Step 2a-Approach3.ipynb

{
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    {
     "name": "stderr",
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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",
    "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",
    "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approach 3\n",
    "\n",
    "-  In this approach the properties data (27 million records) and transaction data (90k records) are merged together before imputing any missing values \n",
    "\n",
    "-  Apart from that, this approach also add new attributes using feature engineering techniques "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Data\n",
    "-  Importing properties_2016 which has data about 27 million unique house properties with 58 attributes\n",
    "\n",
    "-  Importing transaction data which has 90k records of the properties sold in the year 2016\n",
    "\n",
    "-  Merging the data sets based on property_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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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",
    "prop2016= pd.read_csv('C:\\\\Users\\\\Dell\\\\Desktop\\\\inf552\\\\Zillow Prize\\\\properties_2016.csv')\n",
    "##import train 2016  data\n",
    "train2016=pd.read_csv('C:\\\\Users\\\\Dell\\\\Desktop\\\\inf552\\\\Zillow Prize\\\\train_2016.csv',parse_dates=[\"transactiondate\"])\n",
    "##merge\n",
    "df_train = train2016.merge(prop2016, how='left', on='parcelid')\n",
    "df_train['transaction_month'] = df_train['transactiondate'].dt.month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  Renaming attributes names to be meaningful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "code_folding": [
     7
    ]
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   "outputs": [],
   "source": [
    "df_train=df_train.rename(index=str, columns={\"pooltypeid7\": \"pool_with_spa_tub_no\", \n",
    "                                             \"pooltypeid2\": \"pool_with_spa_tub_yes\"})\n",
    "\n",
    "df_train=df_train.rename(index=str, columns={\"hashottuborspa\": \"has_hottub_or_spa\", \n",
    "                                             \"pooltypeid10\": \"just_hottub_or_spa\"})\n",
    "\n",
    "df_train=df_train.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",
    "df_train=df_train.rename(index=str, columns={\"decktypeid\": \"deck_flag\"})\n",
    "\n",
    "df_train=df_train.rename(index=str, columns={\"yardbuildingsqft17\": \"patio_sqft\",\n",
    "                                             \"yardbuildingsqft26\":\"storage_sqft\"})\n",
    "\n",
    "df_train=df_train.rename(index=str, columns={\"bathroomcnt\": \"total_bath\",\n",
    "                                             \"fullbathcnt\": \"full_bath\",\n",
    "                                             \"threequarterbathnbr\": \"half_bath\"})\n",
    "\n",
    "df_train=df_train.rename(index=str, columns={\"storytypeid\": \"basement_flag\"})\n",
    "\n",
    "df_train=df_train.rename(index=str, columns={\"poolsizesum\": \"pool_sqft\"})         "
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.loc[df_train.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": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.loc[df_train.pool_with_spa_tub_no.isnull(),'pool_with_spa_tub_no']=0\n",
    "df_train.loc[df_train.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": 42,
   "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",
    "df_train.loc[ (df_train.poolcnt==1) & (df_train.has_hottub_or_spa==1) & (df_train.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",
    "df_train.loc[ (df_train.has_hottub_or_spa.isnull()) & (df_train.just_hottub_or_spa.isnull()),['has_hottub_or_spa','just_hottub_or_spa']]=0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  When there is no pool, make poolsize as zero instead of Nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16932\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df_train.loc[ df_train.poolcnt==0,'pool_sqft']=0\n",
    "print(df_train.pool_sqft.isnull().sum())"
   ]
  },
  {
   "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": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# basement_flag only has value 7(basement) and only for that basementsqft has value\n",
    "\n",
    "df_train.loc[df_train.basement_flag.isnull(),'basementsqft']=0\n",
    "df_train.loc[df_train.basement_flag.isnull(),'basement_flag']=0\n",
    "df_train.loc[df_train.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": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 222\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#df_train.fireplaceflag.isnull().sum()\n",
    "#df_train.fireplace_count.isnull().sum()\n",
    "df_train.loc[(df_train.fireplace_count.isnull()) & (df_train.fireplaceflag.isnull()),'fireplaceflag'] = False\n",
    "df_train.loc[(df_train.fireplace_count.isnull()) & (df_train.fireplaceflag==False),'fireplace_count'] = 0\n",
    "df_train.loc[df_train['fireplace_count']>0,'fireplaceflag']= True\n",
    "\n",
    "\n",
    "print(\"after\",df_train.fireplace_count.isnull().sum())\n",
    "#print(\"after\",df_train.fireplace_count.value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train=df_train.drop('transactiondate',axis=1)"
   ]
  },
  {
   "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": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 0\n",
      "after 8920\n"
     ]
    }
   ],
   "source": [
    "df_train.loc[df_train.garage_sqft.isnull() & df_train.garagecarcnt.isnull(),['garagecarcnt','garage_sqft']]=0\n",
    "df_train.loc[(df_train.garagecarcnt>0) & (df_train.garage_sqft==0),'garage_sqft']=np.nan\n",
    "print(\"after\",df_train.garagecarcnt.isnull().sum())\n",
    "#print(\"after\",df_train.garagecarcnt.value_counts())\n",
    "print(\"after\",df_train.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": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1165\n",
      "1182\n",
      "1182\n"
     ]
    }
   ],
   "source": [
    "#total_bath & calculatedbathnbr are duplicates , and calculatedbathnbr has more nulls, hence drop it\n",
    "\n",
    "df_train=df_train.drop('calculatedbathnbr',axis=1)\n",
    "\n",
    "# full_bath is null & half_bath is null & total_bath=0 (missing values)\n",
    "df_train.loc[(df_train.full_bath.isnull()) & (df_train.half_bath.isnull()) & (df_train.total_bath==0),'total_bath']=np.nan\n",
    "\n",
    "\n",
    "# when full_bath=total_bath, half_bath=0 \n",
    "\n",
    "df_train.loc[(df_train.full_bath==df_train.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(df_train.total_bath.isnull().sum())\n",
    "print(df_train.half_bath.isnull().sum())\n",
    "print(df_train.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": 49,
   "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",
    "df_train.loc[df_train.patio_sqft.isnull() ,'patio_sqft']=0\n",
    "df_train.loc[df_train.storage_sqft.isnull() ,'storage_sqft']=0\n",
    "print(df_train.patio_sqft.isnull().sum())\n",
    "print(df_train.storage_sqft.isnull().sum())"
   ]
  },
  {
   "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": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "LA               58574\n",
      "Orange_County    24505\n",
      "Ventura           7196\n",
      "Name: fips, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df_train.loc[df_train.fips==6037 ,'fips']=\"LA\"\n",
    "df_train.loc[df_train.fips==6059 ,'fips']=\"Orange_County\"\n",
    "df_train.loc[df_train.fips==6111 ,'fips']=\"Ventura\"\n",
    "print(df_train.fips.isnull().sum())\n",
    "print(df_train.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": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['latitude']=df_train['latitude'].divide(100000)\n",
    "df_train['longitude']=df_train['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": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "df_train.loc[df_train.deck_flag==66 ,'deck_flag']=1\n",
    "df_train.loc[df_train.deck_flag.isnull() ,'deck_flag']=0\n",
    "print(df_train.deck_flag.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  Imputing unit count based on property land type (Mode Imputation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69705\n",
      "69700\n",
      "31922\n",
      "89976\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "#numberofstories & unitcnt &roomcnt\n",
    "\n",
