {'metric': 'euclidean', 'n_neighbors': 15, 'weights': 'distance'}\n",
      "grid.scorer_ make_scorer(f1_score, pos_label=None, average=weighted)\n",
      "predicted output shape (2162261,)\n",
      "11437\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(prop2016.ac_id.isnull().sum())\n",
    "print(prop2016.shape)\n",
    "temp=prop2016.copy()\n",
    "temp['ac_id']=temp['ac_id'].fillna(-1)\n",
    "temp=temp.groupby(\"ac_id\").filter(lambda x: x.ac_id.size > 3)\n",
    "temp['ac_id'] = temp['ac_id'].replace(-1,np.nan)\n",
    "temp=temp.loc[(temp.latitude.notnull()) & (temp.longitude.notnull()),temp.columns]\n",
    "print(temp.ac_id.isnull().sum())\n",
    "print(temp.shape)\n",
    "\n",
    "missing_values=fillna_knn(temp,\n",
    "                  base = [ 'latitude', 'longitude' ] ,\n",
    "                  target = 'ac_id')\n",
    "\n",
    "print(\"predicted output shape\",missing_values.shape)\n",
    "\n",
    "missing_values_boolflag = prop2016.loc[(prop2016.latitude.notnull()) &(prop2016.longitude.notnull()),'ac_id'].isnull()\n",
    "\n",
    "#missing_values.shape\n",
    "prop2016.loc[ (prop2016.latitude.notnull()) &(prop2016.longitude.notnull()) & (missing_values_boolflag), 'ac_id' ]  = missing_values\n",
    "\n",
    "print(prop2016.ac_id.isnull().sum())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#location seems to be related to building quality, (knnregressor)\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "def fillna_knnr( df, base, target):\n",
    "    data_colnames = [ target ] + base\n",
    "    #print(\"data_colnames\",data_colnames)\n",
    "    missing_values_boolflag = df[target].isnull() #true for missing rows, false for columns with values\n",
    "    #print(\"miss\",missing_values_boolflag.head())\n",
    "    not_missing_boolflag = ~missing_values_boolflag \n",
    "    #print(\"not miss\",not_missing_boolflag.head())\n",
    "    number_of_missing_val = missing_values_boolflag.sum()\n",
    "    print(\"# of miss\",number_of_missing_val)\n",
    "    not_missing_rows = df.loc[ not_missing_boolflag, data_colnames]\n",
    "    #print(not_missing_rows.head())\n",
    "    Y = not_missing_rows[target]\n",
    "    X = not_missing_rows[base]\n",
    "    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.20, random_state=3192)\n",
    "    metrics       = ['euclidean'] \n",
    "    weights       = ['distance'] \n",
    "    numNeighbors  = [15,25]\n",
    "    param_grid    = dict(metric=metrics,weights=weights,n_neighbors=numNeighbors)\n",
    "    cv            = KFold(n_splits=2,random_state=3192,shuffle=False) \n",
    "    grid = GridSearchCV(neighbors.KNeighborsRegressor(n_jobs=-1),param_grid=param_grid,cv=cv,scoring='neg_mean_absolute_error',refit=True,return_train_score=True,verbose=1,n_jobs=-1,pre_dispatch='n_jobs')\n",
    "    grid.fit(X_train ,Y_train)\n",
    "    #print(\"grid.cv_results_\",grid.cv_results_)\n",
    "    print(\"grid.best_estimator_\",grid.best_estimator_)\n",
    "    print(\"grid.best_params_\",grid.best_params_)\n",
    "    print(\"grid.scorer_\",grid.scorer_)\n",
    "    #print(\"grid.n_splits_\",grid.n_splits_)\n",
    "    y_true, y_pred = Y_test, grid.predict(X_test) \n",
    "    Z = grid.predict(df.loc[missing_values_boolflag, base])\n",
    "    #df.loc[ missing_values_boolflag, target ]  = Z\n",
    "    return Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "182804\n",
      "(2985217, 44)\n",
      "182804\n",
      "(869262, 44)\n",
      "# of miss 182804\n",
      "Fitting 2 folds for each of 2 candidates, totalling 4 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:  3.6min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid.best_estimator_ KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='euclidean',\n",
      "          metric_params=None, n_jobs=-1, n_neighbors=15, p=2,\n",
      "          weights='distance')\n",
      "grid.best_params_ {'metric': 'euclidean', 'n_neighbors': 15, 'weights': 'distance'}\n",
      "grid.scorer_ make_scorer(mean_absolute_error, greater_is_better=False)\n",
      "predicted output shape (182804,)\n",
      "59928\n"
     ]
    }
   ],
   "source": [
    "#garage_sqft\n",
    "print(prop2016.garage_sqft.isnull().sum())\n",
    "print(prop2016.shape)\n",
    "\n",
    "\n",
    "temp=prop2016.loc[prop2016.garagecarcnt>0,prop2016.columns].copy()\n",
    "temp=temp.loc[(temp.latitude.notnull()) & (temp.longitude.notnull()),temp.columns]\n",
    "\n",
    "print(temp.garage_sqft.isnull().sum())\n",
    "\n",
    "print(temp.shape)\n",
    "\n",
    "missing_values=fillna_knnr(temp,\n",
    "                  base = [ 'latitude', 'longitude','garagecarcnt'] ,\n",
    "                  target = 'garage_sqft')\n",
    "\n",
    "print(\"predicted output shape\",missing_values.shape)\n",
    "\n",
    "\n",
    "missing_values_boolflag = prop2016.loc[(prop2016.latitude.notnull()) &(prop2016.longitude.notnull()) &(prop2016.garagecarcnt>0),'garage_sqft'].isnull()\n",
    "\n",
    "#missing_values.shape\n",
    "prop2016.loc[ (prop2016.latitude.notnull()) &(prop2016.longitude.notnull()) & (prop2016.garagecarcnt>0) & (missing_values_boolflag), 'garage_sqft' ]  = missing_values\n",
    "\n",
    "print(prop2016.yearbuilt.isnull().sum())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11437\n"
     ]
    }
   ],
   "source": [
    "#83 mins\n",
    "#total_finished_living_area_sqft\n",
    "\n",
    "temp=prop2016.copy()\n",
    "temp=temp.loc[(temp.latitude.notnull()) & (temp.longitude.notnull()),temp.columns]\n",
    "print(\"null values\",temp.total_finished_living_area_sqft.isnull().sum())\n",
    "\n",
    "missing_values=fillna_knnr(temp,\n",
    "                  base = [ 'latitude', 'longitude','propertylandusetypeid'] ,\n",
    "                  target = 'total_finished_living_area_sqft')\n",
    "\n",
    "print(\"predicted output shape\",missing_values.shape)\n",
    "\n",
    "\n",
    "missing_values_boolflag = prop2016.loc[(prop2016.latitude.notnull()) &(prop2016.longitude.notnull()) ,'total_finished_living_area_sqft'].isnull()\n",
    "#missing_values.shape\n",
    "prop2016.loc[ (prop2016.latitude.notnull()) &(prop2016.longitude.notnull()) & (missing_values_boolflag), 'total_finished_living_area_sqft' ]  = missing_values\n",
    "print(prop2016.total_finished_living_area_sqft.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48491\n",
      "(2973738, 44)\n",
      "# of miss 48491\n",
      "Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed: 130.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid.best_estimator_ KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='euclidean',\n",
      "           metric_params=None, n_jobs=-1, n_neighbors=10, p=2,\n",
      "           weights='distance')\n",
      "grid.best_params_ {'metric': 'euclidean', 'n_neighbors': 10, 'weights': 'distance'}\n",
      "grid.scorer_ make_scorer(f1_score, pos_label=None, average=weighted)\n",
      "predicted output shape (48491,)\n",
      "11437\n"
     ]
    }
   ],
   "source": [
    "#yearbuilt\n",
    "\n",
    "temp=prop2016.copy()\n",
    "temp=temp.loc[(temp.latitude.notnull()) & (temp.longitude.notnull()),temp.columns]\n",
    "temp=prop2016.copy()\n",
    "temp['yearbuilt']=temp['yearbuilt'].fillna(-1)\n",
    "temp=temp.groupby(\"yearbuilt\").filter(lambda x: x.yearbuilt.size > 3)\n",
    "temp['yearbuilt'] = temp['yearbuilt'].replace(-1,np.nan)\n",
    "temp=temp.loc[(temp.latitude.notnull()) & (temp.longitude.notnull()),temp.columns]\n",
    "print(temp.yearbuilt.isnull().sum())\n",
    "print(temp.shape)\n",
    "\n",
    "missing_values=fillna_knn(temp,\n",
    "                  base = [ 'latitude', 'longitude','propertylandusetypeid' ] ,\n",
    "                  target = 'yearbuilt')\n",
    "\n",
    "print(\"predicted output shape\",missing_values.shape)\n",
    "missing_values_boolflag = prop2016.loc[(prop2016.latitude.notnull()) &(prop2016.longitude.notnull()),'yearbuilt'].isnull()\n",
    "\n",
    "#missing_values.shape\n",
    "prop2016.loc[ (prop2016.latitude.notnull()) &(prop2016.longitude.notnull()) & (missing_values_boolflag), 'yearbuilt' ]  = missing_values\n",
    "\n",
    "print(prop2016.yearbuilt.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128076\n",
      "116639\n",
      "(2973780, 44)\n",
      "# of miss 116639\n",
      "Fitting 2 folds for each of 2 candidates, totalling 4 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed: 18.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid.best_estimator_ KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='euclidean',\n",
      "          metric_params=None, n_jobs=-1, n_neighbors=25, p=2,\n",
      "          weights='distance')\n",
      "grid.best_params_ {'metric': 'euclidean', 'n_neighbors': 25, 'weights': 'distance'}\n",
      "grid.scorer_ make_scorer(mean_absolute_error, greater_is_better=False)\n",
      "predicted output shape (116639,)\n",
      "11437\n"
     ]
    }
   ],
   "source": [
    "#total_finished_living_area_sqft\n",
    "print(prop2016.total_bath.isnull().sum())\n",
    "temp=prop2016.copy()\n",
    "temp=temp.loc[(temp.latitude.notnull()) & (temp.longitude.notnull()),temp.columns]\n",
    "print(temp.total_bath.isnull().sum())\n",
    "print(temp.shape)\n",
    "\n",
    "missing_values=fillna_knnr(temp,\n",
    "                  base = ['propertylandusetypeid','total_finished_living_area_sqft' ] ,\n",
    "                  target = 'total_bath')\n",
    "\n",
    "print(\"predicted output shape\",missing_values.shape)\n",
    "\n",
    "missing_values_boolflag = prop2016.loc[(prop2016.latitude.notnull()) &(prop2016.longitude.notnull()),'total_bath'].isnull()\n",
    "\n",
    "#missing_values.shape\n",
    "prop2016.loc[ (prop2016.latitude.notnull()) &(prop2016.longitude.notnull()) & (missing_values_boolflag), 'total_bath' ]  = missing_values\n",
    "\n",
    "print(prop2016.total_bath.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132916\n",
      "121479\n",
      "(2973780, 44)\n",
      "# of miss 121479\n",
      "Fitting 2 folds for each of 2 candidates, totalling 4 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed: 21.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid.best_estimator_ KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='euclidean',\n",
      "          metric_params=None, n_jobs=-1, n_neighbors=25, p=2,\n",
      "          weights='distance')\n",
      "grid.best_params_ {'metric': 'euclidean', 'n_neighbors': 25, 'weights': 'distance'}\n",
      "grid.scorer_ make_scorer(mean_absolute_error, greater_is_better=False)\n",
      "predicted output shape (121479,)\n",
      "11437\n"
     ]
    }
   ],
   "source": [
    "#bedroomcnt\t1421\n",
    "\n",
    "print(prop2016.bedroomcnt.isnull().sum())\n",
    "\n",
    "temp=prop2016.copy()\n",
    "temp=temp.loc[(temp.latitude.notnull()) & (temp.longitude.notnull()),temp.columns]\n",
    "\n",
    "print(temp.bedroomcnt.isnull().sum())\n",
    "print(temp.shape)\n",
    "missing_values=fillna_knnr(temp,\n",
    "                  base = ['propertylandusetypeid','total_finished_living_area_sqft','total_bath' ] ,\n",
    "                  target = 'bedroomcnt')\n",
    "\n",
    "print(\"predicted output shape\",missing_values.shape)\n",
    "\n",
    "missing_values_boolflag = prop2016.loc[(prop2016.latitude.notnull()) &(prop2016.longitude.notnull()),'bedroomcnt'].isnull()\n",
    "\n",
    "#missing_values.shape\n",
    "prop2016.loc[ (prop2016.latitude.notnull()) &(prop2016.longitude.notnull()) & (missing_values_boolflag), 'bedroomcnt' ]  = missing_values\n",
    "\n",
    "print(prop2016.bedroomcnt.isnull().sum())\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "prop2016['total_bath']=prop2016.total_bath.round(1)\n",
    "prop2016['bedroomcnt']=prop2016.bedroomcnt.round(1)\n",
    "#recalculate roomcnt\t1416 as we have used imputation for total_bath and bedroomcnt\n",
    "\n",