    "df_train.loc[df_train.roomcnt==0 ,'roomcnt']=np.nan\n",
    "\n",
    "print(df_train.numberofstories.isnull().sum())\n",
    "print(df_train.roomcnt.isnull().sum())\n",
    "print(df_train.unitcnt.isnull().sum())\n",
    "\n",
    "# propertylandusetypeid  and unitcnt is related \n",
    "\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",
    "\n",
    "#31 - Commercial/Office/Residential Mixed Used\n",
    "#47 -Store/Office (Mixed Use)\n",
    "#265 -Cluster Home\n",
    "\n",
    "df_train.loc[(df_train.propertylandusetypeid==31) & (df_train.unitcnt.isnull()),'unitcnt']=2\n",
    "df_train.loc[(df_train.propertylandusetypeid==47) & (df_train.unitcnt.isnull()),'unitcnt']=2\n",
    "df_train.loc[(df_train.propertylandusetypeid==246) & (df_train.unitcnt.isnull()),'unitcnt']=2\n",
    "df_train.loc[(df_train.propertylandusetypeid==247) & (df_train.unitcnt.isnull()),'unitcnt']=3\n",
    "df_train.loc[(df_train.propertylandusetypeid==248) & (df_train.unitcnt.isnull()),'unitcnt']=4\n",
    "df_train.loc[(df_train.propertylandusetypeid==260) & (df_train.unitcnt.isnull()),'unitcnt']=1\n",
    "df_train.loc[(df_train.propertylandusetypeid==261) & (df_train.unitcnt.isnull()),'unitcnt']=1\n",
    "df_train.loc[(df_train.propertylandusetypeid==263) & (df_train.unitcnt.isnull()),'unitcnt']=1\n",
    "df_train.loc[(df_train.propertylandusetypeid==264) & (df_train.unitcnt.isnull()),'unitcnt']=1\n",
    "df_train.loc[(df_train.propertylandusetypeid==266) & (df_train.unitcnt.isnull()),'unitcnt']=1\n",
    "df_train.loc[(df_train.propertylandusetypeid==267) & (df_train.unitcnt.isnull()),'unitcnt']=1\n",
    "df_train.loc[(df_train.propertylandusetypeid==269) & (df_train.unitcnt.isnull()),'unitcnt']=1\n",
    "df_train.loc[(df_train.propertylandusetypeid==275) & (df_train.unitcnt.isnull()),'unitcnt']=1\n",
    "#df_train.loc[(df_train.propertylandusetypeid==265) & (df_train.unitcnt.isnull()),'unitcnt']=1\n",
    "\n",
    "\n",
    "#typeconstructiontypeid (based on location and year of building)\n",
    "print(df_train.typeconstructiontypeid.isnull().sum())\n",
    "print(df_train.propertylandusetypeid.isnull().sum())\n"
   ]
  },
  {
   "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": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_train=df_train.drop('preimeter_living_area_sqft', axis=1)\n",
    "df_train=df_train.drop('total_area_sqft', axis=1)\n",
    "df_train=df_train.drop('finished_living_area_sqft', axis=1)\n",
    "df_train=df_train.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": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#propertycountylandusecode\n",
    "#print(df_train.propertycountylandusecode.isnull().sum())\n",
    "df_train=df_train.drop(['propertycountylandusecode'], axis=1)\n",
    "\n",
    "#propertyzoningdesc, same reason as above applies to this column as well\n",
    "\n",
    "df_train=df_train.drop(['propertyzoningdesc'], axis=1)\n",
    "#print(df_train.propertyzoningdesc.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  assessmentyear has a constant value for all the rows's, Let's drop it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.assessmentyear.value_counts()\n",
    "df_train=df_train.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": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#taxdelinquencyyear\n",
    "#taxdelinquencyflag\n",
    "df_train.loc[df_train.taxdelinquencyflag.isnull(),'taxdelinquencyflag'] = \"No\"\n",
    "df_train.loc[df_train.taxdelinquencyflag=='Y','taxdelinquencyflag'] = \"Yes\"\n",
    "df_train.loc[df_train.taxdelinquencyflag=='No','taxdelinquencyyear'] = 0\n",
    "#print(df_train.taxdelinquencyyear.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  Convert yy to yyyy format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.loc[df_train.taxdelinquencyyear==99,'taxdelinquencyyear'] = 1999\n",
    "df_train.loc[df_train.taxdelinquencyyear==6,'taxdelinquencyyear'] = 2006\n",
    "df_train.loc[df_train.taxdelinquencyyear==7,'taxdelinquencyyear'] = 2007\n",
    "df_train.loc[df_train.taxdelinquencyyear==8,'taxdelinquencyyear'] = 2008\n",
    "df_train.loc[df_train.taxdelinquencyyear==9,'taxdelinquencyyear'] = 2009\n",
    "df_train.loc[df_train.taxdelinquencyyear==10,'taxdelinquencyyear'] = 2010\n",
    "df_train.loc[df_train.taxdelinquencyyear==11,'taxdelinquencyyear'] = 2011\n",
    "df_train.loc[df_train.taxdelinquencyyear==12,'taxdelinquencyyear'] = 2012\n",
    "df_train.loc[df_train.taxdelinquencyyear==13,'taxdelinquencyyear'] = 2013\n",
    "df_train.loc[df_train.taxdelinquencyyear==14,'taxdelinquencyyear'] = 2014\n",
    "df_train.loc[df_train.taxdelinquencyyear==15,'taxdelinquencyyear'] = 2015\n",
    "#print(df_train.taxdelinquencyyear.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.loc[df_train.taxdelinquencyyear>0,'taxdelinquencyyear'] = 2016 - df_train['taxdelinquencyyear']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  rawcensustractandblock has 3 fields concatened together, let's do string split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# both are float columns now\n",
    "#rawcensustractandblock\n",
    "s_rawcensustractandblock=df_train.rawcensustractandblock.apply(lambda x: str(x))\n",
    "\n",
    "df_train['census_tractnumber']=s_rawcensustractandblock.str.slice(4,11)\n",
    "df_train['block_number']=s_rawcensustractandblock.str.slice(start=11)\n",
    "df_train['block_number']=df_train['block_number'].apply(lambda x: x[:4]+'.'+x[4:]+'0' )\n",
    "df_train['block_number']=df_train['block_number'].apply(lambda x: int(round(float(x),0)) )\n",
    "df_train['block_number']=df_train['block_number'].apply(lambda x: str(x).ljust(4,'0') )\n",
    "\n",
    "\n",
    "\n",
    "#droping censustractandblock since this is just a duplicate of rawcensustractandblock\n",
    "df_train=df_train.drop('censustractandblock', axis=1)\n",
    "\n",
    "# drooping rawcensustractandblock, since it's already stored as substrings in different column names\n",
    "df_train=df_train.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": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['buildingclasstypeid', 'architecturalstyletypeid', 'typeconstructiontypeid']\n"
     ]
    }
   ],
   "source": [
    "missingvalues_prop = (df_train.isnull().sum()/len(df_train)).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": 62,
   "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",
    "\n",
    "df_train = df_train.drop(['buildingclasstypeid', 'architecturalstyletypeid', 'typeconstructiontypeid'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### time to deal with missing values\n",
    "    -  unitcnt            360 missing rows\n",
    "- it seems most missing values from unitcnt are from propertylandusetypeid=265, which is Cluster Home, google search shows they are group of houses with shared walls __[link nytimes](http://www.nytimes.com/1983/06/05/realestate/patio-homes-gain-in-cluster-complexes.html)__ says the cluster is anywhere between 5 to 25 units, asssume 10 as reassonable count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "df_train.loc[df_train.propertylandusetypeid==265,'unitcnt']=10\n",
    "print(df_train.propertylandusetypeid.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    -  pool_sqft  16932 missing values\n",
    "-  Perform mean imputation for pool_sqft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "poolsizesum_mean = df_train.loc[df_train['poolcnt'] > 0, 'pool_sqft'].mean()\n",
    "\n",
    "df_train.loc[df_train.pool_sqft.isnull(),'pool_sqft']=poolsizesum_mean\n",
    "print(df_train.pool_sqft.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  Recalculate \n",
    "    -  total_parcel_tax =structure_tax + land_tax\n",
    "    -  structure_tax=total_parcel_tax -land_tax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "380\n",
      "1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "df_train.loc[(df_train.total_parcel_tax>0) & (df_train.land_tax>0),'structure_tax']=df_train['total_parcel_tax']-df_train['land_tax']\n",
    "\n",
    "#structure_tax, i see a lot of structure tax is 0's, those must be NA's\n",
    "\n",
    "df_train.loc[df_train.structure_tax==0,'structure_tax']=np.nan\n",
    "\n",
    "print(df_train.total_property_tax_2016.isnull().sum())\n",
    "print(df_train.structure_tax.isnull().sum())\n",
    "print(df_train.total_parcel_tax.isnull().sum())\n",
    "print(df_train.land_tax.isnull().sum())\n",
    "\n",
    "#total_parcel_tax\n",
    "#structure_tax\n",
    "#land_tax\n",
    "#total_property_tax_2016\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90275, 48)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# regionidcounty is exact copy of fips code, dropping the dulicate column\n",
    "\n",
    "df_train = df_train.drop(['regionidcounty'], axis=1)\n",
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1421\n"
     ]
    }
   ],
   "source": [
    "#bedroomcnt #1421 zero bed room houses ??, observed it's missing all other room count also missing\n",
    "\n",
    "df_train.loc[df_train.bedroomcnt==0,'bedroomcnt']= np.nan\n",
    "print(df_train.bedroomcnt.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1165\n",
      "1182\n",
      "1182\n",
      "1421\n",
      "1416\n"
     ]
    }
   ],
   "source": [
    "# propertylandusetypeid & total living area\n",
    "#                              total_bath           1165\n",
    "#                              full_bath           1182\n",
    "#                              half_bath           1182\n",
    "#                                bedroomcnt      1421\n",
    "#                              roomcnt           1416\n",
    "\n",
    "\n",
    "# roomcnt=(full_bath+half_bath)+ bedroomcnt\n",
    "# total_bath=fullbath+ 0.5(half_bath)\n",
    "\n",
    "#caluculate full bath and half bath again from total bath as, it has few extra columns, (fixes 500 missing values in roomcnt )\n",
    "\n",
    "df_train.loc[(df_train.full_bath.notnull()) \n",
    "             & (df_train.half_bath.notnull()) \n",
    "             & (df_train.bedroomcnt.notnull()) \n",
    "             & (df_train.roomcnt.isnull()),['roomcnt']]=df_train.full_bath + df_train.half_bath + df_train.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(df_train.total_bath.isnull().sum())\n",
    "print(df_train.full_bath.isnull().sum())\n",
    "print(df_train.half_bath.isnull().sum())\n",
    "print(df_train.bedroomcnt.isnull().sum())\n",
    "print(df_train.roomcnt.isnull().sum())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "#numberofstories\t69705\n",
    "df_train.loc[(df_train.propertylandusetypeid==246) & (df_train.numberofstories.notnull()),'numberofstories'].mode()\n",
    "df_train.loc[(df_train.propertylandusetypeid==246) & (df_train.numberofstories.isnull()),'numberofstories']=2\n",
    "df_train.loc[(df_train.propertylandusetypeid==247) & (df_train.numberofstories.notnull()),'numberofstories'].mode()\n",
    "df_train.loc[(df_train.propertylandusetypeid==247) & (df_train.numberofstories.isnull()),'numberofstories']=2\n",
    "df_train.loc[(df_train.propertylandusetypeid==248) & (df_train.numberofstories.notnull()),'numberofstories'].mode()\n",
    "df_train.loc[(df_train.propertylandusetypeid==248) & (df_train.numberofstories.isnull()),'numberofstories']=2\n",
    "df_train.loc[(df_train.propertylandusetypeid==260) & (df_train.numberofstories.notnull()),'numberofstories'].mode()\n",
    "df_train.loc[(df_train.propertylandusetypeid==260) & (df_train.numberofstories.isnull()),'numberofstories']=2\n",
    "df_train.loc[(df_train.propertylandusetypeid==261) & (df_train.numberofstories.notnull()),'numberofstories'].mode()\n",
    "df_train.loc[(df_train.propertylandusetypeid==261) & (df_train.numberofstories.isnull()),'numberofstories']=1\n",
    "df_train.loc[(df_train.propertylandusetypeid==263) & (df_train.numberofstories.notnull()),'numberofstories'].mode()\n",
    "df_train.loc[(df_train.propertylandusetypeid==263) & (df_train.numberofstories.isnull()),'numberofstories']=1\n",
    "df_train.loc[(df_train.propertylandusetypeid==266) & (df_train.numberofstories.notnull()),'numberofstories'].mode()\n",
    "df_train.loc[(df_train.propertylandusetypeid==266) & (df_train.numberofstories.isnull()),'numberofstories']=1\n",
    "df_train.loc[(df_train.propertylandusetypeid==269) & (df_train.numberofstories.notnull()),'numberofstories'].mode()\n",
    "df_train.loc[(df_train.propertylandusetypeid==269) & (df_train.numberofstories.isnull()),'numberofstories']=2\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==275) & (prop2016.numberofstories.notnull()),'numberofstories'].mode()\n",
    "df_train.loc[(df_train.propertylandusetypeid==275) & (df_train.numberofstories.isnull()),'numberofstories']=1\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==267) & (prop2016.numberofstories.notnull()),'numberofstories'].mode()\n",
    "df_train.loc[(df_train.propertylandusetypeid==267) & (df_train.numberofstories.isnull()),'numberofstories']=1\n",
    "#https://en.wikipedia.org/wiki/Townhouse , typical town house are usualyy large, and has atleast 6 rooms\n",
    "df_train.loc[(df_train.propertylandusetypeid==264) & (df_train.numberofstories.isnull()),'numberofstories']=2\n",
    "prop2016.loc[(prop2016.propertylandusetypeid==31) & (prop2016.numberofstories.notnull()),'numberofstories'].mode()\n",
    "df_train.loc[(df_train.propertylandusetypeid==31) & (df_train.numberofstories.isnull()),'numberofstories']=2\n",
    "print(df_train.numberofstories.isnull().sum()) \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fireplace_count\n",
    "df_train.loc[(df_train.fireplaceflag==1) & (df_train.fireplace_count.notnull()),'fireplace_count'].mode()\n",
    "df_train.loc[(df_train.fireplaceflag==1) & (df_train.fireplace_count.isnull()),'fireplace_count']=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sXLgQGRkZRtc1EFHpeCCcXjmFu6x27NiB6OhopcshqlQYGvTKiY2Nxfbt2xEQEIDOnTsr\nXQ5RpcLdU0REJBsPhBMRkWwMDSIiku2lP6Zx506O0iUQEVU6tWtri23nlgYREclWZlsaBQUF+PDD\nD5GRkQFTU1NERkZCCIGgoCCoVCrY2tpi3rx5MDExQXR0NI4ePQq1Wo2QkBA4OjriypUrsvsSEVH5\nKLPQOHLkCIA/b26XkJAghcb06dPh6uqK0NBQxMbGwtraGomJidi1axdu3bqFKVOmYM+ePYiMjJTd\nl4iIykeZhUb37t2lWyjfvHkTtWrVwtGjR9GhQwcAgLu7O44fPw4bGxu4ublBpVLB2toaBQUFyMrK\nQmpqquy+VlZWZbUaRET0hDI9EK5WqzF79mx8//33WLFiBY4cOSLdmM7c3Bw5OTnQ6XSoWbOmNE9h\nuxBCdt/SQsPSsgbUatMyWkMioldLmZ89tXDhQsycORODBw/G48ePpXa9Xg8LCwtoNBqjWxzr9Xpo\ntVqjG9g9q29psrMfvsC1ISJ6NZT72VN79+7F2rVrAfx5u2uVSoVWrVohISEBABAXFwcXFxc4Ozsj\nPj4eBoMBN2/ehMFggJWVFRwcHGT3JSKi8lFmtxF5+PAhgoODcffuXeTn52Ps2LFo2rQp5s6di7y8\nPDRp0gTz58+HqakpoqKiEBcXB4PBgODgYLi4uCAjI0N239LwOg0ioudX0pbGS3/vKYYGEdHzKyk0\nXvorwokAYMTxMKVLwKbOytdA9HfxinAiIpKNoUFERLIxNIiISDaGBhERycbQICIi2RgaREQkG0OD\niIhkY2gQEZFsDA0iIpKNoUFERLIxNIiISDaGBhERycbQICIi2RgaREQkG0ODiIhkY2gQEZFsDA0i\nIpKNoUFERLIxNIiISDaGBhERycbQICIi2RgaREQkG0ODiIhkY2gQEZFsDA0iIpKNoUFERLIxNIiI\nSDZ1WTxpXl4eQkJCcOPGDeTm5mLChAmoV68exo8fj8aNGwMAhg4dij59+iA6OhpHjx6FWq1GSEgI\nHB0dceXKFQQFBUGlUsHW1hbz5s2DiYlJsX2JiKj8lElo7Nu3DzVr1sTixYuRnZ0Nb29vTJo0CaNG\njcLo0aOlfqmpqUhMTMSuXbtw69YtTJkyBXv27EFkZCSmT58OV1dXhIaGIjY2FtbW1sX2JSKi8lMm\nodGrVy94eXlJ06ampkhJSUFGRgZiY2PRqFEjhISEICkpCW5ublCpVLC2tkZBQQGysrKQmpqKDh06\nAADc3d1x/Phx2NjYFNvXysqqLFaBiIiKUSahYW5uDgDQ6XSYOnUqpk+fjtzcXPj5+aFVq1ZYvXo1\nVq5cCa1Wi5o1axrNl5OTAyEEVCqVUZtOpyu277NCw9KyBtRq0zJYS6LnU7u2VukSiP62MgkNALh1\n6xYmTZqEYcOGoX///njw4AEsLCwAAD169EB4eDi6desGvV4vzaPX66HVamFiYmLUZmFhAY1GU2zf\nZ8nOfvgC14ror7tzJ0fpEohkK+lDTpmcPXX37l2MHj0as2bNwqBBgwAAY8aMwdmzZwEAJ06cQMuW\nLeHs7Iz4+HgYDAbcvHkTBoMBVlZWcHBwQEJCAgAgLi4OLi4uJfYlIqLyUyZbGmvWrMGDBw+watUq\nrFq1CgAQFBSEiIgImJmZoVatWggPD4dGo4GLiwv8/f1hMBgQGhoKAJg9ezbmzp2LpUuXokmTJvDy\n8oKpqWmxfYmIqPyohBBC6SLKEncJEACMOB6mdAnY1Fn5GojkKtfdU0RE9HJiaBARkWwMDSIikq3M\nTrmlvyczZpjSJaCxzzalSyCiCoZbGkREJBtDg4iIZGNoEBGRbAwNIiKSjaFBRESyMTSIiEg2hgYR\nEcnG0CAiItkYGkREJBtDg4iIZGNoEBGRbAwNIiKSjaFBRESyMTSIiEg2hgYREcnG0CAiItkYGkRE\nJBtDg4iIZGNoEBGRbAwNIiKSjaFBRESyMTSIiEg2hgYREcmmLosnzcvLQ0hICG7cuIHc3FxMmDAB\nzZo1Q1BQEFQqFWxtbTFv3jyYmJggOjoaR48ehVqtRkhICBwdHXHlyhXZfYmIqPyUSWjs27cPNWvW\nxOLFi5GdnQ1vb280b94c06dPh6urK0JDQxEbGwtra2skJiZi165duHXrFqZMmYI9e/YgMjJSdl8i\nIio/ZRIavXr1gpeXlzRtamqK1NRUdOjQAQDg7u6O48ePw8bGBm5ublCpVLC2tkZBQQGysrKeq6+V\nlVVZrAIRERWjTELD3NwcAKDT6TB16lRMnz4dCxcuhEqlkh7PycmBTqdDzZo1jebLycmBEEJ232eF\nhqVlDajVpi96FctcptIFAKhdW6t0CS8Vjie9DMokNADg1q1bmDRpEoYNG4b+/ftj8eLF0mN6vR4W\nFhbQaDTQ6/VG7VqtFiYmJrL7Pkt29sMXtEavnjt3cpQu4aXC8aTKpKQPOWVy9tTdu3cxevRozJo1\nC4MGDQIAODg4ICEhAQAQFxcHFxcXODs7Iz4+HgaDATdv3oTBYICVldVz9SUiovJTJlsaa9aswYMH\nD7Bq1SqsWrUKADBnzhzMnz8fS5cuRZMmTeDl5QVTU1O4uLjA398fBoMBoaGhAIDZs2dj7ty5svoS\nEVH5UQkhhNJFlKXKuksgM2aY0iWgsc82pUt4YUYcD1O6BGzqrHwNRHKV6+4pIiJ6OTE0iIhINoYG\nERHJxtAgIiLZGBpERCQbQ4OIiGRjaBARkWwMDSIiko2hQUREsjE0iIhINoYGERHJxtAgIiLZGBpE\nRCQbQ4OIiGRjaBARkWwMDSIikk1WaISHhxdpmz179gsvhoiIKrZSv+51zpw5uHbtGlJSUnDx4kWp\nPT8/Hzk5lfMb8YiI6K8rNTQmTJiAGzdu4JNPPsHkyZOldlNTUzRt2rTMiyMiooql1NBo0KABGjRo\ngH379kGn0yEnJweFXyn+8OFD1KxZs1yKJCKiiqHU0Ci0du1arF271