    "prop2016.loc[(prop2016.latitude.notnull()) &(prop2016.longitude.notnull()) & (prop2016.roomcnt.isnull()),['roomcnt']]=prop2016.total_bath + prop2016.bedroomcnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "276099\n",
      "0\n",
      "(2973780, 44)\n",
      "# of miss 264662\n",
      "Fitting 2 folds for each of 2 candidates, totalling 4 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:  2.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid.best_estimator_ KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='euclidean',\n",
      "          metric_params=None, n_jobs=-1, n_neighbors=15, p=2,\n",
      "          weights='distance')\n",
      "grid.best_params_ {'metric': 'euclidean', 'n_neighbors': 15, 'weights': 'distance'}\n",
      "grid.scorer_ make_scorer(mean_absolute_error, greater_is_better=False)\n",
      "predicted output shape (264662,)\n",
      "11437\n"
     ]
    }
   ],
   "source": [
    "#lot_area_sqft\n",
    "print(prop2016.lot_area_sqft.isnull().sum())\n",
    "\n",
    "temp=prop2016.copy()\n",
    "temp=temp.loc[(temp.latitude.notnull()) & (temp.longitude.notnull()),temp.columns]\n",
    "\n",
    "print(temp.bedroomcnt.isnull().sum())\n",
    "print(temp.shape)\n",
    "\n",
    "\n",
    "missing_values=fillna_knnr(temp,\n",
    "                  base = ['latitude','longitude','total_finished_living_area_sqft'],\n",
    "                  target = 'lot_area_sqft')\n",
    "\n",
    "print(\"predicted output shape\",missing_values.shape)\n",
    "\n",
    "missing_values_boolflag = prop2016.loc[(prop2016.latitude.notnull()) &(prop2016.longitude.notnull()),'lot_area_sqft'].isnull()\n",
    "\n",
    "#missing_values.shape\n",
    "prop2016.loc[ (prop2016.latitude.notnull()) &(prop2016.longitude.notnull()) & (missing_values_boolflag), 'lot_area_sqft' ]  = missing_values\n",
    "\n",
    "print(prop2016.lot_area_sqft.isnull().sum())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#prop2016.loc[(prop2016.latitude.notnull()) & (prop2016.longitude.notnull()),'total_finished_living_area_sqft'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54982\n",
      "43545\n",
      "(2973780, 44)\n",
      "# of miss 43545\n",
      "Fitting 2 folds for each of 2 candidates, totalling 4 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:  2.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid.best_estimator_ KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='euclidean',\n",
      "          metric_params=None, n_jobs=-1, n_neighbors=25, p=2,\n",
      "          weights='distance')\n",
      "grid.best_params_ {'metric': 'euclidean', 'n_neighbors': 25, 'weights': 'distance'}\n",
      "grid.scorer_ make_scorer(mean_absolute_error, greater_is_better=False)\n",
      "predicted output shape (43545,)\n",
      "11437\n"
     ]
    }
   ],
   "source": [
    "# predict structure_tax and recalculate  total_parcel_tax = land_tax + structure_tax\n",
    "\n",
    "print(prop2016.structure_tax.isnull().sum())\n",
    "\n",
    "temp=prop2016.copy()\n",
    "temp=temp.loc[(temp.latitude.notnull()) & (temp.longitude.notnull()),temp.columns]\n",
    "\n",
    "print(temp.structure_tax.isnull().sum())\n",
    "print(temp.shape)\n",
    "\n",
    "\n",
    "missing_values=fillna_knnr(temp,\n",
    "                  base = ['latitude','longitude','lot_area_sqft','total_finished_living_area_sqft' ] ,\n",
    "                  target = 'structure_tax')\n",
    "\n",
    "print(\"predicted output shape\",missing_values.shape)\n",
    "\n",
    "missing_values_boolflag = prop2016.loc[(prop2016.latitude.notnull()) &(prop2016.longitude.notnull()),'structure_tax'].isnull()\n",
    "\n",
    "#missing_values.shape\n",
    "prop2016.loc[ (prop2016.latitude.notnull()) &(prop2016.longitude.notnull()) & (missing_values_boolflag), 'structure_tax' ]  = missing_values\n",
    "\n",
    "print(prop2016.structure_tax.isnull().sum())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#36 total_property_tax_2016 \n",
    "\n",
    "#total_parcel_tax = land_tax + structure_tax\n",
    "    \n",
    "prop2016['total_parcel_tax']=prop2016['structure_tax']+prop2016['land_tax']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#age of the property\n",
    "prop2016['age'] = 2016 - prop2016['yearbuilt']\n",
    "prop2016=prop2016.drop(['yearbuilt'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#prop2016.loc[(prop2016.latitude.notnull()) & (prop2016.longitude.notnull()),'lot_area_sqft'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31250\n",
      "19813\n",
      "(2973780, 44)\n",
      "# of miss 19813\n",
      "Fitting 2 folds for each of 2 candidates, totalling 4 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:  4.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid.best_estimator_ KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='euclidean',\n",
      "          metric_params=None, n_jobs=-1, n_neighbors=25, p=2,\n",
      "          weights='distance')\n",
      "grid.best_params_ {'metric': 'euclidean', 'n_neighbors': 25, 'weights': 'distance'}\n",
      "grid.scorer_ make_scorer(mean_absolute_error, greater_is_better=False)\n",
      "predicted output shape (19813,)\n",
      "11437\n"
     ]
    }
   ],
   "source": [
    "#total_property_tax_2016\n",
    "\n",
    "\n",
    "print(prop2016.total_property_tax_2016.isnull().sum())\n",
    "\n",
    "temp=prop2016.copy()\n",
    "temp=temp.loc[(temp.latitude.notnull()) & (temp.longitude.notnull()),temp.columns]\n",
    "\n",
    "print(temp.total_property_tax_2016.isnull().sum())\n",
    "print(temp.shape)\n",
    "\n",
    "\n",
    "\n",
    "missing_values=fillna_knnr(temp,\n",
    "                  base = ['latitude','longitude','lot_area_sqft','propertylandusetypeid','total_finished_living_area_sqft' ] ,\n",
    "                  target = 'total_property_tax_2016')\n",
    "\n",
    "print(\"predicted output shape\",missing_values.shape)\n",
    "\n",
    "missing_values_boolflag = prop2016.loc[(prop2016.latitude.notnull()) &(prop2016.longitude.notnull()),'total_property_tax_2016'].isnull()\n",
    "\n",
    "#missing_values.shape\n",
    "prop2016.loc[ (prop2016.latitude.notnull()) &(prop2016.longitude.notnull()) & (missing_values_boolflag), 'total_property_tax_2016' ]  = missing_values\n",
    "\n",
    "print(prop2016.total_property_tax_2016.isnull().sum())\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sqlalchemy\\dialects\\mysql\\base.py:1569: Warning: (1287, \"'@@tx_isolation' is deprecated and will be removed in a future release. Please use '@@transaction_isolation' instead\")\n",
      "  cursor.execute('SELECT @@tx_isolation')\n"
     ]
    }
   ],
   "source": [
    "import MySQLdb\n",
    "from sqlalchemy import create_engine\n",
    "engineString = 'mysql+mysqldb://root:MyNewPass@localhost/sakila'\n",
    "engine = create_engine(engineString)\n",
    "con=engine.connect()\n",
    "\n",
    "with engine.connect() as con, con.begin():\n",
    "    prop2016.to_sql('prop2016_f2', engine, chunksize=10000, index =False,if_exists ='replace')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sqlalchemy\\dialects\\mysql\\base.py:1569: Warning: (1287, \"'@@tx_isolation' is deprecated and will be removed in a future release. Please use '@@transaction_isolation' instead\")\n",
      "  cursor.execute('SELECT @@tx_isolation')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_train = pd.DataFrame()\n",
    "import MySQLdb\n",
    "from sqlalchemy import create_engine\n",
    "engineString = 'mysql+mysqldb://root:MyNewPass@localhost/sakila'\n",
    "engine = create_engine(engineString)\n",
    "#con=engine.connect()\n",
    "prop2016=pd.read_sql_table('prop2016_f2',con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              column_name  missing_count\n",
      "1                                   ac_id          11437\n",
      "35                          structure_tax          11437\n",
      "33                        numberofstories          11437\n",
      "30                                unitcnt          11437\n",
      "27                                roomcnt          11437\n",
      "26                         regionidcounty          11437\n",
      "25                  propertylandusetypeid          11437\n",
      "19                          lot_area_sqft          11437\n",
      "38                total_property_tax_2016          11437\n",
      "17                               latitude          11437\n",
      "18                              longitude          11437\n",
      "10                                   fips          11437\n",
      "8         total_finished_living_area_sqft          11437\n",
      "5                   buildingqualitytypeid          11437\n",
      "4                              bedroomcnt          11437\n",
      "3                              total_bath          11437\n",
      "16                      heating_system_id          11437\n",
      "43                                    age          11437\n",
      "36                       total_parcel_tax          67733\n",
      "37                               land_tax          67733\n",
      "12                              full_bath         128912\n",
      "29                              half_bath         128912\n",
      "9   finished_living_area_entryfloor_sqft1        2782500\n",
      "7   finished_living_area_entryfloor_sqft2        2782500\n",
      "(24, 2)\n"
     ]
    }
   ],
   "source": [
    "# missing count\n",
    "missing_df = prop2016.isnull().sum(axis=0).reset_index()\n",
    "missing_df.columns = ['column_name', 'missing_count']\n",
    "missing_df = missing_df.loc[missing_df['missing_count']>0]\n",
    "missing_df = missing_df.sort_values(by='missing_count')\n",
    "print(missing_df)\n",
    "print(missing_df.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "##import train 2016  data\n",
    "train2016=pd.read_csv('C:\\\\Users\\\\Dell\\\\Desktop\\\\inf552\\\\Zillow Prize\\\\train_2016.csv',parse_dates=[\"transactiondate\"])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = train2016.merge(prop2016, how='left', on='parcelid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_train = df_train.drop(['full_bath','half_bath','finished_living_area_entryfloor_sqft2','finished_living_area_entryfloor_sqft1'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train.loc[df_train.total_parcel_tax.notnull(),df_train.columns]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parcelid</th>\n",
       "      <th>logerror</th>\n",
       "      <th>transactiondate</th>\n",
       "      <th>ac_id</th>\n",
       "      <th>basementsqft</th>\n",
       "      <th>total_bath</th>\n",
       "      <th>bedroomcnt</th>\n",
       "      <th>buildingqualitytypeid</th>\n",
       "      <th>deck_flag</th>\n",
       "      <th>total_finished_living_area_sqft</th>\n",
       "      <th>...</th>\n",
       "      <th>fireplaceflag</th>\n",
       "      <th>structure_tax</th>\n",
       "      <th>total_parcel_tax</th>\n",
       "      <th>land_tax</th>\n",
       "      <th>total_property_tax_2016</th>\n",
       "      <th>taxdelinquencyflag</th>\n",
       "      <th>taxdelinquencyyear</th>\n",
       "      <th>census_tractnumber</th>\n",
       "      <th>block_number</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11016594</td>\n",
       "      <td>0.0276</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1684.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>122754.0</td>\n",
       "      <td>360170.0</td>\n",
       "      <td>237416.0</td>\n",
       "      <td>6735.88</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1066.46</td>\n",
       "      <td>1001</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14366692</td>\n",
       "      <td>-0.1684</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2263.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>346458.0</td>\n",
       "      <td>585529.0</td>\n",
       "      <td>239071.0</td>\n",
       "      <td>10153.02</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0524.22</td>\n",
       "      <td>2024</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12098116</td>\n",