igkVCoVYmNjy6wwIiKqeGSF\nxq5du3D48GFYWVmVdT1ERFSByTp76h//+Adee+21sq6FiIgqOFlbGo0bN8awYcPg6uqKKlWqSO1P\nHhwnIqKXn6zQqFu3LurWrVvWtRARUQUnKzS4RUFERIDM0GjevDlUKpVRW506dXDs2LEyKYqIiCom\nWaFx/vx56fe8vDwcPnwYycnJz5zvzJkzWLJkCTZv3ozU1FSMHz8ejRs3BgAMHToUffr0QXR0NI4e\nPQq1Wo2QkBA4OjriypUrCAoKgkqlgq2tLebNmwcTE5Ni+xIRUfmRFRpPMjMzQ+/evbFmzZpS+61f\nvx779u1D9erVAQC//PILRo0ahdGjR0t9UlNTkZiYiF27duHWrVuYMmUK9uzZg8jISEyfPh2urq4I\nDQ1FbGwsrK2ti+1LRETlR1Zo7N27V/pdCIGLFy9CrS591oYNGyIqKgoffPABACAlJQUZGRmIjY1F\no0aNEBISgqSkJLi5uUGlUsHa2hoFBQXIyspCamoqOnToAABwd3fH8ePHYWNjU2xfXjtCRFR+ZIVG\nQkKC0bSlpSWWL19e6jxeXl64fv26NO3o6Ag/Pz+0atUKq1evxsqVK6HVao2uMjc3N5duVVJ4DKWw\nTafTFdv3WaFhaVkDarWpnNWsUDKVLgBA7dpapUt4qXA86WUgKzQiIyORl5eHjIwMFBQUwNbW9plb\nGk/r0aMHLCwspN/Dw8PRrVs36PV6qY9er4dWq4WJiYlRm4WFBTQaTbF9nyU7++Fz1Un/584d3sn4\nReJ4UmVS0occWVeEp6SkwMvLC0FBQQgODkbXrl1x5syZ5ypgzJgxOHv2LADgxIkTaNmyJZydnREf\nHw+DwYCbN2/CYDDAysoKDg4O0tZNXFwcXFxcSuxLRETlR9bmwvz587Fs2TI4OTkBAJKTkxEeHo7d\nu3fLXlBYWBjCw8NhZmaGWrVqITw8HBqNBi4uLvD394fBYEBoaCiAP7/gae7cuVi6dCmaNGkCLy8v\nmJqaFtuXiIjKj0oU3uu8FAMGDMC+ffuM2vr374+vv/66zAp7USrrLoHMmGFKl4DGPtuULuGFGXE8\nTOkSsKmz8jUQyfW3dk+99tprOHz4sDR9+PBhfpcGEdErSNbuqfDwcIwbNw5z5syR2nbs2FFmRRER\nUcUka0sjLi4O1atXx5EjR7Bp0yZYWVkhMTGxrGsjIqIKRlZo/Oc//8H27dtRo0YNNG/eHDExMdiy\nZUtZ10ZERBWMrNDIy8uDmZmZNP3k70RE9OqQdUyje/fuGDFiBHr37g2VSoWDBw+iW7duZV0bERFV\nMLJCY9asWfjuu+9w8uRJqNVqBAYGonv37mVdGxERVTCy7wXSq1cv9OrVqyxrISKiCk7WMQ0iIiKA\noUFERM+BoUFERLIxNIiISDaGBhERycbQICIi2RgaREQkG0ODiIhkY2gQEZFsDA0iIpKNoUFERLIx\nNIiISDaGBhERycbQICIi2RgaREQkG0ODiIhkY2gQEZFsDA0iIpKNoUFERLKVaWicOXMGAQEBAIAr\nV65g6NChGDZsGObNmweDwQAAiI6OxqBBgzBkyBCcPXv2ufsSEVH5KbPQWL9+PT788EM8fvwYABAZ\nGYnp06dj27ZtEEIgNjYWqampSExMxK5du7B06VJ89NFHz92XiIjKT5mFRsOGDREVFSVNp6amokOH\nDgAAd3d3/Pe//0VSUhLc3NygUqlgbW2NgoICZGVlPVdfIiIqP2UWGl5eXlCr1dK0EAIqlQoAYG5u\njpycHOh0Omg0GqlPYfvz9CUiovKjfnaXF8PE5P/ySa/Xw8LCAhqNBnq93qhdq9U+V99nsbSsAbXa\n9AWtRfnJVLoAALVrP3t8ST6OJ70Myi00HBwckJCQAFdXV8TFxaFjx45o2LAhFi9ejDFjxuB///sf\nDAYDrKysnqvvs2RnPyyHtXs53bnDLbkXieNJlUlJH3LKLTRmz56NuXPnYunSpWjSpAm8vLxgamoK\nFxcX+Pv7w2AwIDQ09Ln7EhFR+VEJIYTSRZSlyvrpLjNmmNIloLHPNqVLeGFGHA9TugRs6qx8DURy\nlbSlwYv7iIhINoYGERHJxtAgIiLZGBpERCQbQ4OIiGRjaBARkWwMDSIiko2hQUREsjE0iIhINoYG\nERHJxtAgIiLZGBpERCQbQ4OIiGRjaBARkWwMDSIiko2hQUREsjE0iIhINoYGERHJxtAgIiLZGBpE\nRCQbQ4OIiGRjaBARkWwMDSIiko2hQUREsjE0iIhINoYGERHJxtAgIiLZGBpERCSburwX+Pbbb0Or\n1QIAGjRoAH9/f3zyyScwNTWFm5sbJk+eDIPBgLCwMKSlpaFKlSqYP38+GjVqhOTk5CJ9iYio/JRr\naDx+/BgAsHnzZqlt4MCBiIqKwhtvvIF3330XqampuHHjBnJzc7Fz504kJydjwYIFWL16NebNm1ek\nb8uWLctzFYiIXmnlGhrnz5/HH3/8gdGjRyM/Px9TpkxBbm4uGjZsCABwc3PDiRMncOfOHXTp0gUA\n0KZNG6SkpECn0xXbl6FBRFR+yjU0qlWrhjFjxsDPzw+ZmZkYO3YsLCwspMfNzc1x7do16HQ6aDQa\nqd3U1LRIW2HfZ7G0rAG12vTFrkg5yFS6AAC1a2uVLuGlwvGkl0G5hoaNjQ0aNWoElUoFGxsbaLVa\n/P7779Ljer0eFhYWePToEfR6vdRuMBig0WiM2gr7Pkt29sMXuxKvkDt3cpQu4aXC8aTKpKQPOeV6\n9tTu3buxYMECAMDt27fxxx9/oEaNGrh69SqEEIiPj4eLiwucnZ0RFxcHAEhOToadnR00Gg3MzMyK\n9CUiovJTrlsagwYNQnBwMIYOHQqVSoWIiAiYmJhg5syZKCgogJubG5ycnNC6dWscP34cQ4YMgRAC\nERERAICPPvqoSF8iIio/KiGEULqIslRZdwlkxgxTugQ09tmmdAkvzIjjYUqXgE2dla+BSK4KsXuK\niIgqN4YGERHJxtAgIiLZGBpERCQbQ4OIiGRjaBARkWwMDSIiko2hQUREsjE0iIhINoYGERHJxtAg\nIiLZGBpERCRbuX9HuOJ2f6V0BcCggUpXQET0l3BLg4iIZGNoEBGRbAwNIiKSjaFBRESyMTSIiEg2\nhgYREcnG0CAiItleves0iOhvmRB3UekSAACr3W2VLuGVxC0NIiKSjaFBRESyMTSIiEg2hgYREcnG\n0CAiItkYGkREJFulO+XWYDDp3KdeAAAOuUlEQVQgLCwMaWlpqFKlCubPn49GjRopXRYR0Suh0m1p\nHD58GLm5udi5cyfef/99LFiwQOmSiIheGZVuSyMpKQldunQBALRp0wYpKSkKV0RE9NcVfHFD6RJg\nGlhfdl+VEEKUYS0v3Jw5c9CzZ0+89dZbAICuXbvi8OHDUKsrXf4REVU6lW73lEajgV6vl6YNBgMD\ng4ionFS60HB2dkZcXBwAIDk5GXZ2dgpXRET06qh0u6cKz566cOEChBCIiIhA06ZNlS6LiOiVUOlC\ng4iIlFPpdk8REZFyGBpERCQbQ4OIiGRjaBARkWy8wIGoGHl5eUhLS0NOTg4sLCxga2uLKlWqKF0W\nkeIYGi+Je/fu4dSpU9KbXJs2bVCnTh2lyyqiMtR59OhR/Otf/0Ljxo1Ro0YN6PV6XL58GTNmzED3\n7t2VLs9IZRjPyshgMMDEpOLviFGiTp5yK0NF/8fctWsXdu7ciXbt2sHc3Bx6vR4nT56En58fhg4d\nqnR5kspS55AhQ/DZZ59Bo9FIbTk5ORg5ciT27NmjYGXGKst4AkB2djZWrVqFEydOQKfTQavVwsXF\nBZMnT8brr7+udHkAgGvXriEyMhIpKSlQq9UwGAyws7NDcHAwbGxslC5Ponidgkr1n//8R/j6+oqI\niAjx6aefioiICOHt7S22bdumdGkSf39/kZuba9T2+PFj4ePjo1BFxassdfr4+Ii8vDyjtsePHwtf\nX1+FKipeZRlPIYR49913xTfffCNycnKEwWAQOTk5Yv/+/WLEiBFKlyYJCAgQycnJRm2nT58W/v7+\nClVUPKXr5O6pZ9izZw+2b98OMzMzqS03NxdDhw6tMJ/m8vPz8fjxY6MaHz16BJVKpWBVRVWWOv39\n/eHt7Y127dpBq9VCp9MhKSkJAQEBSpdmpLKMJwDodDr06dNHmtZoNOjbty+2bt2qYFXGcnNz4eTk\nZNTWpk0bhaopmdJ1MjSeoTL8Y06cOBE+Pj5o1KiR9CZ35coVBAcHK12akcpS5+DBg+Hp6YmzZ89C\np9NBo9Fg0qRJqFWrltKlGaks4wkAr7/+OqKjo+Hu7i7ddPTYsWOoXbu20qVJ7O3tERwcjC5dukCr\n1Uo12tvbK12aEaXr5DGNZ/jhhx+wYMGCYv8xu3btqnR5kvz8fFy6dEl6k2vatGmFvPtvZamzOEeO\nHIGHh4fSZRipLOP5+PFjbN++HUlJSVKtzs7OGDp0KKpVq6Z0eQAAIQQOHz5cpMYePXpUqA+JStfJ\n0JChsvxjPm3Xrl3w8/NTuoxnqmh1Fr7OT9u4cSNGjhxZ/gU9p4o2nqXJz8+v8P9L//vf/1CvXj2l\ny3im8qqz4p9TVgGo1WrY29ujXbt2sLe3h1qtxq5du5Quq0T37t0DAFSvXl3hSkqXlZUFIUSFq7Nz\n587Fvr4VPTByc3Px6NGjCjeepRk3bpzSJTzTsmXLlC5BlvKqk1saf9H+/fvRr18/pcsAAGRkZBhN\nz549GwsXLgSACnWq4J49e3Dr1i14eHjg/fffR9WqVfHo0SOEhoaic+fOSpcn8ff3R8uWLZGeno7J\nkyejQ4cOSpdUrIyMDCxbtgxmZmYICAjA7NmzkZ+fjxkzZqBv375Kl0cvKYbGS6Br166oVq0a6tSp\nAyEEzp8/j+bNm0OlUuGLL75QujyJr68vNm/ejAkTJiAsLAw2Nja4ffs2Jk6cWKGufwgMDMQXX3yB\nc+fOYd26dcjIyMCbb76JN954A4GBgUqXJxk+fDgmTpyInJwchIaGYt++fdBqtRg1ahR27typdHlF\nHD58GCdOnJCud2rXrh169epVoY4XVAZZWVlYt24dqlatipEjR8LS0hIAEB0djcmTJ5f58iv2zsQK\nICAgAHl5eUZtQgioVCrs2LFDoaqM7dmzB/PmzcPQoUPRuXNnBAQEYPPmzUqXVYSZmRlq1KgBc3Nz\nvPHGGwCAunXrVrg3jcLPUa1bt0ZUVBRycnJw8uTJIlt0SsvPz0enTp0ghMDSpUtRt25dAKiQxwg+\n+ugjGAwGuLu7SxcixsXFIT4+Hp988onS5QFAqUHr7+9fjpWU7oMPPkCPHj2Qn5+P4cOHY926dahf\nvz4SExPLZfkV76+rgpk5cyY+/PBDrFy5EqampkqXU6zXX38dy5cvx8KFC3Hu3DmlyymRp6cnJkyY\nADs7O4wbNw5dunTBjz/+iI4dOypdmhEfHx+jaa1WC09PT4WqKVn9+vXx3nvvoaCgAObm5li2bBk0\nGk2FOo210MWLF7Flyxajtm7dumHIkCEKVVTU5cuXceTIEQwYMEDpUkqVm5srhViLFi0wceJEbN68\nGeW104ih8QxOTk4YOHAg0tLS0KNHD6XLKZFarcacOXMQExNTbn88z+vdd99FYmIi4uPjYW1tjXv3\n7iEgIKBCnboMAN7e3kqXIMvChQtx7NgxNG7cGObm5ti4cSOqVauGiIgIpUsrwmAw4NSpU3BxcZHa\nTp48aXT9k9KCg4Nx+fJluLu7w9HRUelySlRQUIC0tDTY29vD2dkZ48aNw4QJE/Dw4cNyWT6PaRBR\nmbt69SoiIyORmpoKIQRMTEzg4OCA2bNno3HjxkqXJ8nKysLDhw/RoEEDpUsp0a+//oqIiAgsW7ZM\nuuD0q6++QkREBBISEsp8+QwNIqKXQHnd8Za7p4iozBV3QkmhinJCSWU46QVQfiy5pUFEZe7MmTMl\nnlBSv359haoyVhlqBJSv0zQsLCyszJdCRK+0evXq4eHDh8jPz0ebNm1gYWEh/VQUlaFGQPk6uaVB\nRESy8d5TREQkG0ODiIhkY2hQpZSQkPBc36Q3duxY3L59GzExMQgKCir1+ebMmaP4lfVBQUGIiYnB\n7du3MXbsWAB/fp/Hhg0bSp0vODgYN27ceGF1PLn8p1W0Lyei8sHQoFfC+vXrpXszPcsnn3yC1q1b\nl3FF8tStWxfr168HAKSkpECn05XaPyEh4YXeEeDJ5RMBvE6DKrHs7GyMGTMGv/32GxwdHTFv3jy0\nbt0aaWlpAICYmBgkJiZiwYIF8PT0LHLH3/j4eERGRqJq1apGt5APCAiQ7ha6du1aVKtWDZcuXYK9\nvT2WLFmCKlWq4IsvvsCWLVug1WrRpEkTNGzYEFOmTMHevXuxevVqaDQatGnTBnq93mj5DRo0QEJC\nAqKjo7F582YkJiZi2bJlePToER48eIDg4GB0795dquX69esIDAzEunXrpHPw69Wrh9WrV+Pzzz+H\njY0NHj58iN69e+Odd97Bb7/9hnfffRfTpk3Dhg0bpHliYmJw5swZODk54ejRo7h37x7u3LkDDw8P\nBAUFQaVSYd26dThw4AAKCgrg5uaGWbNm4caNGwgMDMQPP/yA69evY9asWXj48GGR76imVwe3NKjS\nun79OubOnYt9+/ZBr9dj+/btsufNzc1FUFAQVqxYgZiYmBK/cvT06dMIDQ3FgQMHcPPmTcTHx+P8\n+fPYunUrYmJisG3bNly5cgXAn9+ctmjRImzZsgU7d+6UdVfcLVu2YP78+fjyyy8xf/58fPrpp8X2\na9asGYYMGYIhQ4bAz88Pb7/9Nvbt2wcAOHToELp27Yp3330XderUwbp169CzZ0/cuXMHV69eBQDs\n3btXuhFjUlISPv30U+zfvx9nzpzB999/j7i4OKSkpGD37t3Yu3cvbt++LT1/ofDwcPj4+OCrr76C\ns7OzvIGmlw5DgyotFxcXNG7cGCqVCv3793+uW0OnpaWhTp06aNq0KYCSb1Joa2uLevXqwcTEBE2b\nNsX9+/dx4sQJeHh4QKPRoGrVqtIXHp0+fRrOzs6oXbs21Gq1rBsfLl68GBcvXsTKlSuxYcMG6PV6\nWfX7+Phg//79AIAvv/yyyJ15VSoVvL29sW/fPty8eRP37t2Ttg66deuGWrVqoUqVKujTpw9++ukn\nnDhxAmfPnoWPjw+8vb2RkpKC9PR0o+dMTExE7969AQADBgyoUDcbpPLD3VNUaT35vRFCCGm68NYP\n+fn5Jc6rUqmM9v2XdNv7qlWrFpnHxMQEBoOhSN+nt1ae/l6LwuU9WdewYcPg6uoKV1dXvPnmm5g5\nc2aJNT+pQYMGsLa2xqFDh4wC4Une3t745z//iSpVqmDgwIFS+5PrajAYYGpqioKCAowYMQKjRo0C\nADx48ACmpqbIzs4udh1UKlW53OeIKh6+6lRpJSUl4ebNmzAYDNi7dy86deoES0tLXLx4EUII/PDD\nDyXOa29vj7t37+L8+fMAgG+++Ub2ct98800cO3YMOp0Oubm5OHToEFQqFZycnJCSkoJbt27BYDDg\n22+/leaxtLSUPrnHxsYCAH7//XdkZmZi2rRpcHd3R2xsLAoKCkpcrqmpqVHg+Pr6Yv78+Ubf/1AY\nAMCft5SoV68eduzYYRQaP/74I3JycvD48WN88803cHd3R8eOHfHVV19Br9cjPz8fkyZNwsGDB42W\n36lTJ6NdYo8fP5Y9ZvTy4JYGVVrNmjVDSEgI7ty5g44dO2LQoEEwMTHB+PHjUatWLbRr167IJ+VC\nZmZmWLp0KWbNmgW1Wg0HBwfZy7Wzs0NgYCD8/f1Ro0YNWFpaomrVqrCyssLHH3+McePGQa1Wo06d\nOtI8U6dORXh4OKKjo+Hm5gYAqFmzJgYNGoS+fftCrVajY8eOePToUYnfi9C+fXvMnj0btWrVQkBA\nAHr27Im5c+caBULhsY3PPvsMb7zxBvr06YNDhw4ZnTlmZWWFsWPHIjs7GwMGDECXLl0AAOfPn8fg\nwYNRUFCALl26wNvb2+j03dDQUMyaNQs7d+5Eq1atYG5uLnvM6OXB24gQPaeMjAwcO3YMI0eOBABM\nmDABfn5+Rb7d78mzt140IQTi4uKwfft2rFmzptg++fn5+OCDD9CrVy/07NmzzGuiVwO3NIieU/36\n9XHu3Dn069cPKpUKbm5u8PDwKNcaIiIicOTIkRKvoRBCoEuXLujUqZPRKbxEfxe3NIiISDYeCCci\nItkYGkREJBtDg4iIZGNoEBGRbAwNIiKSjaFBRESy/T9HFeU0OAfCEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ae18553710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "\n",