       "      <td>-0.0040</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2217.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>61994.0</td>\n",
       "      <td>119906.0</td>\n",
       "      <td>57912.0</td>\n",
       "      <td>11484.48</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4638.00</td>\n",
       "      <td>3004</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12643413</td>\n",
       "      <td>0.0218</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>839.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>171518.0</td>\n",
       "      <td>244880.0</td>\n",
       "      <td>73362.0</td>\n",
       "      <td>3048.74</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2963.00</td>\n",
       "      <td>2002</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14432541</td>\n",
       "      <td>-0.0050</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2283.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>169574.0</td>\n",
       "      <td>434551.0</td>\n",
       "      <td>264977.0</td>\n",
       "      <td>5488.96</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0423.38</td>\n",
       "      <td>1006</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   parcelid  logerror transactiondate  ac_id  basementsqft  total_bath  \\\n",
       "0  11016594    0.0276      2016-01-01    1.0           0.0         2.0   \n",
       "1  14366692   -0.1684      2016-01-01   13.0           0.0         3.5   \n",
       "2  12098116   -0.0040      2016-01-01    1.0           0.0         3.0   \n",
       "3  12643413    0.0218      2016-01-02    1.0           0.0         2.0   \n",
       "4  14432541   -0.0050      2016-01-02    5.0           0.0         2.5   \n",
       "\n",
       "   bedroomcnt  buildingqualitytypeid  deck_flag  \\\n",
       "0         3.0                    4.0        0.0   \n",
       "1         4.0                    4.0        0.0   \n",
       "2         2.0                    4.0        0.0   \n",
       "3         2.0                    4.0        0.0   \n",
       "4         4.0                    4.0        0.0   \n",
       "\n",
       "   total_finished_living_area_sqft  ...  fireplaceflag  structure_tax  \\\n",
       "0                           1684.0  ...              0       122754.0   \n",
       "1                           2263.0  ...              0       346458.0   \n",
       "2                           2217.0  ...              0        61994.0   \n",
       "3                            839.0  ...              0       171518.0   \n",
       "4                           2283.0  ...              0       169574.0   \n",
       "\n",
       "   total_parcel_tax  land_tax total_property_tax_2016  taxdelinquencyflag  \\\n",
       "0          360170.0  237416.0                 6735.88                  No   \n",
       "1          585529.0  239071.0                10153.02                  No   \n",
       "2          119906.0   57912.0                11484.48                  No   \n",
       "3          244880.0   73362.0                 3048.74                  No   \n",
       "4          434551.0  264977.0                 5488.96                  No   \n",
       "\n",
       "   taxdelinquencyyear  census_tractnumber  block_number   age  \n",
       "0                 0.0             1066.46          1001  57.0  \n",
       "1                 0.0             0524.22          2024   2.0  \n",
       "2                 0.0             4638.00          3004  76.0  \n",
       "3                 0.0             2963.00          2002  29.0  \n",
       "4                 0.0             0423.38          1006  35.0  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [column_name, missing_count]\n",
      "Index: []\n",
      "(0, 2)\n"
     ]
    }
   ],
   "source": [
    "# missing count\n",
    "missing_df = df_train.isnull().sum(axis=0).reset_index()\n",
    "missing_df.columns = ['column_name', 'missing_count']\n",
    "missing_df = missing_df.loc[missing_df['missing_count']>0]\n",
    "missing_df = missing_df.sort_values(by='missing_count')\n",
    "print(missing_df)\n",
    "print(missing_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['parcelid', 'logerror', 'transactiondate', 'ac_id', 'basementsqft',\n",
       "       'total_bath', 'bedroomcnt', 'buildingqualitytypeid', 'deck_flag',\n",
       "       'total_finished_living_area_sqft', 'fips', 'fireplace_count',\n",
       "       'garagecarcnt', 'garage_sqft', 'has_hottub_or_spa', 'heating_system_id',\n",
       "       'latitude', 'longitude', 'lot_area_sqft', 'poolcnt', 'pool_sqft',\n",
       "       'just_hottub_or_spa', 'pool_with_spa_tub_yes', 'pool_with_spa_tub_no',\n",
       "       'propertylandusetypeid', 'regionidcounty', 'roomcnt', 'basement_flag',\n",
       "       'unitcnt', 'patio_sqft', 'storage_sqft', 'numberofstories',\n",
       "       'fireplaceflag', 'structure_tax', 'total_parcel_tax', 'land_tax',\n",
       "       'total_property_tax_2016', 'taxdelinquencyflag', 'taxdelinquencyyear',\n",
       "       'census_tractnumber', 'block_number', 'age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#proportion of living area\n",
    "df_train['N-LivingAreaProp'] = df_train['total_finished_living_area_sqft']/df_train['lot_area_sqft']\n",
    "\n",
    "\n",
    "#Amout of extra space\n",
    "df_train['N-ExtraSpace'] = df_train['lot_area_sqft'] - df_train['total_finished_living_area_sqft'] \n",
    "#df_train['N-ExtraSpace-2'] = df_train['total_area_sqft'] - df_train['finished_living_area_sqft'] \n",
    "\n",
    "#Total number of rooms\n",
    "df_train['N-TotalRooms'] = df_train['total_bath']*df_train['bedroomcnt']\n",
    "\n",
    "# Number of Extra rooms\n",
    "df_train['N-ExtraRooms'] = df_train['roomcnt'] - df_train['N-TotalRooms'] \n",
    "\n",
    "#Average room size N-AvRoomSizeze\n",
    "df_train['N-AvRoomSize'] = df_train['total_finished_living_area_sqft']/df_train['roomcnt']\n",
    "\n",
    "\n",
    "#Ratio of the built structure value to land area\n",
    "df_train['N-ValueProp'] = df_train['structure_tax']/df_train['land_tax']\n",
    "\n",
    "#Ratio of tax of property over parcel\n",
    "df_train['N-ValueRatio'] = df_train['total_parcel_tax']/df_train['total_property_tax_2016']\n",
    "\n",
    "#proportion of living area\n",
    "df_train['N-LivingAreaProp'] = df_train['total_finished_living_area_sqft']/df_train['lot_area_sqft']\n",
    "\n",
    "#Amout of extra space\n",
    "df_train['N-ExtraSpace'] = df_train['lot_area_sqft'] - df_train['total_finished_living_area_sqft'] \n",
    "\n",
    "#TotalTaxScore\n",
    "df_train['N-TaxScore'] = df_train['total_parcel_tax']*df_train['total_property_tax_2016']\n",
    "\n",
    "#polnomials of the variable\n",
    "df_train[\"N-structure_tax-2\"] = df_train[\"structure_tax\"] ** 2\n",
    "df_train[\"N-structure_tax-3\"] = df_train[\"structure_tax\"] ** 3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Indicator whether it has AC or not\n",
    "df_train['N-ACInd'] = (df_train['ac_id']!=5)*1\n",
    "\n",
    "#Indicator whether it has Heating or not \n",
    "df_train['N-HeatInd'] = (df_train['heating_system_id']!=13)*1\n",
    "\n",
    "#There's 25 different property uses - let's compress them down to 4 categories\n",
    "#df_train['N-PropType'] = df_train.propertylandusetypeid.replace({31 : \"Mixed\", 46 : \"Other\", 47 : \"Mixed\", 246 : \"Mixed\", 247 : \"Mixed\", 248 : \"Mixed\", 260 : \"Home\", 261 : \"Home\", 262 : \"Home\", 263 : \"Home\", 264 : \"Home\", 265 : \"Home\", 266 : \"Home\", 267 : \"Home\", 268 : \"Home\", 269 : \"Not Built\", 270 : \"Home\", 271 : \"Home\", 273 : \"Home\", 274 : \"Other\", 275 : \"Home\", 276 : \"Home\", 279 : \"Home\", 290 : \"Not Built\", 291 : \"Not Built\" })\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_train=df_train.drop(['ac_id','heating_system_id','propertylandusetypeid'], axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.loc[df_train.has_hottub_or_spa==1,'has_hottub_or_spa']=\"Yes\"\n",
    "df_train.loc[df_train.has_hottub_or_spa==0,'has_hottub_or_spa']=\"No\"\n",
    "\n",
    "df_train.loc[df_train.just_hottub_or_spa==0,'just_hottub_or_spa']=\"No\"\n",
    "df_train.loc[df_train.just_hottub_or_spa==1,'just_hottub_or_spa']=\"Yes\"\n",
    "\n",
    "df_train.loc[df_train.deck_flag==0,'deck_flag']=\"No\"\n",
    "df_train.loc[df_train.deck_flag==1,'deck_flag']=\"Yes\"\n",
    "\n",
    "df_train.loc[df_train.basement_flag==0,'basement_flag']=\"No\"\n",
    "df_train.loc[df_train.basement_flag==1,'basement_flag']=\"Yes\"\n",
    "\n",
    "df_train.loc[df_train.fireplaceflag==False,'fireplaceflag']=\"No\"\n",
    "df_train.loc[df_train.fireplaceflag==True,'fireplaceflag']=\"Yes\"\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ac_id,heating_system_id,propertylandusetypeid\n",
    "dummieslist=['has_hottub_or_spa','just_hottub_or_spa','N-ACInd','N-HeatInd',\n",
    "             'deck_flag','fips','basement_flag','fireplaceflag','taxdelinquencyflag']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train[dummieslist] = df_train[dummieslist].astype(object)\n",
    "dummies = pd.get_dummies(df_train[dummieslist], prefix= dummieslist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>has_hottub_or_spa_0</th>\n",
       "      <th>has_hottub_or_spa_1</th>\n",
       "      <th>just_hottub_or_spa_No</th>\n",
       "      <th>just_hottub_or_spa_Yes</th>\n",
       "      <th>N-ACInd_0</th>\n",
       "      <th>N-ACInd_1</th>\n",
       "      <th>N-HeatInd_0</th>\n",
       "      <th>N-HeatInd_1</th>\n",
       "      <th>deck_flag_No</th>\n",
       "      <th>deck_flag_Yes</th>\n",
       "      <th>fips_LA</th>\n",
       "      <th>fips_Orange_County</th>\n",
       "      <th>fips_Ventura</th>\n",
       "      <th>basement_flag_No</th>\n",
       "      <th>basement_flag_Yes</th>\n",
       "      <th>fireplaceflag_No</th>\n",
       "      <th>fireplaceflag_Yes</th>\n",
       "      <th>taxdelinquencyflag_No</th>\n",
       "      <th>taxdelinquencyflag_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   has_hottub_or_spa_0  has_hottub_or_spa_1  just_hottub_or_spa_No  \\\n",
       "0                    1                    0                      1   \n",
       "1                    1                    0                      1   \n",
       "2                    1                    0                      1   \n",
       "3                    1                    0                      1   \n",
       "4                    1                    0                      1   \n",
       "\n",
       "   just_hottub_or_spa_Yes  N-ACInd_0  N-ACInd_1  N-HeatInd_0  N-HeatInd_1  \\\n",
       "0                       0          0          1            0            1   \n",
       "1                       0          0          1            0            1   \n",
       "2                       0          0          1            0            1   \n",
       "3                       0          0          1            0            1   \n",
       "4                       0          1          0            0            1   \n",
       "\n",
       "   deck_flag_No  deck_flag_Yes  fips_LA  fips_Orange_County  fips_Ventura  \\\n",
       "0             1              0        1                   0             0   \n",
       "1             1              0        0                   1             0   \n",
       "2             1              0        1                   0             0   \n",
       "3             1              0        1                   0             0   \n",
       "4             1              0        0                   1             0   \n",
       "\n",
       "   basement_flag_No  basement_flag_Yes  fireplaceflag_No  fireplaceflag_Yes  \\\n",
       "0                 1                  0                 1                  0   \n",
       "1                 1                  0                 1                  0   \n",
       "2                 1                  0                 1                  0   \n",
       "3                 1                  0                 1                  0   \n",
       "4                 1                  0                 1                  0   \n",
       "\n",
       "   taxdelinquencyflag_No  taxdelinquencyflag_Yes  \n",
       "0                      1                       0  \n",
       "1                      1                       0  \n",
       "2                      1                       0  \n",
       "3                      1                       0  \n",
       "4                      1                       0  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train=df_train.drop(['has_hottub_or_spa','just_hottub_or_spa','N-ACInd','N-HeatInd',\n",
    "             'deck_flag','fips','basement_flag','fireplaceflag','taxdelinquencyflag'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies2=['pool_with_spa_tub_no','pool_with_spa_tub_yes']\n",
    "df_train[dummies2] = df_train[dummies2].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "numcols = set(list(df_train.columns))-set((list(dummies.columns)+dummies2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#numcols2=['basementsqft','total_bath','bedroomcnt','total_finished_living_area_sqft','fireplace_count','garagecarcnt',\n",
    "# 'garage_sqft','latitude','longitude','lot_area_sqft','poolcnt','pool_sqft','roomcnt','unitcnt','patio_sqft','storage_sqft',\n",
    "# 'numberofstories','structure_tax','total_parcel_tax','land_tax','total_property_tax_2016','taxdelinquencyyear','transaction_month',\n",
    "# 'census_tractnumber','block_number','age','N-LivingAreaProp','N-ExtraSpace','N-TotalRooms','N-ExtraRooms','N-AvRoomSize','N-ValueProp',\n",
    "#'N-ValueRatio','N-LivingAreaProp','N-ExtraSpace','N-TaxScore','N-structure_tax-2','N-structure_tax-3']\n",
    "\n",
    "\n",
    "\n",
    "numcols=['N-AvRoomSize','N-ExtraRooms','N-ExtraSpace','N-LivingAreaProp','N-TaxScore','N-TotalRooms','N-ValueProp','N-ValueRatio',\n",
    " 'N-structure_tax-2','N-structure_tax-3','age','basementsqft','bedroomcnt','block_number','buildingqualitytypeid',\n",
    " 'census_tractnumber','fireplace_count','garage_sqft','garagecarcnt','land_tax','latitude','longitude',\n",
    " 'lot_area_sqft','numberofstories','patio_sqft','pool_sqft','poolcnt','roomcnt','storage_sqft','structure_tax',\n",
    " 'taxdelinquencyyear','total_bath','total_finished_living_area_sqft','total_parcel_tax','total_property_tax_2016','transaction_month',\n",
    " 'unitcnt']\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=df_train['logerror']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['transaction_month'] = df_train['transactiondate'].dt.month\n",
    "df_train=df_train.drop('parcelid',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "df_train=df_train.drop(['transactiondate'], axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2 variables are in object datatype, coverting into numeric\n",
    "df_train[['census_tractnumber','block_number']] = df_train[['census_tractnumber','block_number']].apply(pd.to_numeric)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['has_hottub_or_spa_0', 'has_hottub_or_spa_1', 'just_hottub_or_spa_No',\n",
       "       'just_hottub_or_spa_Yes', 'N-ACInd_0', 'N-ACInd_1', 'N-HeatInd_0',\n",
       "       'N-HeatInd_1', 'deck_flag_No', 'deck_flag_Yes', 'fips_LA',\n",
       "       'fips_Orange_County', 'fips_Ventura', 'basement_flag_No',\n",
       "       'basement_flag_Yes', 'fireplaceflag_No', 'fireplaceflag_Yes',\n",
       "       'taxdelinquencyflag_No', 'taxdelinquencyflag_Yes',\n",
       "       'pool_with_spa_tub_no', 'pool_with_spa_tub_yes', 'N-AvRoomSize',\n",
       "       'N-ExtraRooms', 'N-ExtraSpace', 'N-LivingAreaProp', 'N-TaxScore',\n",
       "       'N-TotalRooms', 'N-ValueProp', 'N-ValueRatio', 'N-structure_tax-2',\n",
       "       'N-structure_tax-3', 'age', 'basementsqft', 'bedroomcnt',\n",
       "       'block_number', 'buildingqualitytypeid', 'census_tractnumber',\n",
       "       'fireplace_count', 'garage_sqft', 'garagecarcnt', 'land_tax',\n",
       "       'latitude', 'longitude', 'lot_area_sqft', 'numberofstories',\n",
       "       'patio_sqft', 'pool_sqft', 'poolcnt', 'roomcnt', 'storage_sqft',\n",
       "       'structure_tax', 'taxdelinquencyyear', 'total_bath',\n",
       "       'total_finished_living_area_sqft', 'total_parcel_tax',\n",
       "       'total_property_tax_2016', 'transaction_month', 'unitcnt'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=pd.concat([dummies,df_train[dummies2],df_train[numcols]],axis=1)\n",
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.10, random_state=3192)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:164: UserWarning: Numerical issues were encountered when centering the data and might not be solved. Dataset may contain too large values. You may need to prescale your features.\n",
      "  warnings.warn(\"Numerical issues were encountered \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pca selection\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import scale\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import scale\n",
    "%matplotlib inline\n",
    "scaled_x = scale(X)\n",
    "pca = PCA(n_components=None, copy=True, whiten=False, svd_solver='auto', tol=0.0, iterated_power='auto', random_state=None)\n",
    "pca.fit(scaled_x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  14.28   24.87   32.29   38.     43.48   48.64   53.42   58.1    61.77\n",
      "   65.11   68.02   70.67   73.02   75.24   77.39   79.26   81.05   82.78\n",
      "   84.49   86.17   87.83   89.42   90.91   92.22   93.4    94.54   95.5\n",
      "   96.29   96.86   97.36   97.77   98.18   98.51   98.81   99.07   99.33\n",
      "   99.55   99.72   99.83   99.91   99.95   99.99  100.02  100.03  100.03\n",
      "  100.03  100.03  100.03  100.03  100.03  100.03  100.03  100.03  100.03\n",
      "  100.03  100.03  100.03  100.03]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fb9c126d30>]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rcabe9CwwP/L9fOAZD7Mctcg87wPARufcjw/6kW/3y8zyIiN5zCwdOJvuzx5e\nBeZFNvPVPjnnbnfOFTjniuj+HXrFOXcFPt4nADPLNLOB730PnAusw8fvv2j4+oQpM7uA7tFHIvCg\nc+77HkfqETP7HXAG3VfXqwbuAP4ALAYKgXLgUufc+z+wjVlm9jHgDWAt/5j7/Rbd8/S+3C8zm0b3\nB3iJdA+SFjvn7jSzsXSPhnOAUuBK51y7d0l7JjJ183Xn3EV+36dI/t9HFpOAR51z3zezXHz6/ouG\nr4teREQOz89TNyIicgRU9CIiAaeiFxEJOBW9iEjAqehFRAJORS8iEnAqehGRgFPRi4gE3P8BteEk\n4oKDf24AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb3eda5128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#The amount of variance that each PC explains\n",
    "var= pca.explained_variance_ratio_\n",
    "#Cumulative Variance explains\n",
    "var1=np.cumsum(np.round(pca.explained_variance_ratio_, decimals=4)*100)\n",
    "print(var1)\n",
    "plt.plot(var1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Looking at above plot I'm taking 30 variables\n",
    "\n",
    "pca = PCA(n_components=30, copy=True, whiten=False, svd_solver='auto', tol=0.0, iterated_power='auto', random_state=None)\n",
    "pca.fit(scaled_x)\n",
    "\n",
    "pca1=pca.fit_transform(scaled_x)\n",
    "\n",
    "pca = PCA(n_components=30, copy=True, whiten=True, svd_solver='auto', tol=0.0, iterated_power='auto', random_state=None)\n",
    "pca.fit(scaled_x)\n",
    "pca2=pca.fit_transform(scaled_x)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcaX_train, pcaX_test, pcaY_train, pcaY_test = train_test_split(pca1, Y, test_size=0.10, random_state=3192)\n",
    "pca2X_train, pca2X_test, pca2Y_train, pca2Y_test = train_test_split(pca2, Y, test_size=0.10, random_state=3192)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oqFjruazweIyKQ7GPU/H9TW1XAi8CkyF7xwqYna49GBFjG7m/FFhMlujU59Oa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\nRqWhGMdpyJAhz0fEwHzqbskzJ0UhIs6QdDwwBBiec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GM8//zzVFZWsnbtWj788EN+/etf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\nysQFl5mZmZmZWZm44DIzMzMzMyuT/weZztdb9NyUfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fba94370f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  top features\n",
    "import xgboost as xgb\n",
    "xgb_params = {\n",
    "    'eta': 0.05,\n",
    "    'max_depth': 8,\n",
    "    'subsample': 0.7,\n",
    "    'colsample_bytree': 0.7,\n",
    "    'objective': 'reg:linear',\n",
    "    'silent': 1,\n",
    "    'seed' : 0\n",
    "}\n",
    "dtrain = xgb.DMatrix(X_train, Y_train, feature_names=X_train.columns.values)\n",
    "model = xgb.train(dict(xgb_params, silent=0), dtrain, num_boost_round=50)\n",
    "# plot the important features #\n",
    "fig, ax = plt.subplots(figsize=(12,18))\n",
    "#max_num_features=50, error for no reason \n",
    "xgb.plot_importance(model, height=0.8, ax=ax)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "#top features\n",
    "#xgboost_selection=['total_finished_living_area_sqft','latitude','structure_tax','total_property_tax_2016',\n",
    "#'total_parcel_tax','land_tax','longitude','lot_area_sqft','census_tractnumber','age','total_bath','bedroomcnt',\n",
    "#'block_number','transaction_month','roomcnt','taxdelinquencyyear','unitcnt','taxdelinquencyflag_No',\n",
    "#'fips_LA','garage_sqft','pool_with_spa_tub_no','has_hottub_or_spa_No','garagecarcnt','deck_flag_No',\n",
    "#'poolcnt','pool_sqft'\n",
    "#]\n",
    "\n",
    "xgboost_selection=['N-AvRoomSize','N-ValueRatio','N-ExtraSpace','N-TaxScore','N-ValueProp','total_finished_living_area_sqft','N-structure_tax-2','latitude','census_tractnumber',\n",
    "'total_property_tax_2016','N-LivingAreaProp','longitude','age','N-ExtraRooms','land_tax','block_number','total_parcel_tax','transaction_month',\n",
    "'N-TotalRooms','bedroomcnt','fips_LA','taxdelinquencyflag_No','pool_with_spa_tub_no','total_bath','garage_sqft','roomcnt','buildingqualitytypeid','unitcnt',\n",
    "'fips_Orange_County','has_hottub_or_spa_0','taxdelinquencyflag_Yes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(81246, 58)\n",
      "(81246, 26)\n"
     ]
    },
    {
     "data": {
      "image/png": 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QYRsAAAYRtgEAYBBhGwAABhG2AQBgEGEbAAAGEbYBAGAQYRsAAAYRtgEAYBBhGwAABhG2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\nk21JkiRpSky2JUmSpCkx2ZYkSZKm5H8BDcO60oLRslgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fbfa49ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# feature selection\n",
    "#c_id,heating_system_id,propertylandusetypeid\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "reg = ExtraTreesRegressor(n_estimators=500, max_depth=8, max_features='sqrt',\n",
    "                          min_samples_split=100 ,min_samples_leaf=10, bootstrap=True,n_jobs=-1, random_state=3192)\n",
    "reg = reg.fit(X_train, Y_train)\n",
    "#print(\"importance\",reg.feature_importances_) \n",
    "model = SelectFromModel(reg, prefit=True)\n",
    "# since we haven'tset any thresold , it applies l1 regularisation and picks 27 attributes\n",
    "X_new = model.transform(X_train)\n",
    "print(X_train.shape)\n",
    "print(X_new.shape)  \n",
    "\n",
    "feat_names = X.columns.values\n",
    "importances = reg.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in reg.estimators_], axis=0)\n",
    "indices = np.argsort(importances)[::-1][:36]\n",
    "plt.figure(figsize=(12,12))\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(len(indices)), importances[indices], color=\"r\", yerr=std[indices], align=\"center\")\n",
    "plt.xticks(range(len(indices)), feat_names[indices], rotation='vertical')\n",
    "plt.xlim([-1, len(indices)])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tree_selection=[\n",
    "#    'total_finished_living_area_sqft','structure_tax','total_property_tax_2016','total_bath','total_parcel_tax',\n",
    "#    'age','latitude','census_tractnumber','bedroomcnt','longitude','land_tax','propertylandusetypeid','block_number',\n",
    "#    'buildingqualitytypeid','numberofstories','heating_system_id','unitcnt','transaction_month','lot_area_sqft','roomcnt',\n",