    "ax = sns.countplot(x=\"buildingqualitytypeid\", data=df_train)\n",
    "\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title(\"Frequency of Bathroom count\", fontsize=15)\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ChQsX4p133oGiKF7HO50aVNUYclgiorijqoCmeV/ucEQ/T8/0elbKyMjAqFGjkJycjFGj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ae798c4828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_train.yearbuilt,df_train.buildingqualitytypeid , 'ro')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from ggplot import *\n",
    "\n",
    "#ggplot(aes(x='latitude', y='longitude', color='buildingqualitytypeid'), data=df_train) +  geom_point() +   scale_color_gradient(low = 'red', high = 'blue')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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  = [5,10,15,20,25]\n",
    "    param_grid    = dict(metric=metrics,weights=weights,n_neighbors=numNeighbors)\n",
    "    cv            = StratifiedKFold(n_splits=3,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": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32911\n",
      "(90275, 48)\n",
      "32911\n",
      "(90272, 48)\n",
      "# of miss 32911\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:  1.6min 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 (32911,)\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(df_train.buildingqualitytypeid.isnull().sum())\n",
    "print(df_train.shape)\n",
    "temp=df_train.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",
    "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)\n",
    "missing_values_boolflag = df_train['buildingqualitytypeid'].isnull()\n",
    "df_train.loc[ missing_values_boolflag, 'buildingqualitytypeid' ]  = missing_values\n",
    "\n",
    "\n",
    "print(df_train.buildingqualitytypeid.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34195\n",
      "(90275, 48)\n",
      "34195\n",
      "(90269, 48)\n",
      "# of miss 34195\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:   45.4s 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=5, p=2,\n",
      "           weights='distance')\n",
      "grid.best_params_ {'metric': 'euclidean', 'n_neighbors': 5, 'weights': 'distance'}\n",
      "grid.scorer_ make_scorer(f1_score, pos_label=None, average=weighted)\n",
      "predicted output shape (34195,)\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(df_train.heating_system_id.isnull().sum())\n",
    "print(df_train.shape)\n",
    "temp=df_train.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",
    "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",
    "missing_values_boolflag = df_train['heating_system_id'].isnull()\n",
    "df_train.loc[ missing_values_boolflag, 'heating_system_id' ]  = missing_values\n",
    "\n",
    "\n",
    "print(df_train.heating_system_id.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from ggplot import *\n",
    "#heatingorsystemtypeid\n",
    "#heating_system_id\n",
    "\n",
    "\n",
    "#ggplot(aes(x='latitude', y='longitude', color='heatingorsystemtypeid'), data=prop2016) +  geom_point() +   scale_color_gradient(low = 'red', high = 'blue')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from ggplot import *\n",
    "#airconditioningtypeid\n",
    "#ac_id\n",
    "#ggplot(aes(x='latitude', y='longitude', color='airconditioningtypeid'), data=prop2016) +  geom_point() +   scale_color_gradient(low = 'red', high = 'blue')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61494\n",
      "(90275, 48)\n",
      "61494\n",
      "(90273, 48)\n",
      "# of miss 61494\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:   45.3s 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=25, p=2,\n",
      "           weights='distance')\n",
      "grid.best_params_ {'metric': 'euclidean', 'n_neighbors': 25, 'weights': 'distance'}\n",
      "grid.scorer_ make_scorer(f1_score, pos_label=None, average=weighted)\n",
      "predicted output shape (61494,)\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(df_train.ac_id.isnull().sum())\n",
    "print(df_train.shape)\n",
    "temp=df_train.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",
    "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",
    "missing_values_boolflag = df_train['ac_id'].isnull()\n",
    "df_train.loc[ missing_values_boolflag, 'ac_id' ]  = missing_values\n",
    "\n",
    "\n",
    "print(df_train.ac_id.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from ggplot import *\n",
    "\n",
    "#ggplot(aes(x='latitude', y='longitude', color='yearbuilt'), data=df_train) +  geom_point() +   scale_color_gradient(low='#E1FA72', high='#F46FEE')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "756\n",
      "(90275, 48)\n",
      "756\n",
      "(90261, 48)\n",
      "# of miss 756\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:  1.7min 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=5, p=2,\n",
      "           weights='distance')\n",
      "grid.best_params_ {'metric': 'euclidean', 'n_neighbors': 5, 'weights': 'distance'}\n",
      "grid.scorer_ make_scorer(f1_score, pos_label=None, average=weighted)\n",
      "predicted output shape (756,)\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "#yearbuilt\n",
    "\n",
    "print(df_train.yearbuilt.isnull().sum())\n",
    "print(df_train.shape)\n",
    "temp=df_train.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",
    "print(temp.yearbuilt.isnull().sum())\n",
    "print(temp.shape)\n",
    "\n",
    "missing_values=fillna_knn(temp,\n",
    "                  base = [ 'latitude', 'longitude','buildingqualitytypeid','propertylandusetypeid' ] ,\n",
    "                  target = 'yearbuilt')\n",
    "\n",
    "print(\"predicted output shape\",missing_values.shape)\n",
    "missing_values_boolflag = df_train['yearbuilt'].isnull()\n",
    "df_train.loc[ missing_values_boolflag, 'yearbuilt' ]  = missing_values\n",
    "print(df_train.yearbuilt.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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  = [5,10,15,20,25]\n",
    "    param_grid    = dict(metric=metrics,weights=weights,n_neighbors=numNeighbors)\n",
    "    cv            = KFold(n_splits=3,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": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8920\n",
      "(90275, 48)\n",
      "8920\n",
      "(29647, 48)\n",
      "# of miss 8920\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:  1.6min 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=5, p=2,\n",
      "          weights='distance')\n",
      "grid.best_params_ {'metric': 'euclidean', 'n_neighbors': 5, 'weights': 'distance'}\n",
      "grid.scorer_ make_scorer(mean_absolute_error, greater_is_better=False)\n",
      "predicted output shape (8920,)\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "#garage_sqft\n",
    "print(df_train.garage_sqft.isnull().sum())\n",
    "print(df_train.shape)\n",
    "temp=df_train.loc[df_train.garagecarcnt>0,df_train.columns].copy()\n",
    "\n",
    "print(temp.garage_sqft.isnull().sum())\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",
    "missing_values_boolflag = df_train['garage_sqft'].isnull()\n",
    "df_train.loc[ missing_values_boolflag, 'garage_sqft' ] = missing_values\n",
    "print(df_train.garage_sqft.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train.drop('parcelid', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "#All the other columns with missing values seems to be  integer, will need regression to be imputed,\n",
    "#time to get categorical variables hot encoded\n",
    "\n",
    "#Identify numerical columns to produce a heatmap\n",
    "catcols = ['ac_id','buildingqualitytypeid','deck_flag','fips', 'heating_system_id','has_hottub_or_spa',\n",
    "          'just_hottub_or_spa', 'pool_with_spa_tub_yes','pool_with_spa_tub_no','propertylandusetypeid','basement_flag'\n",
    "          ,'fireplaceflag','taxdelinquencyflag']\n",
    "numcols = [x for x in df_train.columns if x not in catcols]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from ggplot import *\n",
    "\n",
    "#ggplot(aes(x='latitude', y='longitude', color='total_finished_living_area_sqft'), data=df_train) +  geom_point() +   scale_color_gradient(low='#E1FA72', high='#F46FEE')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "661\n",
      "(90275, 47)\n",
      "661\n",
      "(90275, 47)\n",
      "# of miss 661\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:  3.7min 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 (661,)\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "#total_finished_living_area_sqft\n",
    "\n",
    "print(df_train.total_finished_living_area_sqft.isnull().sum())\n",
    "print(df_train.shape)\n",
    "temp=df_train.copy()\n",
    "print(temp.total_finished_living_area_sqft.isnull().sum())\n",
    "print(temp.shape)\n",
    "missing_values=fillna_knnr(temp,\n",
    "                  base = [ 'latitude', 'longitude','basementsqft','numberofstories','poolcnt','garagecarcnt','garage_sqft','propertylandusetypeid'] ,\n",
    "                  target = 'total_finished_living_area_sqft')\n",
    "\n",
    "print(\"predicted output shape\",missing_values.shape)\n",
    "missing_values_boolflag = df_train['total_finished_living_area_sqft'].isnull()\n",
    "df_train.loc[ missing_values_boolflag, 'total_finished_living_area_sqft' ] = missing_values\n",
    "print(df_train.total_finished_living_area_sqft.isnull().sum())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1165\n",
      "(90275, 47)\n",
      "1165\n",
      "(90275, 47)\n",
      "# of miss 1165\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:  1.4min 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 (1165,)\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "#total_bath\t1165\n",
    "#full_bath\t1182\n",
    "#half_bath\t1182\n",
    "#roomcnt\t1416\n",
    "#bedroomcnt\t1421\n",
    "\n",
    "#total_finished_living_area_sqft\n",
    "\n",
    "print(df_train.total_bath.isnull().sum())\n",
    "print(df_train.shape)\n",
    "temp=df_train.copy()\n",
    "print(temp.total_bath.isnull().sum())\n",
    "print(temp.shape)\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",
    "missing_values_boolflag = df_train['total_bath'].isnull()\n",
    "df_train.loc[ missing_values_boolflag, 'total_bath' ] = missing_values\n",
    "print(df_train.total_bath.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop half_bath and full bath, as there are only redundant values of total_bath\n",
    "df_train = df_train.drop(['full_bath','half_bath'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1421\n",
      "(90275, 45)\n",
      "1421\n",
      "(90275, 45)\n",
      "# of miss 1421\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:  1.5min 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 (1421,)\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#bedroomcnt\t1421\n",
    "\n",
    "print(df_train.bedroomcnt.isnull().sum())\n",
    "print(df_train.shape)\n",
    "temp=df_train.copy()\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",
    "missing_values_boolflag = df_train['bedroomcnt'].isnull()\n",
    "df_train.loc[ missing_values_boolflag, 'bedroomcnt' ] = missing_values\n",
    "print(df_train.bedroomcnt.isnull().sum())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['total_bath']=df_train.total_bath.round(1)\n",
    "df_train['bedroomcnt']=df_train.bedroomcnt.round(1)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "#recalculate roomcnt\t1416 as we have used imputation for total_bath and bedroomcnt\n",
    "\n",
    "df_train.loc[(df_train.roomcnt.isnull()),['roomcnt']]=df_train.total_bath + df_train.bedroomcnt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(90275, 45)\n",
      "(90274, 45)\n"
     ]
    }
   ],
   "source": [
    "print(df_train.shape)\n",
    "df_train =df_train.loc[(df_train.total_parcel_tax.notnull()) & (df_train.land_tax.notnull()),df_train.columns]\n",
    "\n",
    "print(df_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10150\n",
      "(90274, 45)\n",
      "10150\n",
      "(90274, 45)\n",
      "# of miss 10150\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:  1.5min 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=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 (10150,)\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "#lot_area_sqft\n",
    "print(df_train.lot_area_sqft.isnull().sum())\n",
    "print(df_train.shape)\n",
    "temp=df_train.copy()\n",
    "print(temp.lot_area_sqft.isnull().sum())\n",
    "print(temp.shape)\n",
    "missing_values=fillna_knnr(temp,\n",
    "                  base = ['latitude','longitude','propertylandusetypeid','total_finished_living_area_sqft','roomcnt','numberofstories' ] ,\n",
    "                  target = 'lot_area_sqft')\n",
    "\n",
    "print(\"predicted output shape\",missing_values.shape)\n",
    "missing_values_boolflag = df_train['lot_area_sqft'].isnull()\n",
    "df_train.loc[ missing_values_boolflag, 'lot_area_sqft' ] = missing_values.round(2)\n",
    "print(df_train.lot_area_sqft.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "379\n",
      "(90274, 45)\n",
      "379\n",
      "(90274, 45)\n",
      "# of miss 379\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:  1.4min 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 (379,)\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "# predict structure_tax and recalculate  total_parcel_tax = land_tax + structure_tax\n",
    "\n",
    "\n",
    "print(df_train.structure_tax.isnull().sum())\n",
    "print(df_train.shape)\n",
    "temp=df_train.copy()\n",
    "print(temp.structure_tax.isnull().sum())\n",
    "print(temp.shape)\n",
    "missing_values=fillna_knnr(temp,\n",
    "                  base = ['latitude','longitude','lot_area_sqft','propertylandusetypeid','total_finished_living_area_sqft','roomcnt','numberofstories' ] ,\n",
    "                  target = 'structure_tax')\n",
    "\n",
    "print(\"predicted output shape\",missing_values.shape)\n",
    "missing_values_boolflag = df_train['structure_tax'].isnull()\n",
    "df_train.loc[ missing_values_boolflag, 'structure_tax' ] = missing_values.round(2)\n",
    "print(df_train.structure_tax.isnull().sum())\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "#36 total_property_tax_2016 \n",
    "\n",
    "#total_parcel_tax = land_tax + structure_tax\n",
    "    \n",
    "df_train['total_parcel_tax']=df_train['structure_tax']+df_train['land_tax']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "#age of the property\n",
    "df_train['age'] = 2016 - df_train['yearbuilt']\n",
    "df_train=df_train.drop(['yearbuilt'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "(90274, 45)\n",
      "6\n",
      "(90274, 45)\n",
      "# of miss 6\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:  1.4min 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 (6,)\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "#total_property_tax_2016\n",
    "\n",
    "\n",
    "print(df_train.total_property_tax_2016.isnull().sum())\n",
    "print(df_train.shape)\n",
    "temp=df_train.copy()\n",
    "print(temp.total_property_tax_2016.isnull().sum())\n",
    "print(temp.shape)\n",
    "missing_values=fillna_knnr(temp,\n",
    "                  base = ['latitude','longitude','lot_area_sqft','propertylandusetypeid','total_finished_living_area_sqft','roomcnt','numberofstories' ] ,\n",
    "                  target = 'total_property_tax_2016')\n",
    "\n",
    "print(\"predicted output shape\",missing_values.shape)\n",
    "missing_values_boolflag = df_train['total_property_tax_2016'].isnull()\n",
    "df_train.loc[ missing_values_boolflag, 'total_property_tax_2016' ] = missing_values.round(2)\n",
    "print(df_train.total_property_tax_2016.isnull().sum())\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              column_name  missing_count\n",
      "27                            regionidzip             35\n",
      "25                           regionidcity           1803\n",
      "26                   regionidneighborhood          54262\n",
      "7   finished_living_area_entryfloor_sqft2          83419\n",
      "9   finished_living_area_entryfloor_sqft1          83419\n",
      "(5, 2)\n"
     ]
    }
   ],
   "source": [
    "#check missing values\n",
    "\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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#both the columns above miss 92% of the data, there is no related varibale to impute it, hence dropping them at this point\n",
    "\n",
    "df_train = df_train.drop(['finished_living_area_entryfloor_sqft2','finished_living_area_entryfloor_sqft1'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Identify numerical columns to produce a heatmap\n",