    "#    'garage_sqft','garagecarcnt','pool_with_spa_tub_no','poolcnt','fips_LA','taxdelinquencyyear','patio_sqft',\n",
    "#    'taxdelinquencyflag_No','taxdelinquencyflag_Yes'\n",
    "#]\n",
    "\n",
    "tree_selection=['total_finished_living_area_sqft','structure_tax','N-TotalRooms','total_bath','roomcnt','bedroomcnt','total_property_tax_2016',\n",
    "                'N-ExtraRooms',\n",
    "'taxdelinquencyflag_No','longitude','age','transaction_month','N-LivingAreaProp','census_tractnumber','N-AvRoomSize','total_parcel_tax',\n",
    "'taxdelinquencyflag_Yes','N-ValueRatio','latitude','land_tax','numberofstories','taxdelinquencyyear','buildingqualitytypeid','poolcnt',\n",
    "'N-TaxScore','block_number','pool_with_spa_tub_no','pool_sqft','unitcnt','fips_Ventura','lot_area_sqft','N-ExtraSpace','fips_Orange_County',\n",
    "'garagecarcnt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features : 28\n"
     ]
    },
    {
     "data": {
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cksmb/zORnsmxAEwZnkHn2EjeWLStkbsczbv4NtnVL8RQOxwU0bq9p/nZ+QxO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\nOeec63K8cHLOOdfleOHknHOuy/k/iiyGLR3oD+QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb80f1d240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.linear_model import Ridge,Lasso\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score,mean_absolute_error,make_scorer\n",
    "\n",
    "#model=Lasso(alpha=0.2, fit_intercept=True, normalize=True, precompute=False, copy_X=True,\n",
    " #                                max_iter=1000, \n",
    "  #                               tol=0.0001, warm_start=False, positive=False, random_state=3192, selection='cyclic')\n",
    "\n",
    "#Ridge(random_state=3192,solver='auto',fit_intercept=True,normalize=True,alpha=0.1)\n",
    "#LinearRegression(n_jobs=-1,fit_intercept=True, normalize=True, copy_X=True)\n",
    "\n",
    "\n",
    "rfecv = RFECV(estimator=LinearRegression(n_jobs=-1,fit_intercept=True, normalize=True, copy_X=True), step=2, cv=KFold(4),scoring='neg_mean_absolute_error')\n",
    "rfecv.fit(X_train, Y_train)\n",
    "\n",
    "print(\"Optimal number of features : %d\" % rfecv.n_features_)\n",
    "\n",
    "# Plot number of features VS. cross-validation scores\n",
    "plt.figure()\n",
    "plt.xlabel(\"Number of features selected\")\n",
    "\n",
    "plt.ylabel(\"Cross validation score (nb of correct classifications)\")\n",
    "plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfe_selection = [i for indx,i in enumerate(X.columns) if rfecv.support_[indx] == True]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE on test data 0.0673694173694\n",
      "MSE on test data 0.0235427546673\n",
      "R Squared data  0.0018341340652\n"
     ]
    }
   ],
   "source": [
    "#pcaX_train, pcaX_test, pcaY_train, pcaY_test\n",
    "\n",
    "#rfe_selection, tree_selection, xgboost_selection\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score,mean_absolute_error,make_scorer,mean_squared_error\n",
    "\n",
    "# just to check whether normalized /not normalized data gives better results\n",
    "parameters = {'fit_intercept':[True], 'normalize':[True,False], 'copy_X':[True]}\n",
    "scoring = {'MAE':'neg_mean_absolute_error','MSE': make_scorer(mean_squared_error,greater_is_better=False)}\n",
    "\n",
    "grid = GridSearchCV(LinearRegression(n_jobs=-1),param_grid=parameters, scoring=scoring,cv=5,refit='MAE',\n",
    "                    return_train_score=True,\n",
    "                    verbose=0,n_jobs=-1,pre_dispatch='n_jobs')\n",
    "\n",
    "grid.fit(pcaX_train, pcaY_train)\n",
    "#print(\"5. grid best_score_\",abs(grid.best_score_))\n",
    "pcaY_pred = grid.predict(pcaX_test)\n",
    "print(\"MAE on test data\",mean_absolute_error(pcaY_test,pcaY_pred))\n",
    "print(\"MSE on test data\",mean_squared_error(pcaY_test,pcaY_pred))\n",
    "print(\"R Squared data \",r2_score(pcaY_test,pcaY_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE on test data 0.0672987042915\n",
      "MSE on test data 0.0235809119005\n",
      "R Squared data  0.00021634344423\n"
     ]
    }
   ],
   "source": [
    "#Linear regression with rfe_selection selection\n",
    "#rfe_selection, tree_selection, xgboost_selection\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score,mean_absolute_error,make_scorer,mean_squared_error\n",
    "\n",
    "# just to check whether normalized /not normalized data gives better results\n",
    "parameters = {'fit_intercept':[True], 'normalize':[True,False], 'copy_X':[True]}\n",
    "scoring = {'MAE':'neg_mean_absolute_error','MSE': make_scorer(mean_squared_error,greater_is_better=False)}\n",
    "\n",
    "grid = GridSearchCV(LinearRegression(n_jobs=-1),param_grid=parameters, scoring=scoring,cv=5,refit='MAE',\n",
    "                    return_train_score=True,\n",
    "                    verbose=0,n_jobs=-1,pre_dispatch='n_jobs')\n",
    "\n",
    "grid.fit(X_train[rfe_selection], Y_train)\n",
    "#print(\"5. grid best_score_\",abs(grid.best_score_))\n",
    "Y_pred = grid.predict(X_test[rfe_selection])\n",
    "print(\"MAE on test data\",mean_absolute_error(Y_test,Y_pred))\n",
    "print(\"MSE on test data\",mean_squared_error(Y_test,Y_pred))\n",
    "print(\"R Squared data \",r2_score(Y_test,Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE on test data 0.0673154878507\n",
      "MSE on test data 0.0234384838998\n",
      "R Squared data  0.00625500674349\n"
     ]
    }
   ],
   "source": [
    "#tree_selection\n",
    "\n",
    "#Linear regression with tree_selection selection\n",
    "#rfe_selection, tree_selection, xgboost_selection\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score,mean_absolute_error,make_scorer\n",
    "\n",
    "# just to check whether normalized /not normalized data gives better results\n",
    "parameters = {'fit_intercept':[True], 'normalize':[True,False], 'copy_X':[True]}\n",
    "scoring = {'MAE':'neg_mean_absolute_error','MSE': make_scorer(mean_squared_error,greater_is_better=False)}\n",
    "\n",
    "grid = GridSearchCV(LinearRegression(n_jobs=-1),param_grid=parameters, scoring=scoring,cv=5,refit='MAE',\n",
    "                    return_train_score=True,\n",
    "                    verbose=0,n_jobs=-1,pre_dispatch='n_jobs')\n",
    "\n",
    "grid.fit(X_train[tree_selection], Y_train)\n",
    "#print(\"5. grid best_score_\",abs(grid.best_score_))\n",
    "Y_pred = grid.predict(X_test[tree_selection])\n",
    "print(\"MAE on test data\",mean_absolute_error(Y_test,Y_pred))\n",
    "print(\"MSE on test data\",mean_squared_error(Y_test,Y_pred))\n",
    "print(\"R Squared data \",r2_score(Y_test,Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE on test data 0.067344125298\n",
      "MSE on test data 0.0234477095454\n",
      "R Squared data  0.00586385776056\n"
     ]
    }
   ],
   "source": [
    " #xgboost_selection\n",
    "\n",
    "#Linear regression with tree_selection selection\n",
    "#rfe_selection, tree_selection, xgboost_selection\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score,mean_absolute_error,make_scorer\n",
    "\n",
    "# just to check whether normalized /not normalized data gives better results\n",
    "parameters = {'fit_intercept':[True], 'normalize':[True,False], 'copy_X':[True]}\n",
    "scoring = {'MAE':'neg_mean_absolute_error','MSE': make_scorer(mean_squared_error,greater_is_better=False)}\n",
    "\n",
    "grid = GridSearchCV(LinearRegression(n_jobs=-1),param_grid=parameters, scoring=scoring,cv=5,refit='MAE',\n",
    "                    return_train_score=True,\n",
    "                    verbose=0,n_jobs=-1,pre_dispatch='n_jobs')\n",
    "\n",
    "grid.fit(X_train[xgboost_selection], Y_train)\n",
    "#print(\"5. grid best_score_\",abs(grid.best_score_))\n",
    "Y_pred = grid.predict(X_test[xgboost_selection])\n",
    "print(\"MAE on test data\",mean_absolute_error(Y_test,Y_pred))\n",
    "print(\"MSE on test data\",mean_squared_error(Y_test,Y_pred))\n",
    "print(\"R Squared data \",r2_score(Y_test,Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best params {'alpha': 0.1, 'copy_X': True, 'fit_intercept': True, 'normalize': True}\n",
      "MAE on test data 0.0673192378296\n",
      "MSE on test data 0.0235716614478\n",
      "R Squared data  0.000608544195029\n"
     ]
    }
   ],
   "source": [
    "#Ridge\n",
    "#rfe_selection, tree_selection, xgboost_selection\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import uniform as sp_rand\n",
    "from sklearn.metrics import r2_score,mean_absolute_error,make_scorer\n",
    "\n",
    "# just to check whether normalized /not normalized data gives better results\n",
    "parameters = {'fit_intercept':[True], 'normalize':[True], 'copy_X':[True],'alpha':[0.001,0.1,0.01,0.02,0.03,0.05,0.2,0.015,0.020]}\n",
    "scoring = {'MAE':'neg_mean_absolute_error','MSE': make_scorer(mean_squared_error,greater_is_better=False)}\n",
    "\n",
    "grid = GridSearchCV(Ridge(random_state=3192,solver='auto'),param_grid=parameters, scoring=scoring,cv=5,refit='MAE',\n",
    "                    return_train_score=True,\n",
    "                    verbose=0,n_jobs=-1,pre_dispatch='n_jobs')\n",
    "\n",
    "grid.fit(X_train[rfe_selection], Y_train)\n",
    "#print(\"5. grid best_score_\",abs(grid.best_score_))\n",
    "print(\"best params\",grid.best_params_)\n",
    "Y_pred = grid.predict(X_test[rfe_selection])\n",
    "print(\"MAE on test data\",mean_absolute_error(Y_test,Y_pred))\n",
    "print(\"MSE on test data\",mean_squared_error(Y_test,Y_pred))\n",
    "print(\"R Squared data \",r2_score(Y_test,Y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best params {'alpha': 0.2, 'copy_X': True, 'fit_intercept': True, 'normalize': True}\n",
      "MAE on test data 0.0672317928274\n",
      "MSE on test data 0.0234976448245\n",
      "R Squared data  0.00374670147542\n"
     ]
    }
   ],
   "source": [
    "#Ridge\n",
    "#rfe_selection, tree_selection, xgboost_selection\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import uniform as sp_rand\n",
    "from sklearn.metrics import r2_score,mean_absolute_error,make_scorer\n",
    "\n",
    "# just to check whether normalized /not normalized data gives better results\n",
    "parameters = {'fit_intercept':[True], 'normalize':[True], 'copy_X':[True],'alpha':[0.001,0.1,0.01,0.02,0.03,0.05,0.2,0.015,0.020]}\n",
    "scoring = {'MAE':'neg_mean_absolute_error','MSE': make_scorer(mean_squared_error,greater_is_better=False)}\n",
    "\n",
    "grid = GridSearchCV(Ridge(random_state=3192,solver='auto'),param_grid=parameters, scoring=scoring,cv=5,refit='MAE',\n",
    "                    return_train_score=True,\n",
    "                    verbose=0,n_jobs=-1,pre_dispatch='n_jobs')\n",
    "\n",
    "grid.fit(X_train[tree_selection], Y_train)\n",
    "#print(\"5. grid best_score_\",abs(grid.best_score_))\n",
    "print(\"best params\",grid.best_params_)\n",
    "Y_pred = grid.predict(X_test[tree_selection])\n",
    "print(\"MAE on test data\",mean_absolute_error(Y_test,Y_pred))\n",
    "print(\"MSE on test data\",mean_squared_error(Y_test,Y_pred))\n",
    "print(\"R Squared data \",r2_score(Y_test,Y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best params {'alpha': 0.2, 'copy_X': True, 'fit_intercept': True, 'normalize': True}\n",
      "MAE on test data 0.0672200578814\n",
      "MSE on test data 0.023500015669\n",
      "R Squared data  0.00364618239141\n"
     ]
    }
   ],
   "source": [
    "#Ridge\n",
    "#rfe_selection, tree_selection, xgboost_selection\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import uniform as sp_rand\n",
    "from sklearn.metrics import r2_score,mean_absolute_error,make_scorer\n",
    "\n",
    "# just to check whether normalized /not normalized data gives better results\n",
    "parameters = {'fit_intercept':[True], 'normalize':[True], 'copy_X':[True],'alpha':[0.001,0.1,0.01,0.02,0.03,0.05,0.2,0.015]}\n",
    "scoring = {'MAE':'neg_mean_absolute_error','MSE': make_scorer(mean_squared_error,greater_is_better=False)}\n",
    "\n",