    "catcols = ['ac_id','buildingqualitytypeid','deck_flag','fips','pool_with_spa_tub_no','pool_with_spa_tub_yes','has_hottub_or_spa',\n",
    "           'just_hottub_or_spa','heating_system_id','propertylandusetypeid','basement_flag','fireplaceflag','taxdelinquencyflag']\n",
    "numcols = [x for x in df_train.columns if x not in catcols]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping categorical columns as xgboost feature selection cannot hadle it\n",
    "\n",
    "train_x = df_train.drop(catcols+['logerror'], axis=1)\n",
    "\n",
    "train_y=df_train['logerror']\n",
    "\n",
    "train_x = train_x.astype(float) \n",
    "train_y = train_y.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = 65\n",
    "\n",
    "dtype_df = train_x.dtypes.reset_index()\n",
    "dtype_df.columns = [\"Count\", \"Column Type\"]\n",
    "#dtype_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.loc[df_train.has_hottub_or_spa==True,'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": 106,
   "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",
    "    df_train.to_sql('df_train_f2', engine, chunksize=10000, index =False,if_exists ='replace')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
    "df_train=pd.read_sql_table('df_train_f2',con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#regionidcity\n",
    "#regionidzip\n",
    "#regionidneighborhood\n",
    "#most zip id's either invalid or out of city, since enough information about location is given in latitude and longitude,\n",
    "# drop the above the \n",
    "\n",
    "df_train=df_train.drop(['regionidzip','regionidcity','regionidneighborhood'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Deviation away from average\n",
    "#df_train['N-Dev-structure_tax'] = abs((df_train['structure_tax'] - df_train['N-Avg-structure_tax']))/df_train['N-Avg-structure_tax']\n",
    "\n",
    "\n",
    "\n",
    "#Does property have a garage, pool or hot tub and AC?\n",
    "#df_train['N-GarPoolAC'] = ((df_train['garagecarcnt']>0) & (df_train['pooltypeid10']>0) & (df_train['airconditioningtypeid']!=5))*1 \n",
    "\n",
    "\n",
    "\n",
    "#Number of properties in the zip\n",
    "#zip_count = df_train['regionidzip'].value_counts().to_dict()\n",
    "#df_train['N-zip_count'] = df_train['regionidzip'].map(zip_count)\n",
    "\n",
    "#Number of properties in the city\n",
    "#city_count = df_train['regionidcity'].value_counts().to_dict()\n",
    "#df_train['N-city_count'] = df_train['regionidcity'].map(city_count)\n",
    "\n",
    "#Number of properties in the city\n",
    "#region_count = df_train['regionidcounty'].value_counts().to_dict()\n",
    "#df_train['N-county_count'] = df_train['regionidcounty'].map(region_count)\n",
    "\n",
    "\n",
    "#Average structure_tax by city\n",
    "#group = df_train.groupby('regionidcity')['structure_tax'].aggregate('mean').to_dict()\n",
    "#df_train['N-Avg-structure_tax'] = df_train['regionidcity'].map(group)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_train=df_train.drop(['ac_id','heating_system_id','propertylandusetypeid'], axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "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": 10,
   "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": 11,
   "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "numcols = set(list(df_train.columns))-set((list(dummies.columns)+dummies2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=df_train['logerror']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#buildingqualitytypeid ->has order\n",
    "#le = LabelEncoder()\n",
    "#df_train['buildingqualitytypeid']=le.fit_transform(df_train.buildingqualitytypeid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_train.ac_id.value_counts()\n",
    "#df_train.propertylandusetypeid.value_counts()\n",
    "#'buildingqualitytypeid','ac_id','heating_system_id','propertylandusetypeid'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90274, 58)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=pd.concat([dummies,df_train[dummies2],df_train[numcols]],axis=1)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.10, random_state=3192)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 19,
     "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  14.43   24.96   32.35   37.99   43.48   48.62   53.38   58.05   61.64\n",
      "   64.99   67.94   70.75   73.15   75.42   77.61   79.51   81.27   83.\n",
      "   84.71   86.38   88.05   89.63   91.08   92.39   93.55   94.53   95.47\n",
      "   96.26   96.83   97.32   97.74   98.15   98.48   98.78   99.05   99.31\n",
      "   99.53   99.7    99.8    99.88   99.92   99.96   99.99  100.    100.\n",
      "  100.    100.    100.    100.    100.    100.    100.    100.    100.\n",
      "  100.    100.    100.    100.  ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0e88e77b8>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kw+s4IhJgGkZ6YMO+Rn75chmXzy1g4XR9gIiI9C8V/QA70tnNrU9sZvTQTL794WlexxGR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\nIpM+op7wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c0e3ad8a58>"
      ]
     },
     "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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Looking at above plot I'm taking 36 variables\n",
    "\n",
    "pca = PCA(n_components=36, 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=36, 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": 22,
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ppwEwf/58Nm/evKVdY5ZKpZrEfTQlnpP84zmpm7dnNW03AKOANgCSmiVb80ok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\n0+YSkne8sssrM44rMo47ZRzfAdyRHj9H8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ZKuAP4baA/8CfhKRISkjwFTgPXAI8AJEZHz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\nERER0SD/H+x4BeNYRDUTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c0e90f53c8>"
      ]
     },
     "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": 24,
   "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_No','taxdelinquencyflag_Yes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(81246, 58)\n",
      "(81246, 27)\n"
     ]
    },
    {
     "data": {
      "image/png": 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GAIBBhG0AABhE2AYAgEGEbQAAGETYBgCAQYRtAAAYRNgGAIBBhG0AABhE2AYAgEGEbQAA\nGETYBgCAQRYStqvqzKp6X1UdqKpnrrL9oVX1jqq6oaoet2LbuVX1/ul07tz6B1bVu6eav1BVtYhe\nAQBgq2w6bFfVcUlenOSsJKcneUJVnb5it79P8tQkr1lx7F2SPDfJg5OckeS5VXXnafMvJzkvyWnT\n6czN9goAAFtpESPbZyQ50N1XdvdnkrwuydnzO3T3h7r7XUk+u+LYRyR5c3df293XJXlzkjOr6h5J\n7tDdf9HdneSVSR67gF4BAGDLLCJsn5jkqrnlg9O6zRx74nR+IzUBAGBHWETYXm0udW/y2DXXrKrz\nqmp/Ve2/5ppr1nixAAAw3iLC9sEkJ88tn5Tk6k0ee3A6f9ia3X1Bd+/p7j27d+9ec9MAADDaIsL2\nZUlOq6p7V9WtkpyTZO8aj700ycOr6s7TGyMfnuTS7v5Ikuur6iHTp5A8JckbF9ArAABsmU2H7e6+\nIckzMgvOf53k17v7iqo6v6oekyRV9aCqOpjk8UleUlVXTMdem+QnMwvslyU5f1qXJN+T5FeTHEjy\ngSS/u9leAQBgK+1aRJHuviTJJSvWPWfu/GX5/Gkh8/tdmOTCVdbvT3K/RfQHAADbwTdIAgDAIMI2\nAAAMImwDAMAgwjYAAAwibAMAwCDCNgAADCJsAwDAIMI2AAAMImwDAMAgwjYAAAwibAMAwCDCNgAA\nDCJsAwDAIMI2AAAMImwDAMAgwjYAAAwibAMAwCDC9g63tLSUpaWl7W4DAIANELYBAGAQYRsAAAYR\ntgEAYBBhGwAABhG2AQBgEGEbAAAGEbYBAGAQYRsAAAYRtmHiC4QAgEUTtgEAYBBhGwAABhG2AQBg\nEGEbAAAGEbaPEd78BwCw9YRtAAAYRNgGAIBBhG0AABhE2AYAgEGEbQAAGETYBgCAQYRtAAAYRNgG\nAIBBhG0AABhE2AYAgEGEbbaNr5AHAI52wjYAAAwibAMAwCDCNutm+gcAwNoI2wAAMIiwDQAAgwjb\nAAAwiLANAACDCNsAADCIsA0AAIMI2wAAMIiwDcDN8tn6ABsnbAMAwCDCNhzljEoCwPYRtgEAYBBh\nGwAABhG2AQBgEGEbAAAGEbYBAGAQYRsAAAYRtgEAYBBhGwAABhG2AQBgEGEbAAAGEbYBgFUtLS1l\naWlpu9uAI9pCwnZVnVlV76uqA1X1zFW2H19Vr5+2v7WqTp3WP7GqLp87fbaqHjBt2zfVXN52t0X0\nCgAAW2XTYbuqjkvy4iRnJTk9yROq6vQVuz0tyXXdfd8kL0rywiTp7l/r7gd09wOSPDnJh7r78rnj\nnri8vbs/utleYSsYCQIAli1iZPuMJAe6+8ru/kyS1yU5e8U+Zye5aDp/cZKHVVWt2OcJSV67gH4A\nAGBHWETYPjHJVXPLB6d1q+7T3Tck+USSu67Y59vzhWH75dMUkmevEs4BAGBHW0TYXi0E93r2qaoH\nJ/nn7n7P3PYndvdXJvmG6fTkVS+86ryq2l9V+6+55pr1dQ4AAAMtImwfTHLy3PJJSa4+1D5VtSvJ\nHZNcO7f9nKwY1e7uD08/r0/ymsymq3yB7r6gu/d0957du3dv4moAAMBiLSJsX5bktKq6d1XdKrPg\nvHfFPnuTnDudf1ySt3R3J0lV3SLJ4zOb651p3a6qOmE6f8skj0ryngAAwBFk12YLdPcNVfWMJJcm\nOS7Jhd19RVWdn2R/d+9N8rIkr6qqA5mNaJ8zV+KhSQ5295Vz645PcukUtI9L8gdJXrrZXgEAYCtt\nOmwnSXdfkuSSFeueM3f+XzIbvV7t2H1JHrJi3SeTPHARvQEAwHbxDZIAADCIsA0AAIMI2wAAMIiw\nDQAAgwjbAAAwiLANAACDCNsAADCIsA0AAIMI2wA7zNLSUpaWlra7DQAWQNgGAIBBhG0AABhE2AYA\ngEGEbQAAGETYBgCAQYRtAAAYRNgGAIBBhG0AABhE2AYAgEGEbeCI5FsWATgSCNsAADCIsA0AAIMI\n2wALYmoLACsJ2wAAMIiwDQAAgwjbAAAwiLANAACD7NruBrZN1ca2dy++F1jF8hvt9u3bt619AAAb\nZ2QbAAAGEbYBOKL