    "grid = GridSearchCV(Ridge(random_state=3192,solver='auto'),param_grid=parameters, scoring=scoring,cv=5,refit='MAE',\n",
    "                    return_train_score=True,\n",
    "                    verbose=0,n_jobs=-1,pre_dispatch='n_jobs')\n",
    "\n",
    "grid.fit(X_train[xgboost_selection], Y_train)\n",
    "#print(\"5. grid best_score_\",abs(grid.best_score_))\n",
    "print(\"best params\",grid.best_params_)\n",
    "Y_pred = grid.predict(X_test[xgboost_selection])\n",
    "print(\"MAE on test data\",mean_absolute_error(Y_test,Y_pred))\n",
    "print(\"MSE on test data\",mean_squared_error(Y_test,Y_pred))\n",
    "print(\"R Squared data \",r2_score(Y_test,Y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. grid best_score_ 0.0148213468765\n",
      "best params {'n_estimators': 1200}\n",
      "best score 0.0148213468765\n",
      "MAE on test data 0.0672892583453\n",
      "MSE on test data 0.02311353979\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_absolute_error,make_scorer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# just to check whether normalized /not normalized data gives better results\n",
    "\n",
    " # 0.005 for 1200 trees.\n",
    "#param_grid={'n_estimators':[1000,1200]}\n",
    "param_grid={'n_estimators':[1200]}              \n",
    "grid1 = GridSearchCV(GradientBoostingRegressor(subsample=0.8,max_features=11,min_samples_leaf=50,min_samples_split=50,\n",
    "                                        \n",
    "                                               max_depth=9,loss='ls',criterion='friedman_mse',learning_rate=0.005,\n",
    "                                             random_state=3192),\n",
    "                     param_grid=param_grid, cv=5,refit='MAE',\n",
    "                    return_train_score=True,\n",
    "                    verbose=0,n_jobs=-1,pre_dispatch='n_jobs')\n",
    "\n",
    "grid1.fit(X_train[xgboost_selection], Y_train)\n",
    "print(\"5. grid best_score_\",abs(grid1.best_score_))\n",
    "print(\"best params\",grid1.best_params_)\n",
    "print(\"best score\",grid1.best_score_)\n",
    "Y_pred = grid1.predict(X_test[xgboost_selection])\n",
    "print(\"MAE on test data\",mean_absolute_error(Y_test,Y_pred))\n",
    "print(\"MSE on test data\",mean_squared_error(Y_test,Y_pred))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. grid best_score_ 0.0144432458404\n",
      "best params {'max_features': 22, 'n_estimators': 1200}\n",
      "best score 0.0144432458404\n",
      "MAE on test data 0.0671834477878\n",
      "MSE on test data 0.0230845083703\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_absolute_error,make_scorer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# just to check whether normalized /not normalized data gives better results\n",
    "\n",
    " # 0.005 for 1200 trees.\n",
    "param_grid={'n_estimators':[1200],'max_features':[22]}\n",
    "\n",
    "              \n",
    "grid1 = GridSearchCV(GradientBoostingRegressor(subsample=0.8,min_samples_leaf=50,min_samples_split=50,max_depth=9,loss='ls',criterion='friedman_mse',learning_rate=0.005,random_state=3192),\n",
    "                     param_grid=param_grid, cv=5,refit='MAE',\n",
    "                    return_train_score=True,\n",
    "                    verbose=0,n_jobs=-1,pre_dispatch='n_jobs')\n",
    "\n",
    "grid1.fit(X_train, Y_train)\n",
    "print(\"5. grid best_score_\",abs(grid1.best_score_))\n",
    "print(\"best params\",grid1.best_params_)\n",
    "print(\"best score\",grid1.best_score_)\n",
    "Y_pred = grid1.predict(X_test)\n",
    "print(\"MAE on test data\",mean_absolute_error(Y_test,Y_pred))\n",
    "print(\"MSE on test data\",mean_squared_error(Y_test,Y_pred))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. grid best_score_ 0.00915215652412\n",
      "best params {'max_features': 22, 'n_estimators': 1200}\n",
      "best score 0.00915215652412\n",
      "MAE on test data 0.0674881717265\n",
      "MSE on test data 0.0232135051855\n"
     ]
    }
   ],
   "source": [
    "#pcaX_train, pcaX_test, pcaY_train, pcaY_test\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_absolute_error,make_scorer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# just to check whether normalized /not normalized data gives better results\n",
    "\n",
    " # 0.005 for 1200 trees.\n",
    "param_grid={'n_estimators':[1200],'max_features':[22]}\n",
    "\n",
    "              \n",
    "grid1 = GridSearchCV(GradientBoostingRegressor(subsample=0.8,min_samples_leaf=50,min_samples_split=50,max_depth=9,loss='ls',criterion='friedman_mse',learning_rate=0.005,random_state=3192),\n",
    "                     param_grid=param_grid, cv=5,refit='MAE',\n",
    "                    return_train_score=True,\n",
    "                    verbose=0,n_jobs=-1,pre_dispatch='n_jobs')\n",
    "\n",
    "grid1.fit(pcaX_train, pcaY_train)\n",
    "print(\"5. grid best_score_\",abs(grid1.best_score_))\n",
    "print(\"best params\",grid1.best_params_)\n",
    "print(\"best score\",grid1.best_score_)\n",
    "pcaY_pred = grid1.predict(pcaX_test)\n",
    "print(\"MAE on test data\",mean_absolute_error(pcaY_test,pcaY_pred))\n",
    "print(\"MSE on test data\",mean_squared_error(pcaY_test,pcaY_pred))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "def modelfit(model, X_train, Y_train,X_test,Y_test,useTrainCV=True, cv_folds=5, early_stopping_rounds=50):\n",
    "    \n",
    "    if useTrainCV:\n",
    "        xgb_param = model.get_xgb_params()\n",
    "        \n",
    "        xgtrain = xgb.DMatrix(X_train, Y_train)\n",
    "        \n",
    "        cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=model.get_params()['n_estimators'], nfold=cv_folds,\n",
    "            metrics='mae', early_stopping_rounds=early_stopping_rounds, verbose_eval=1,seed=3192)\n",
    "        \n",
    "        model.set_params(n_estimators=cvresult.shape[0])\n",
    "    \n",
    "    #Fit the algorithm on the data\n",
    "    model.fit(X_train,Y_train,eval_metric='mae')\n",
    "        \n",
    "    #Predict training set:\n",
    "    y_pred = model.predict(X_test)\n",
    "    print(\"MAE on test data\",mean_absolute_error(Y_test, y_pred))\n",
    "\n",
    "                    \n",
    "    feat_imp = pd.Series(model.booster().get_fscore()).sort_values(ascending=False)\n",
    "    feat_imp.plot(kind='bar', title='Feature Importances')\n",
    "    plt.ylabel('Feature Importance Score')\n",
    "    #print(\" model.get_params()['n_estimators']\",model.get_params()['n_estimators'])\n",
    "    print(\"n_estimators\",cvresult.shape[0])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 11 candidates, totalling 55 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  2.3min\n",
      "[Parallel(n_jobs=-1)]: Done  55 out of  55 | elapsed:  3.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. grid best_score_ 0.0682312019914\n",
      "best params {'reg_alpha': 0.07}\n",
      "best score -0.0682312019914\n",
      "MAE on test data 0.0671153390846\n",
      "MSE on test data 0.023314226378\n"
     ]
    }
   ],
   "source": [
    "param_test7 = {\n",
    " 'reg_alpha':[0.001,0.005,0.01,0.03,0.04,0.05,0.06,0.07,0.08,0.09,0.1]\n",
    "}\n",
    "\n",
    "gsearch7 = GridSearchCV(estimator = XGBRegressor(learning_rate =0.1,n_estimators=81,\n",
    "                    max_depth=2,\n",
    "                    min_child_weight=350,\n",
    "                    gamma=0.4,\n",
    "                    objective= 'reg:linear',\n",
    "                    nthread=-1,scale_pos_weight=1,seed=3192,colsample_bytree=0.9,subsample=0.95),\n",
    "param_grid = param_test7, scoring='neg_mean_absolute_error',n_jobs=-1, cv=5,verbose=2)\n",
    "\n",
    "gsearch7.fit(X_train, Y_train)\n",
    "print(\"5. grid best_score_\",abs(gsearch7.best_score_))\n",
    "print(\"best params\",gsearch7.best_params_)\n",
    "print(\"best score\",gsearch7.best_score_)\n",
    "\n",
    "Y_pred = gsearch7.predict(X_test)\n",
    "print(\"MAE on test data\",mean_absolute_error(Y_test,Y_pred))\n",
    "print(\"MSE on test data\",mean_squared_error(Y_test,Y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-mae:0.449081+0.000243059\ttest-mae:0.449085+0.00105928\n",
      "[1]\ttrain-mae:0.40611+0.000223214\ttest-mae:0.406112+0.00104221\n",
      "[2]\ttrain-mae:0.367609+0.000196549\ttest-mae:0.367612+0.00101597\n",
      "[3]\ttrain-mae:0.33313+0.000185393\ttest-mae:0.333138+0.000980525\n",
      "[4]\ttrain-mae:0.302301+0.000170008\ttest-mae:0.302312+0.000963247\n",
      "[5]\ttrain-mae:0.27477+0.00015269\ttest-mae:0.274784+0.000960584\n",
      "[6]\ttrain-mae:0.250181+0.00015499\ttest-mae:0.250194+0.000962728\n",
      "[7]\ttrain-mae:0.228246+0.000150753\ttest-mae:0.228265+0.00096685\n",
      "[8]\ttrain-mae:0.2087+0.000146362\ttest-mae:0.208718+0.000990338\n",
      "[9]\ttrain-mae:0.191308+0.000147627\ttest-mae:0.191328+0.00100849\n",
      "[10]\ttrain-mae:0.175853+0.000146576\ttest-mae:0.175878+0.00102924\n",
      "[11]\ttrain-mae:0.162124+0.000146515\ttest-mae:0.162149+0.00102543\n",
      "[12]\ttrain-mae:0.14995+0.000148152\ttest-mae:0.149982+0.00101946\n",
      "[13]\ttrain-mae:0.139183+0.000146013\ttest-mae:0.13922+0.00101786\n",
      "[14]\ttrain-mae:0.12968+0.00013815\ttest-mae:0.12972+0.00100334\n",
      "[15]\ttrain-mae:0.12131+0.000149881\ttest-mae:0.121356+0.000962567\n",
      "[16]\ttrain-mae:0.113961+0.000146053\ttest-mae:0.114007+0.000939306\n",
      "[17]\ttrain-mae:0.107532+0.000155373\ttest-mae:0.10758+0.000912006\n",
      "[18]\ttrain-mae:0.101923+0.000162872\ttest-mae:0.101974+0.000895984\n",
      "[19]\ttrain-mae:0.0970394+0.000165867\ttest-mae:0.097093+0.000873301\n",
      "[20]\ttrain-mae:0.0928208+0.000161586\ttest-mae:0.0928772+0.000840864\n",
      "[21]\ttrain-mae:0.0891664+0.000164937\ttest-mae:0.0892298+0.000807899\n",
      "[22]\ttrain-mae:0.0860238+0.000160912\ttest-mae:0.0860936+0.000797437\n",
      "[23]\ttrain-mae:0.0833326+0.000159647\ttest-mae:0.0834072+0.0007909\n",
      "[24]\ttrain-mae:0.0810346+0.000161536\ttest-mae:0.0811144+0.000772795\n",
      "[25]\ttrain-mae:0.0790712+0.000162268\ttest-mae:0.079155+0.000752466\n",
      "[26]\ttrain-mae:0.0774184+0.000168922\ttest-mae:0.0775048+0.000734708\n",
      "[27]\ttrain-mae:0.0760028+0.000173643\ttest-mae:0.076097+0.000723318\n",
      "[28]\ttrain-mae:0.07481+0.000176177\ttest-mae:0.0749024+0.000707654\n",
      "[29]\ttrain-mae:0.0738052+0.000167539\ttest-mae:0.073897+0.000695714\n",
      "[30]\ttrain-mae:0.072943+0.000163107\ttest-mae:0.073037+0.000687605\n",
      "[31]\ttrain-mae:0.0722236+0.000159395\ttest-mae:0.0723182+0.000678498\n",
      "[32]\ttrain-mae:0.0716204+0.000154297\ttest-mae:0.0717132+0.0006782\n",
      "[33]\ttrain-mae:0.071102+0.000156263\ttest-mae:0.071198+0.000665858\n",
      "[34]\ttrain-mae:0.0706684+0.000151463\ttest-mae:0.0707636+0.000659314\n",
      "[35]\ttrain-mae:0.0703042+0.000154082\ttest-mae:0.0704012+0.00065331\n",
      "[36]\ttrain-mae:0.0699954+0.000151848\ttest-mae:0.0700926+0.000651407\n",
      "[37]\ttrain-mae:0.069729+0.000148751\ttest-mae:0.0698294+0.000645237\n",
      "[38]\ttrain-mae:0.0695138+0.000147432\ttest-mae:0.0696156+0.000639642\n",
      "[39]\ttrain-mae:0.069324+0.000147363\ttest-mae:0.069429+0.000635502\n",
      "[40]\ttrain-mae:0.06916+0.000145517\ttest-mae:0.0692666+0.000635389\n",
      "[41]\ttrain-mae:0.0690228+0.000145803\ttest-mae:0.0691312+0.000630334\n",
      "[42]\ttrain-mae:0.0689042+0.00014075\ttest-mae:0.0690164+0.000628303\n",
      "[43]\ttrain-mae:0.0687988+0.000143518\ttest-mae:0.0689108+0.000625523\n",
      "[44]\ttrain-mae:0.0687108+0.000141262\ttest-mae:0.0688218+0.000625317\n",
      "[45]\ttrain-mae:0.068633+0.000141865\ttest-mae:0.0687442+0.000623937\n",
      "[46]\ttrain-mae:0.068567+0.000143233\ttest-mae:0.0686782+0.0006217\n",
      "[47]\ttrain-mae:0.0685082+0.000140909\ttest-mae:0.0686226+0.000620316\n",
      "[48]\ttrain-mae:0.0684576+0.000142419\ttest-mae:0.0685728+0.000618375\n",
      "[49]\ttrain-mae:0.0684124+0.000139676\ttest-mae:0.0685294+0.000617589\n",
      "[50]\ttrain-mae:0.0683752+0.000139046\ttest-mae:0.0684938+0.000617249\n",
      "[51]\ttrain-mae:0.0683442+0.000139577\ttest-mae:0.068466+0.00061632\n",
      "[52]\ttrain-mae:0.068317+0.000139695\ttest-mae:0.0684386+0.000616094\n",
      "[53]\ttrain-mae:0.0682882+0.000142472\ttest-mae:0.0684118+0.000614368\n",
      "[54]\ttrain-mae:0.0682642+0.000141889\ttest-mae:0.0683882+0.00061468\n",
      "[55]\ttrain-mae:0.0682488+0.000143987\ttest-mae:0.0683736+0.00061226\n",
      "[56]\ttrain-mae:0.0682284+0.000142834\ttest-mae:0.0683556+0.000612406\n",
      "[57]\ttrain-mae:0.0682136+0.000143277\ttest-mae:0.0683414+0.000611645\n",
      "[58]\ttrain-mae:0.0682006+0.000144124\ttest-mae:0.0683298+0.00060984\n",
      "[59]\ttrain-mae:0.0681906+0.000144953\ttest-mae:0.0683218+0.000611119\n",
      "[60]\ttrain-mae:0.0681786+0.000144777\ttest-mae:0.0683086+0.0006109\n",
      "[61]\ttrain-mae:0.068171+0.000147093\ttest-mae:0.068304+0.00060842\n",
      "[62]\ttrain-mae:0.068163+0.000147725\ttest-mae:0.0682958+0.000607183\n",
      "[63]\ttrain-mae:0.0681556+0.000148085\ttest-mae:0.0682882+0.000604891\n",
      "[64]\ttrain-mae:0.068151+0.000149054\ttest-mae:0.0682828+0.000604222\n",
      "[65]\ttrain-mae:0.0681456+0.000150067\ttest-mae:0.0682798+0.000601187\n",
      "[66]\ttrain-mae:0.0681392+0.000149335\ttest-mae:0.0682744+0.000601655\n",
      "[67]\ttrain-mae:0.0681334+0.000152602\ttest-mae:0.0682698+0.00060149\n",
      "[68]\ttrain-mae:0.0681268+0.000156054\ttest-mae:0.068263+0.000597466\n",
      "[69]\ttrain-mae:0.068123+0.000155349\ttest-mae:0.0682602+0.000597421\n",
      "[70]\ttrain-mae:0.0681202+0.000155005\ttest-mae:0.0682574+0.000597209\n",
      "[71]\ttrain-mae:0.0681168+0.000155746\ttest-mae:0.068256+0.000595915\n",
      "[72]\ttrain-mae:0.0681166+0.000151318\ttest-mae:0.0682556+0.00059977\n",
      "[73]\ttrain-mae:0.0681142+0.000152759\ttest-mae:0.068253+0.000599305\n",
      "[74]\ttrain-mae:0.0681108+0.000153515\ttest-mae:0.0682496+0.000597579\n",
      "[75]\ttrain-mae:0.0681088+0.000153257\ttest-mae:0.0682476+0.000597858\n",
      "[76]\ttrain-mae:0.0681056+0.00015154\ttest-mae:0.0682442+0.000598968\n",
      "[77]\ttrain-mae:0.068103+0.000155988\ttest-mae:0.068242+0.000595672\n",
      "[78]\ttrain-mae:0.0681022+0.000155036\ttest-mae:0.0682408+0.000596333\n",
      "[79]\ttrain-mae:0.0681016+0.000155482\ttest-mae:0.06824+0.000596242\n",
      "[80]\ttrain-mae:0.0680996+0.000153651\ttest-mae:0.0682378+0.000597868\n",
      "[81]\ttrain-mae:0.0680974+0.000151048\ttest-mae:0.0682382+0.000597835\n",
      "[82]\ttrain-mae:0.0680952+0.000150272\ttest-mae:0.0682376+0.000597924\n",
      "[83]\ttrain-mae:0.0680938+0.000149615\ttest-mae:0.0682362+0.000599122\n",
      "[84]\ttrain-mae:0.068089+0.000148292\ttest-mae:0.0682298+0.000601147\n",
      "[85]\ttrain-mae:0.0680902+0.000148191\ttest-mae:0.0682318+0.00060082\n",
      "[86]\ttrain-mae:0.068088+0.000146745\ttest-mae:0.0682308+0.000602381\n",
      "[87]\ttrain-mae:0.0680882+0.000146744\ttest-mae:0.0682314+0.000602087\n",
      "[88]\ttrain-mae:0.068086+0.00014761\ttest-mae:0.0682282+0.000599054\n",
      "[89]\ttrain-mae:0.0680816+0.000154783\ttest-mae:0.068224+0.000591795\n",
      "[90]\ttrain-mae:0.0680802+0.000155325\ttest-mae:0.0682232+0.000591747\n",
      "[91]\ttrain-mae:0.0680802+0.00015612\ttest-mae:0.0682244+0.000593176\n",
      "[92]\ttrain-mae:0.0680806+0.000156922\ttest-mae:0.0682252+0.000592125\n",
      "[93]\ttrain-mae:0.068083+0.000153956\ttest-mae:0.0682294+0.000598416\n",
      "[94]\ttrain-mae:0.0680818+0.000152831\ttest-mae:0.0682288+0.000598795\n",
      "[95]\ttrain-mae:0.0680814+0.000153005\ttest-mae:0.0682286+0.00059823\n",
      "[96]\ttrain-mae:0.0680816+0.000152927\ttest-mae:0.0682288+0.000598743\n",
      "[97]\ttrain-mae:0.0680828+0.000154436\ttest-mae:0.0682294+0.000597527\n",
      "[98]\ttrain-mae:0.0680808+0.000153599\ttest-mae:0.0682272+0.000598611\n",
      "[99]\ttrain-mae:0.0680798+0.000154343\ttest-mae:0.0682266+0.000598142\n",
      "[100]\ttrain-mae:0.068079+0.00015397\ttest-mae:0.0682264+0.000597863\n",
      "[101]\ttrain-mae:0.0680792+0.00015522\ttest-mae:0.06823+0.000595712\n",
      "[102]\ttrain-mae:0.0680794+0.000154356\ttest-mae:0.0682312+0.000595341\n",
      "[103]\ttrain-mae:0.068082+0.000156663\ttest-mae:0.0682344+0.00059275\n",
      "[104]\ttrain-mae:0.0680804+0.000155157\ttest-mae:0.0682334+0.00059406\n",
      "[105]\ttrain-mae:0.068081+0.000155552\ttest-mae:0.0682342+0.00059379\n",
      "[106]\ttrain-mae:0.0680816+0.000155346\ttest-mae:0.0682354+0.000593748\n",
      "[107]\ttrain-mae:0.0680824+0.000155205\ttest-mae:0.0682366+0.000594572\n",
      "[108]\ttrain-mae:0.0680838+0.000157296\ttest-mae:0.0682394+0.000592315\n",
      "[109]\ttrain-mae:0.0680812+0.000156912\ttest-mae:0.068236+0.000592693\n",
      "[110]\ttrain-mae:0.0680822+0.000155827\ttest-mae:0.0682368+0.000594042\n",
      "[111]\ttrain-mae:0.068082+0.000156347\ttest-mae:0.068237+0.000593945\n",
      "[112]\ttrain-mae:0.0680832+0.000157266\ttest-mae:0.0682384+0.000593448\n",
      "[113]\ttrain-mae:0.068083+0.000157395\ttest-mae:0.068239+0.00059178\n",
      "[114]\ttrain-mae:0.0680826+0.000156894\ttest-mae:0.0682398+0.000592777\n",
      "[115]\ttrain-mae:0.0680828+0.000157459\ttest-mae:0.0682406+0.000592686\n",
      "[116]\ttrain-mae:0.0680856+0.000156434\ttest-mae:0.0682456+0.00059148\n",
      "[117]\ttrain-mae:0.0680866+0.000156712\ttest-mae:0.0682472+0.000591468\n",
      "[118]\ttrain-mae:0.0680858+0.000155254\ttest-mae:0.0682462+0.000592971\n",
      "[119]\ttrain-mae:0.068083+0.000153075\ttest-mae:0.0682448+0.000593586\n",
      "[120]\ttrain-mae:0.0680842+0.000153197\ttest-mae:0.068246+0.000593509\n",
      "[121]\ttrain-mae:0.0680842+0.000152428\ttest-mae:0.0682466+0.000594469\n",
      "[122]\ttrain-mae:0.0680858+0.000151741\ttest-mae:0.06825+0.000593686\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[123]\ttrain-mae:0.068086+0.000153198\ttest-mae:0.06825+0.000592836\n",
      "[124]\ttrain-mae:0.0680874+0.00015439\ttest-mae:0.0682522+0.000591652\n",
      "[125]\ttrain-mae:0.06809+0.000156183\ttest-mae:0.0682544+0.000590173\n",
      "[126]\ttrain-mae:0.0680894+0.000156231\ttest-mae:0.068254+0.000589862\n",
      "[127]\ttrain-mae:0.0680884+0.000154042\ttest-mae:0.0682526+0.000590337\n",
      "[128]\ttrain-mae:0.0680866+0.000152374\ttest-mae:0.0682504+0.000591994\n",
      "[129]\ttrain-mae:0.068086+0.000152051\ttest-mae:0.0682498+0.000592196\n",
      "[130]\ttrain-mae:0.0680846+0.000153358\ttest-mae:0.0682492+0.000591701\n",
      "[131]\ttrain-mae:0.0680856+0.000154884\ttest-mae:0.0682514+0.000590309\n",
      "[132]\ttrain-mae:0.0680846+0.00015451\ttest-mae:0.0682506+0.000591076\n",
      "[133]\ttrain-mae:0.0680854+0.00015315\ttest-mae:0.0682512+0.00059292\n",
      "[134]\ttrain-mae:0.0680862+0.000151526\ttest-mae:0.0682528+0.000595244\n",
      "[135]\ttrain-mae:0.0680844+0.000154123\ttest-mae:0.0682506+0.000592189\n",
      "[136]\ttrain-mae:0.0680858+0.00015136\ttest-mae:0.068252+0.000595603\n",
      "[137]\ttrain-mae:0.0680862+0.000148872\ttest-mae:0.0682532+0.000598984\n",
      "[138]\ttrain-mae:0.0680866+0.000149426\ttest-mae:0.0682542+0.000597894\n",
      "[139]\ttrain-mae:0.0680866+0.000150094\ttest-mae:0.0682536+0.000595662\n",
      "MAE on test data 0.0671183885501\n",
      "n_estimators 91\n"
     ]
    },
    {
     "data": {
      "image/png": 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fy3GP3fPwgdxFNe/3ZYH50vdFgZVK9v0BsFBmfWXg/JrXcfl0X78xrU+ue2/k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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fc195dae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#0.05 0.0670037948915\n",
    "#0.0670416222198\n",
    "\n",
    "xgb24=XGBRegressor(learning_rate =0.1,n_estimators=1000,\n",
    "                    max_depth=2,\n",
    "                    min_child_weight=350,\n",
    "                    gamma=0.4,\n",
    "                    objective= 'reg:linear',\n",
    "                    nthread=-1,scale_pos_weight=1,seed=3192,colsample_bytree=0.9,subsample=0.95,reg_alpha=0.05)\n",
    "\n",
    "#recaliberate n_estimators with the values found above\n",
    "\n",
    "modelfit(xgb24, X_train, Y_train,X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-mae:0.449086+0.000243038\ttest-mae:0.449085+0.00105332\n",
      "[1]\ttrain-mae:0.406114+0.000226328\ttest-mae:0.406112+0.00102582\n",
      "[2]\ttrain-mae:0.367608+0.000196405\ttest-mae:0.367609+0.000994137\n",
      "[3]\ttrain-mae:0.333143+0.000188656\ttest-mae:0.333142+0.000953312\n",
      "[4]\ttrain-mae:0.30232+0.000171741\ttest-mae:0.302319+0.000934523\n",
      "[5]\ttrain-mae:0.274789+0.000169211\ttest-mae:0.274792+0.000920647\n",
      "[6]\ttrain-mae:0.250208+0.00016446\ttest-mae:0.250219+0.000930738\n",
      "[7]\ttrain-mae:0.228279+0.000164054\ttest-mae:0.228291+0.000941118\n",
      "[8]\ttrain-mae:0.208739+0.000171097\ttest-mae:0.208753+0.00096409\n",
      "[9]\ttrain-mae:0.191347+0.000171129\ttest-mae:0.191363+0.000982794\n",
      "[10]\ttrain-mae:0.175913+0.000166981\ttest-mae:0.175929+0.000990277\n",
      "[11]\ttrain-mae:0.162207+0.000171363\ttest-mae:0.162225+0.000998267\n",
      "[12]\ttrain-mae:0.150051+0.000178922\ttest-mae:0.150075+0.00100394\n",
      "[13]\ttrain-mae:0.139312+0.000180533\ttest-mae:0.139337+0.000995735\n",
      "[14]\ttrain-mae:0.129829+0.000182689\ttest-mae:0.129854+0.000978168\n",
      "[15]\ttrain-mae:0.121483+0.000187978\ttest-mae:0.121516+0.000955282\n",
      "[16]\ttrain-mae:0.114145+0.000192831\ttest-mae:0.11418+0.000930822\n",
      "[17]\ttrain-mae:0.107728+0.000198008\ttest-mae:0.107768+0.000904584\n",
      "[18]\ttrain-mae:0.102117+0.000194944\ttest-mae:0.102162+0.000891405\n",
      "[19]\ttrain-mae:0.0972434+0.000200815\ttest-mae:0.0972926+0.000860427\n",
      "[20]\ttrain-mae:0.093013+0.000188628\ttest-mae:0.0930672+0.000843433\n",
      "[21]\ttrain-mae:0.0893636+0.000187132\ttest-mae:0.0894224+0.000830574\n",
      "[22]\ttrain-mae:0.0862216+0.000182782\ttest-mae:0.0862818+0.000818864\n",
      "[23]\ttrain-mae:0.0835214+0.000172044\ttest-mae:0.0835878+0.000819544\n",
      "[24]\ttrain-mae:0.081212+0.000173302\ttest-mae:0.081286+0.000804302\n",
      "[25]\ttrain-mae:0.0792526+0.000173864\ttest-mae:0.0793318+0.000797697\n",
      "[26]\ttrain-mae:0.07758+0.000168503\ttest-mae:0.077663+0.000787842\n",
      "[27]\ttrain-mae:0.0761596+0.000161286\ttest-mae:0.0762482+0.00078782\n",
      "[28]\ttrain-mae:0.074959+0.00016164\ttest-mae:0.0750494+0.00077553\n",
      "[29]\ttrain-mae:0.0739408+0.000163819\ttest-mae:0.074041+0.000764302\n",
      "[30]\ttrain-mae:0.0730828+0.000163905\ttest-mae:0.0731858+0.000750952\n",
      "[31]\ttrain-mae:0.072349+0.000166853\ttest-mae:0.0724584+0.000737459\n",
      "[32]\ttrain-mae:0.0717366+0.000164982\ttest-mae:0.0718488+0.00072388\n",
      "[33]\ttrain-mae:0.0712168+0.000164249\ttest-mae:0.0713338+0.000718895\n",
      "[34]\ttrain-mae:0.070773+0.000161938\ttest-mae:0.0708934+0.000718592\n",
      "[35]\ttrain-mae:0.0704036+0.000161051\ttest-mae:0.0705246+0.00070742\n",
      "[36]\ttrain-mae:0.0700888+0.00016045\ttest-mae:0.0702082+0.000706064\n",
      "[37]\ttrain-mae:0.0698176+0.000155352\ttest-mae:0.0699388+0.000703565\n",
      "[38]\ttrain-mae:0.0695872+0.000157477\ttest-mae:0.069712+0.000694413\n",
      "[39]\ttrain-mae:0.069394+0.000157443\ttest-mae:0.0695208+0.000689806\n",
      "[40]\ttrain-mae:0.069232+0.000161099\ttest-mae:0.0693608+0.00068261\n",
      "[41]\ttrain-mae:0.069088+0.00016263\ttest-mae:0.069218+0.000680111\n",
      "[42]\ttrain-mae:0.068972+0.00016401\ttest-mae:0.0691038+0.000677668\n",
      "[43]\ttrain-mae:0.0688652+0.000163513\ttest-mae:0.0689972+0.000674487\n",
      "[44]\ttrain-mae:0.0687786+0.000162506\ttest-mae:0.0689086+0.000671982\n",
      "[45]\ttrain-mae:0.0687006+0.000165633\ttest-mae:0.0688348+0.000665041\n",
      "[46]\ttrain-mae:0.0686378+0.00016428\ttest-mae:0.068774+0.000663314\n",
      "[47]\ttrain-mae:0.0685764+0.000163293\ttest-mae:0.0687148+0.000663496\n",
      "[48]\ttrain-mae:0.0685254+0.000165897\ttest-mae:0.0686644+0.000659844\n",
      "[49]\ttrain-mae:0.0684786+0.000163101\ttest-mae:0.0686184+0.000661417\n",
      "[50]\ttrain-mae:0.068439+0.000164624\ttest-mae:0.068579+0.000658305\n",
      "[51]\ttrain-mae:0.0684048+0.000164503\ttest-mae:0.0685468+0.000657865\n",
      "[52]\ttrain-mae:0.0683734+0.000162099\ttest-mae:0.0685166+0.000657482\n",
      "[53]\ttrain-mae:0.0683442+0.0001627\ttest-mae:0.0684888+0.000657062\n",
      "[54]\ttrain-mae:0.0683222+0.000164379\ttest-mae:0.0684684+0.000655737\n",
      "[55]\ttrain-mae:0.0683004+0.000159851\ttest-mae:0.068447+0.000659571\n",
      "[56]\ttrain-mae:0.0682804+0.000163269\ttest-mae:0.068429+0.000656295\n",
      "[57]\ttrain-mae:0.0682616+0.000163671\ttest-mae:0.0684122+0.00065603\n",
      "[58]\ttrain-mae:0.0682472+0.000162578\ttest-mae:0.0683994+0.000654832\n",
      "[59]\ttrain-mae:0.0682334+0.000162438\ttest-mae:0.0683846+0.000655776\n",
      "[60]\ttrain-mae:0.068224+0.000162026\ttest-mae:0.0683728+0.000657097\n",
      "[61]\ttrain-mae:0.068215+0.000162416\ttest-mae:0.0683642+0.000657286\n",
      "[62]\ttrain-mae:0.0682082+0.000161763\ttest-mae:0.0683576+0.00065631\n",
      "[63]\ttrain-mae:0.068201+0.000162632\ttest-mae:0.0683526+0.000656144\n",
      "[64]\ttrain-mae:0.0681948+0.000163489\ttest-mae:0.0683464+0.000655632\n",
      "[65]\ttrain-mae:0.0681878+0.000166324\ttest-mae:0.0683416+0.000655846\n",
      "[66]\ttrain-mae:0.0681822+0.000166185\ttest-mae:0.0683362+0.000655603\n",
      "[67]\ttrain-mae:0.0681802+0.000166903\ttest-mae:0.068335+0.000655122\n",
      "[68]\ttrain-mae:0.0681744+0.000167168\ttest-mae:0.0683296+0.000654264\n",
      "[69]\ttrain-mae:0.0681718+0.000168118\ttest-mae:0.0683264+0.000653845\n",
      "[70]\ttrain-mae:0.0681676+0.00016884\ttest-mae:0.068322+0.000653477\n",
      "[71]\ttrain-mae:0.068166+0.000169852\ttest-mae:0.06832+0.000652467\n",
      "[72]\ttrain-mae:0.0681634+0.000170245\ttest-mae:0.0683182+0.000653294\n",
      "[73]\ttrain-mae:0.068159+0.000168669\ttest-mae:0.0683156+0.000658247\n",
      "[74]\ttrain-mae:0.0681582+0.000169058\ttest-mae:0.0683146+0.000657734\n",
      "[75]\ttrain-mae:0.0681546+0.000168228\ttest-mae:0.0683122+0.000658456\n",
      "[76]\ttrain-mae:0.0681514+0.000171558\ttest-mae:0.068309+0.000654549\n",
      "[77]\ttrain-mae:0.0681492+0.000171234\ttest-mae:0.0683086+0.000654649\n",
      "[78]\ttrain-mae:0.0681484+0.00016958\ttest-mae:0.0683096+0.000655607\n",
      "[79]\ttrain-mae:0.0681448+0.000168056\ttest-mae:0.068308+0.00065583\n",
      "[80]\ttrain-mae:0.0681436+0.000169934\ttest-mae:0.0683062+0.00065362\n",
      "[81]\ttrain-mae:0.0681434+0.000170704\ttest-mae:0.0683054+0.000653627\n",
      "[82]\ttrain-mae:0.0681434+0.000170554\ttest-mae:0.0683054+0.000653172\n",
      "[83]\ttrain-mae:0.0681434+0.000170543\ttest-mae:0.0683058+0.000652692\n",
      "[84]\ttrain-mae:0.0681446+0.000168303\ttest-mae:0.068307+0.000655526\n",
      "[85]\ttrain-mae:0.0681428+0.000167274\ttest-mae:0.0683052+0.000656447\n",
      "[86]\ttrain-mae:0.0681418+0.000166631\ttest-mae:0.0683076+0.000655836\n",
      "[87]\ttrain-mae:0.068141+0.000167827\ttest-mae:0.068307+0.000655139\n",
      "[88]\ttrain-mae:0.06814+0.000167837\ttest-mae:0.0683076+0.000655031\n",
      "[89]\ttrain-mae:0.06814+0.00016936\ttest-mae:0.0683074+0.000654012\n",
      "[90]\ttrain-mae:0.0681392+0.000169366\ttest-mae:0.0683066+0.000653895\n",
      "[91]\ttrain-mae:0.068138+0.000169712\ttest-mae:0.0683052+0.000652648\n",
      "[92]\ttrain-mae:0.0681378+0.000169308\ttest-mae:0.0683052+0.000653323\n",
      "[93]\ttrain-mae:0.0681392+0.000168928\ttest-mae:0.068307+0.000652793\n",
      "[94]\ttrain-mae:0.0681378+0.000169233\ttest-mae:0.0683058+0.000652\n",
      "[95]\ttrain-mae:0.0681376+0.000169345\ttest-mae:0.0683082+0.00065155\n",
      "[96]\ttrain-mae:0.0681378+0.000169324\ttest-mae:0.0683092+0.000650986\n",
      "[97]\ttrain-mae:0.0681374+0.000170638\ttest-mae:0.0683094+0.00064985\n",
      "[98]\ttrain-mae:0.068136+0.000169855\ttest-mae:0.0683074+0.000649979\n",
      "[99]\ttrain-mae:0.068135+0.000169609\ttest-mae:0.0683066+0.000649985\n",
      "[100]\ttrain-mae:0.0681322+0.000171879\ttest-mae:0.068305+0.000647645\n",
      "[101]\ttrain-mae:0.0681314+0.000173153\ttest-mae:0.0683046+0.000646781\n",
      "[102]\ttrain-mae:0.0681282+0.000173037\ttest-mae:0.0683026+0.000647276\n",
      "[103]\ttrain-mae:0.068129+0.000175235\ttest-mae:0.0683028+0.000645131\n",
      "[104]\ttrain-mae:0.0681292+0.000174852\ttest-mae:0.0683032+0.000645931\n",
      "[105]\ttrain-mae:0.0681294+0.000173768\ttest-mae:0.0683032+0.000646435\n",
      "[106]\ttrain-mae:0.0681304+0.000173781\ttest-mae:0.0683036+0.00064665\n",
      "[107]\ttrain-mae:0.068131+0.000174935\ttest-mae:0.068305+0.000645319\n",
      "[108]\ttrain-mae:0.068131+0.000174447\ttest-mae:0.068305+0.000646338\n",
      "[109]\ttrain-mae:0.0681312+0.000172553\ttest-mae:0.0683052+0.000648274\n",
      "[110]\ttrain-mae:0.0681294+0.000173165\ttest-mae:0.0683036+0.000648104\n",
      "[111]\ttrain-mae:0.0681288+0.000171815\ttest-mae:0.0683052+0.000647244\n",
      "[112]\ttrain-mae:0.0681286+0.00017147\ttest-mae:0.0683052+0.00064712\n",
      "[113]\ttrain-mae:0.068129+0.000172065\ttest-mae:0.0683056+0.000646002\n",
      "[114]\ttrain-mae:0.0681298+0.000173067\ttest-mae:0.068306+0.000644418\n",
      "[115]\ttrain-mae:0.06813+0.00017301\ttest-mae:0.0683062+0.000644367\n",
      "[116]\ttrain-mae:0.0681296+0.000171941\ttest-mae:0.068306+0.000645572\n",
      "[117]\ttrain-mae:0.0681294+0.000171705\ttest-mae:0.0683062+0.000645836\n",
      "[118]\ttrain-mae:0.0681282+0.000171545\ttest-mae:0.068305+0.000645506\n",
      "[119]\ttrain-mae:0.0681284+0.000173079\ttest-mae:0.0683062+0.000643857\n",
      "[120]\ttrain-mae:0.068128+0.000171108\ttest-mae:0.068306+0.000645897\n",
      "[121]\ttrain-mae:0.0681274+0.000171614\ttest-mae:0.068306+0.000644369\n",
      "[122]\ttrain-mae:0.068127+0.00017136\ttest-mae:0.0683052+0.000644879\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[123]\ttrain-mae:0.068128+0.000171834\ttest-mae:0.0683078+0.000643714\n",
      "[124]\ttrain-mae:0.0681284+0.000171603\ttest-mae:0.0683078+0.000644606\n",
      "[125]\ttrain-mae:0.06813+0.000169296\ttest-mae:0.0683106+0.000646072\n",
      "[126]\ttrain-mae:0.0681306+0.000169591\ttest-mae:0.068311+0.0006459\n",
      "[127]\ttrain-mae:0.0681298+0.000170373\ttest-mae:0.0683104+0.00064516\n",
      "[128]\ttrain-mae:0.0681292+0.000171264\ttest-mae:0.0683098+0.000644416\n",
      "[129]\ttrain-mae:0.068129+0.000170609\ttest-mae:0.0683102+0.000644699\n",
      "[130]\ttrain-mae:0.0681294+0.000170668\ttest-mae:0.0683104+0.000644597\n",
      "[131]\ttrain-mae:0.0681312+0.000170706\ttest-mae:0.0683132+0.000643615\n",
      "[132]\ttrain-mae:0.068131+0.000170262\ttest-mae:0.0683128+0.000643855\n",
      "[133]\ttrain-mae:0.0681308+0.000169301\ttest-mae:0.0683126+0.000644933\n",
      "[134]\ttrain-mae:0.0681302+0.000168771\ttest-mae:0.0683118+0.000645839\n",
      "[135]\ttrain-mae:0.0681302+0.00016897\ttest-mae:0.0683132+0.000644936\n",
      "[136]\ttrain-mae:0.068131+0.000168879\ttest-mae:0.0683136+0.000645796\n",
      "[137]\ttrain-mae:0.0681314+0.000168513\ttest-mae:0.0683134+0.000645536\n",
      "[138]\ttrain-mae:0.068132+0.000168984\ttest-mae:0.0683154+0.000644373\n",
      "[139]\ttrain-mae:0.0681326+0.000170561\ttest-mae:0.0683158+0.000643118\n",
      "[140]\ttrain-mae:0.068133+0.00017035\ttest-mae:0.0683166+0.00064279\n",
      "[141]\ttrain-mae:0.0681326+0.00016952\ttest-mae:0.0683162+0.000643598\n",
      "[142]\ttrain-mae:0.0681316+0.000169907\ttest-mae:0.0683154+0.000642589\n",
      "[143]\ttrain-mae:0.0681298+0.00016856\ttest-mae:0.068314+0.000644226\n",
      "[144]\ttrain-mae:0.0681292+0.00016757\ttest-mae:0.0683136+0.000645714\n",
      "[145]\ttrain-mae:0.0681282+0.000167973\ttest-mae:0.0683124+0.000645706\n",
      "[146]\ttrain-mae:0.0681256+0.000169917\ttest-mae:0.0683108+0.000642433\n",
      "[147]\ttrain-mae:0.0681256+0.000169917\ttest-mae:0.0683106+0.000642475\n",
      "[148]\ttrain-mae:0.068126+0.000168888\ttest-mae:0.068311+0.000643325\n",
      "[149]\ttrain-mae:0.0681256+0.000168759\ttest-mae:0.0683112+0.000643153\n",
      "[150]\ttrain-mae:0.0681256+0.000168297\ttest-mae:0.0683108+0.000643513\n",
      "[151]\ttrain-mae:0.0681242+0.000167626\ttest-mae:0.0683092+0.000644249\n",
      "MAE on test data 0.067239903321\n",
      "n_estimators 103\n"
     ]
    },
    {
     "data": {
      "image/png": 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G9Yj077SYZFfbCyRtAMySdJvtq1vMazowHWDKlCmjHWpERBQdE4akFwFHAK8EXjRUbvvw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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fbbabcaef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pcaX_train, pcaX_test, pcaY_train, pcaY_test = train_test_split(pca1, Y, test_size=0.10, random_state=3192)\n",
    "\n",
    "\n",
    "#0.05 0.0670037948915\n",
    "#0.0670416222198\n",
    "\n",
    "xgb24=XGBRegressor(learning_rate =0.1,n_estimators=1000,\n",
    "                    max_depth=2,\n",
    "                    min_child_weight=350,\n",
    "                    gamma=0.4,\n",
    "                    objective= 'reg:linear',\n",
    "                    nthread=-1,scale_pos_weight=1,seed=3192,colsample_bytree=0.9,subsample=0.95,reg_alpha=0.05)\n",
    "\n",
    "#recaliberate n_estimators with the values found above\n",
    "\n",
    "modelfit(xgb24, pcaX_train,pcaY_train,pcaX_test,pcaY_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import r2_score,mean_absolute_error,make_scorer,mean_squared_error\n",
    "\n",
    "\n",
    "regr = RandomForestRegressor(n_jobs=-1, random_state=30, verbose=2)\n",
    "parameters = { \"n_estimators\": [50],\"criterion\":[\"mae\"]}\n",
    "\n",
    "\n",
    "grid = GridSearchCV(regr,param_grid=parameters,cv=5,refit='MAE',verbose=0,n_jobs=-1,pre_dispatch='n_jobs')\n",
    "\n",
    "grid.fit(X_train[xgboost_selection], Y_train)\n",
    "print(\"5. grid best_score_\",abs(grid.best_score_))\n",
    "Y_pred = grid.predict(X_test[xgboost_selection])\n",
    "print(\"MAE on test data\",mean_absolute_error(Y_test,Y_pred))\n",
    "print(\"MSE on test data\",mean_squared_error(Y_test,Y_pred))\n",
    "print(\"R Squared data \",r2_score(Y_test,Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4JOecc84515A11N2fnjKzKuB1SVsVeH4icKWk7YAnzOyd+Ll5rplNj3WmAl0V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865StYIk6qbFe+paL7GAgcQeigejbeTJHUDkLRh3CWxJ/BJbFAMImz+V5ejMv8/F4/nAkPj8SFA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\nIiJiVjKoiIiIiIiIWfl/FRO7XDp7jvUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1da814e52e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lets start by plotting a heatmap to determine if any variables are correlated\n",
    "import seaborn as sns\n",
    "\n",
    "numcols.remove('threequarterbathnbr')\n",
    "\n",
    "numcols.remove('parcelid')\n",
    "plt.figure(figsize = (12,8))\n",
    "sns.heatmap(data=prop2016[numcols].corr())\n",
    "plt.show()\n",
    "plt.gcf().clear()\n",
    "\n",
    "#unitcnt 996290\n",
    "#regionidneighborhood 1817378\n",
    "#buildingqualitytypeid 1035292\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1da7e32c0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#from pandas.tools.plotting import scatter_matrix\n",
    "#scatter_matrix(df_train[['buildingqualitytypeid','yearbuilt']])\n",
    "#plt.show()\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(prop2016.latitude, prop2016.longitude, 'ro')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  },
  "varInspector": {
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    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
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    "builtin_function_or_method",
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    "_Feature"
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   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}