4iEXgSCJsAwDAIMI2cMwzUgrAKMI2AAAMImwDAMAgwjawJqZaAMD6CdsAADCI\nsA0AAIMI2wAAMIiwDQAAgwjbAAAwiLANcJTyCTIA20/YBgCAQYRtAAAYRNgGtpSpDQAcS4RtAAAY\nRNgGAIBBhG0AABhE2AYAgEGEbQAAGETYBgCAQYRtAAAYRNgGAIBBhG0AABhE2AYAgEGEbQAAGETY\nBmBLLC0tZWlpabvbWLij9XoBiyFsAwDAIMI2AAAMImwDAMAgwjYAAAwibAMAwCDCNgAADCJsAwDA\nIMI2AAAMImwDAEP54h+OZcI2AAAMImwDAMAgwjYAAAyykLBdVWdW1fuq6kBVPXOV7cdX1eun7W+t\nqlOn9adW1aeq6vLp9Ctzxzywqt49HfMLVVWL6BUAALbKpsN2VR2X5MVJzkpyepInVNXpK3Z7WpLr\nuvu+SV6U5IVz2z7Q3Q+YTk+fW//LSc5Lctp0OnOzvQIAwFZaxMj2GUkOdPeV3f2ZJK9LcvaKfc5O\nctF0/uIkD7u5keqqukeSO3T3X3R3J3llkscuoFcAANgyiwjbJya5am754LRu1X26+4Ykn0hy12nb\nvavqr6rqj6vqG+b2P3iYmgAAsKPtWkCN1Uaoe437fCTJKd39j1X1wCS/XVVfscaas8JV52U23SSn\nnHLKmpsGAIDRFjGyfTDJyXPLJyW5+lD7VNWuJHdMcm13f7q7/zFJuvvtST6Q5Eun/U86TM1Mx13Q\n3Xu6e8/u3bsXcHUAOBb4ohVgKywibF+W5LSqundV3SrJOUn2rthnb5Jzp/OPS/KW7u6q2j29wTJV\ndZ/M3gh5ZXd/JMn1VfWQaW73U5K8cQG9AgDAltn0NJLuvqGqnpHk0iTHJbmwu6+oqvOT7O/uvUle\nluRVVXUgybWZBfIkeWiS86vqhiQ3Jnl6d187bfueJK9I8kVJfnc6AQDAEWMRc7bT3ZckuWTFuufM\nnf+XJI9f5bg3JHnDIWruT3K/RfQHAADbwTdIAgDAIMI2AAAMImwDAMAgwjYAAAwibAMAwCAL+TSS\nY1qt9mWXa9zeq34pJgAARwkj2wAAMIiwDQAAgwjbAAAwiLANADvA0tJSlpaWtrsNYMGEbQA4ygju\nsHMI2wAAMIiwDQAAgwjbAAAwiLANAACDCNsAADCIsA0AHFN8WgtbSdgGAIBBhG0AABhE2AYAgEGE\nbQAAGETYBgCAQYRtAAAYRNgGAIBBhG0A4Ijg87E5EgnbAAAwiLANAACDCNsAADCIsA0AAIPs2u4G\nmFRtfHv34usAALBpRrYBAGAQI9us7nAj5De3jxFyAIAkRrYBAGAYYRsAAAYRtgEAYBBhGwAABhG2\nAQBgEGEbAAAGEbYBAGAQYRsAYBstLS1laWlpu9tgEF9qw1i+HAcAOIYZ2QYAOAoYId+ZhG0AABZO\n+J8RtgEAYBBztjlyHG7+t7nfAMAOY2QbAAAGEbYBADbAnGTWQtgGAOAmnkQslrANAACDeIMkxx5v\ntAQAtoiRbQAAGETYBgBgxzrS55AL2wAAMIg527BR5n4DAIdhZBsAAAYRtgEAYBBhGwAABhG2AQA4\nJmzHJ5sI2wAAMIhPI4Ht5lNNAOCoJWzD0WJRoV34B4CFMY0EAAAGEbYBAGAQYRsAAAYxZxsYY6Nz\nvxPzvwE4ahjZBgCAQYRtAAAYZCFhu6rOrKr3VdWBqnrmKtuPr6rXT9vfWlWnTuu/uareXlXvnn5+\n49wx+6aal0+nuy2iVwAA2CqbnrNdVccleXGSb05yMMllVbW3u987t9vTklzX3fetqnOSvDDJtyf5\nWJJHd/fVVXW/JJcmOXHuuCd29/7N9ggAANthESPbZyQ50N1Xdvdnkrwuydkr9jk7yUXT+YuTPKyq\nqrv/qruvntZfkeTWVXX8AnoCAIBtt4hPIzkxyVVzyweTPPhQ+3T3DVX1iSR3zWxke9m3Jvmr7v70\n3LqXV9WNSd6Q5HndPqIAjjmL+lSTw9W5uX3c9QCwQYsI26s9Oq18ZLrZfarqKzKbWvLwue1P7O4P\nV9XtMwvbT07yyi+48KrzkpyXJKeccsr6OgdYL6EdgHVYxDSSg0lOnls+KcnVh9qnqnYluWOSa6fl\nk5L8VpKndPcHlg/o7g9PP69P8prMpqt8ge6+oLv3dPee3bt3L+DqAADAYixiZPuyJKdV1b2TfDjJ\nOUm+Y8U+e5Ocm+QvkjwuyVu6u6vqTknelORZ3f1nyztPgfxO3f2xqrplkkcl+YMF9AqwMyxyhHyj\nU21G1QHgJpsO29Mc7Gdk9kkixyW5sLuvqKrzk+zv7r1JXpbkVVV1ILMR7XOmw5+R5L5Jnl1Vz57W\nPTzJJ5NcOgXt4zIL2i/dbK8AbAGhHeAmC/m69u6+JMklK9Y9Z+78vyR5/CrHPS/J8w5R9oGL6A0A\nALaLb5AEAIBBFjKyDQALZzoKcBQwsg0AAIMI2wAAMIiwDQAAg5izDcDRzdxvYBsZ2QYAgEGEbQAA\nGETYBgCAQYRtAAAYxBskAWAtNvpGy8SbLeEYJmwDwFYS2uGYYhoJAAAMYmQbAI5Ehxshv7l9jJDD\nlhG2AeBYJrTDUKaRAADAIMI2AAAMImwDAMAg5mwDAJtn7jesStgGAHaWjX4WudDODiRsAwBHJ6Gd\nHcCcbQAAGETYBgCAQYRtAAAYRNgGAIBBhG0AABhE2AYAgEGEbQAAGETYBgCAQYRtAAAYRNgGAIBB\nfF07AMDNWdTXvvv6+GOSkW0AABhE2AYAgEGEbQAAGETYBgCAQbxBEgDgSOKNlkcUI9sAADCIsA0A\nAIMI2wAAMIg52wAAx6KNzv1OzP9eByPbAAAwiJFtAAA2blEj5FtV5+b2GTBib2QbAAAGEbYBAGAQ\nYRsAAAYRtgEAYBBhGwAABhG2AQBgEGEbAAAGEbYBAGAQYRsAAAYRtgEAYBBhGwAABhG2AQBgEGEb\nAAAGEbYBAGAQYRsAAAYRtgEAYBBhGwAABhG2AQBgEGEbAAAGEbYBAGAQYRsAAAYRtgEAYBBhGwAA\nBhG2AQBgkIWE7ao6s6reV1UHquqZq2w/vqpeP21/a1WdOrftWdP691XVI9ZaEwAAdrpNh+2qOi7J\ni5OcleT0JE+oqtNX7Pa0JNd1932TvCjJC6djT09yTpKvSHJmkl+qquPWWBMAAHa0RYxsn5HkQHdf\n2d2fSfK6JGev2OfsJBdN5y9O8rCqqmn967r70939wSQHpnprqQkAADvaIsL2iUmumls+OK1bdZ/u\nviHJJ5Lc9WaOXUtNAADY0XYtoEatsq7XuM+h1q/2JGBlzVnhqvOSnJckp5xyyqG7/IJqq5ZLlpZm\nP/ftU+dQ1lNrUXVurpY66mymznpr7bQ6N1dLnWOzznpr7bQ6N1dLHXU2U2cjtRZgESPbB5OcPLd8\nUpKrD7VPVe1Kcsck197MsWupmSTp7gu6e09379m9e/cmrgYAACzWIsL2ZUlOq6p7V9WtMnvD494V\n++xNcu50/nFJ3tLdPa0/Z/q0knsnOS3J29ZYEwAAdrRNTyPp7huq6hlJLk1yXJILu/uKqjo/yf7u\n3pvkZUleVVUHMhvRPmc69oqq+vUk701yQ5Lv7e4bk2S1mpvtFQAAttIi5mynuy9JcsmKdc+ZO/8v\nSR5/iGOfn+T5a6kJAABHEt8gCQAAgwjbAAAwiLANAACDCNsAADCIsA0AAIMI2wAAMIiwDQAAgwjb\nAAAwiLANAACDCNsAADDIQr6uHWCt9u3bt90tAMCWMbINAACDGNkG1sSINIzlfwyOTka2AQBgECPb\nAByTjCQDW0HYhh1KEAD4fO4XORIJ2wBwlBFKt4bbmbUQtlk3dy4AAGsjbAOwJTxRB45FPo0EAAAG\nEbYBAGAQYRsAAAYRtgEAYBBhGwAABvFpJADAMWWnfTLOTuuHxTKyDQAAgwjbAAAwiGkkAAAcE7Zj\nyo6wfYwwHwwAYOuZRgIAAIMY2QYAOAp4FXtnErYBjlIeeAG2n7ANAMCOdaQPHAjbAADc5EgPtzuN\nsA0c8zywADCKTyMBAIBBhG0AABjENBK2jZfuAYCjnZFtAAAYxMg2LJgRewBgmZFtAAAYxMj2DmeU\nFADgyCVsA8AmGBSB1fnfmDFi5YABAAAgAElEQVSNBAAABhG2AQBgENNIgCPSTnx5cif2BMD2MrIN\nAACDCNsAADCIsA0AAIOYsw0ADOX9DBzLjGwDAMAgRrYBOKIYJQWOJEa2AQBgEGEbAAAGEbYBAGAQ\nc7ZXMBcQAIBFMbINAACDCNsAADCIsA0AAIMI2wAAMIiwDQAAgwjbAAAwiI/+A9hhfAQpwNHDyDYA\nAAwibAMAwCDCNgAADGLONhzlzP8FgO2zqZHtqrpLVb25qt4//bzzIfY7d9rn/VV17rTuNlX1pqr6\nm6q6oqpeMLf/U6vqmqq6fDp912b6BACA7bDZaSTPTPKH3X1akj+clj9PVd0lyXOTPDjJGUmeOxfK\nf7q7vzzJVyf5uqo6a+7Q13f3A6bTr26yTwAA2HKbDdtnJ7loOn9Rkseuss8jkry5u6/t7uuSvDnJ\nmd39z939R0nS3Z9J8o4kJ22yHwAA2DE2G7bv3t0fSZLp591W2efEJFfNLR+c1t2kqu6U5NGZjY4v\n+9aqeldVXVxVJ2+yTwAA2HKHfYNkVf1Bki9eZdOPrfEyapV1PVd/V5LXJvmF7r5yWv07SV7b3Z+u\nqqdnNmr+jYfo77wk5yXJKaecssaWAABgvMOG7e7+pkNtq6p/qKp7dPdHquoeST66ym4HkyzNLZ+U\nZN/c8gVJ3t/dPzd3mf84t/2lSV54M/1dMNXInj17+lD7AQDAVtvsNJK9Sc6dzp+b5I2r7HNpkodX\n1Z2nN0Y+fFqXqnpekjsm+f75A6bgvuwxSf56k33CYe3bt8/H5AEAC7XZsP2CJN9cVe9P8s3Tcqpq\nT1X9apJ097VJfjLJZdPp/O6+tqpOymwqyulJ3rHiI/6+b/o4wHcm+b4kT91knwAAsOU29aU203SP\nh62yfn+S75pbvjDJhSv2OZjV53Onu5+V5Fmb6Q0AALabr2sHAIBBhG0AABhE2AYAgEGEbQAAGETY\nBgCAQYRtAAAYRNgGAIBBhG0AABhE2AYAgEGEbQAAGETYBgCAQYRtAAAYRNgGAIBBhG0AABhE2AYA\ngEGEbQAAGETYBgCAQYRtAAAYZNd2NwDAzrZv377tbgHgiGVkGwAABhG2AQBgEGEbAAAGEbYBAGAQ\nb5AcxBuKAAAwsg0AAIMI2wAAMIiwDQAAgwjbAAAwiLANAACDCNsAADCIsA0AAIMI2wAAMIiwDQAA\ngwjbAAAwiLANAACDCNsAADCIsA0AAIMI2wAAMMiu7W4AANiZ9u3bt90twBHPyDYAAAwibAMAwCDC\nNgAADCJsAwDAIMI2AAAMImwDAMAgwjYAAAwibAMAwCDCNgAADCJsAwDAIMI2AAAMImwDAMAgwjYA\nAAwibAMAwCDCNgAADCJsAwDAIMI2AAAMImwDAMAgwjYAAAwibAMAwCC7trsB2Kx9+/ZtdwsAAKsy\nsg0AAIMI2wAAMIiwDQAAgwjbAAAwiLANAACDCNsAADCIsA0AAINsKmxX1V2q6s1V9f7p550Psd+5\n0z7vr6pz59bvq6r3VdXl0+lu0/rjq+r1VXWgqt5aVadupk8AANgOmx3ZfmaSP+zu05L84bT8earq\nLkmem+TBSc5I8twVofyJ3f2A6fTRad3TklzX3fdN8qIkL9xknwAAsOU2G7bPTnLRdP6iJI9dZZ9H\nJHlzd1/b3dcleXOSM9dR9+IkD6uq2mSvAACwpTYbtu/e3R9Jkunn3VbZ58QkV80tH5zWLXv5NIXk\n2XOB+qZjuvuGJJ9IctdN9goAAFtq1+F2qKo/SPLFq2z6sTVexmoj0j39fGJ3f7iqbp/kDUmenOSV\nhzlmZX/nJTkvSU455ZQ1tgQAAOMdNmx39zcdaltV/UNV3aO7P1JV90jy0VV2O5hkaW75pCT7ptof\nnn5eX1WvyWxO9yunY05OcrCqdiW5Y5JrD9HfBUkuSJI9e/asGsgBAGA7bHYayd4ky58ucm6SN66y\nz6VJHl5Vd57eGPnwJJdW1a6qOiFJquqWSR6V5D2r1H1ckrd0tyANAMAR5bAj24fxgiS/XlVPS/L3\nSR6fJFW1J8nTu/u7uvvaqvrJJJdNx5w/rbttZqH7lkmOS/IHSV467fOyJK+qqgOZjWifs8k+AQBg\ny9XRNGC8Z8+e3r9//3a3AQDAUa6q3t7dew63n2+QBACAQYRtAAAYRNgGAIBBhG0AABhE2AYAgEGE\nbQAAGETYBgCAQYRtAAAYRNgGAIBBhG0AABhE2AYAgEGEbQAAGKS6e7t7WJiquibJ3y2g1AlJPqbO\nltRSR52dUGeRtdRRZyfUWWQtddQ5muossta9unv3YffqbqcVpyT71TmyelJHnZ1SSx11dkKdndiT\nOurshDqLrrWWk2kkAAAwiLANAACDCNuru0CdLauljjo7oc4ia6mjzk6os8ha6qhzNNVZdK3DOqre\nIAkAADuJkW0AABhE2AYAgEGEbQAAGETYnlTV161l3Qbq3moBNe5YVadvts4me1jY7VNVx69l3bGu\nqm5fVbfb7j6WVdVtt7uHeVV1h6q6/QaPvUVVfe2C+/miqvqyTRx/61XWnbC5ro4+O+V2mv6Gvm2r\nL/fmVNUL17LuSLPo/9equntVPWo63W2DNRbyOLYTHw+r6t5rWbfGWpt63Kiq/7KWdTudsP05/2ON\n6w6pqvZV1alzy2ckuWwjzVTVH05h4s5J3p3kNVX1/2+gzvFV9R1V9aNV9Zzl0wZa2vTtM+cv1rhu\nVVV1XFV9d1X95MrAX1U/vo46z1h+kK6q+1bV/6qqj1fVW6vqK9daZ67e1y3fsVTVk6rqZ6vqXhuo\n85VV9VdJ3pPkvVX19qq63wbqfOn0d/Seafn+67l95up8bVW9N8lfT8tfVVW/tIE6X1dVb66qv62q\nK6vqg1V15Qbq7Kmqdyd5V5L3VNU7q+qB66nR3Z9N8jPrveyb6enRSS5P8nvT8gOqau86y1xWVQ+Z\nq/mtSf58A7389+m+Y1dVXVpV/1BV37HeOlOtW1bV91XVxdPpP1fVLTdQ5zZV9eyqeum0fFpVPWoj\nPWUBt1NVfclyoKmqpek63mk9Naa/oWes55hV+viamzttoOQ3r7LurHX2dJuq+uGq+qGqunVVPbWq\n9lbVT230yX9VnVNVPzadP3k7/1+nJ0hvS/L4JN+W5K1V9bgNlNrU49ii61TVQ6rqsqr6P1X1maq6\nsar+aQP9JMkbVll38Tr7WcjjRpJzV1n31PUWqar9VfW9U6bacru240J3kqr6t0m+NsnuqvqBuU13\nSHLcOsv99yS/V1W/kOTEzO7kvnODrd2lu/+pqp6W5KLufnZVvSvJD62zzhuTfCLJ25N8er1NLPL2\nqaovzux2+aKq+uokNVfrNuso9ZJp/7cl+YWq+uPuXu7tW5I8b411vqe7f3E6//NJXtTdv1VVS0l+\nJcl6R+5/OclXVdVXJfnhJC9L8sok/88667wkyQ909x8lszCQ2ccUrXdk56WZ/b28JEm6+11V9Zqs\n/fZZ9qIkj0iyd6rzzqp66DprJLPb4//L7G/xxg0cv+zCJP+pu/8kSarq65O8PMn911nn96eg9pu9\n+Y9l+okkZyTZlyTdfXnNPfFeo+9IcmFV7UtyzyR3TfKNG+jlrO5+VlU9NslHk3xFkj9M8poN1Prl\nJLdMsvwg+eRp3Xets87LM/u9/9tp+WCS30jyPzfQ0yJupzck2VNV983s73JvZrfPI9dZ581V9YNJ\nXp/kk8sru/vaNR6/HCBvnWRPkndmdr94/yRvTfL1aylSVd+T5D8luc/0OLHs9kn+bI29LHtFkquS\nfFGSN2UWln46yaMz+90/eT3FquoXM/sbemiS52d2O/1Kkgets69F/b/+WJIHdfdHp/52J/mDrDFM\nLupxbIGPh8t+Mck5mf1f7UnylCT3XU+BqvryzO4v7lhV3zK36Q6Z/Y2ux6YeN6rqCZn9r997xcDF\n7ZP84zp7SWa3zXdm9mR9f2b3Sb+/gPv+NTnmw3aSWyW5XWa3xfxL0v+UZF3Pdrv70qp6epI3J/lY\nkq/u7v+9wb52TXcCj0+ykZHoZSd195mbOH5ht09m/3hPTXJSkp+dW399kh9dR50zuvv+yU135L9U\nVb+Z5An53B3WWsz//d+tu38rSbp7X21sesIN3d1VdXaSn+/ul1XVas/KD+e2y0F7rp+NvBR3m+5+\nW9Xn3SQ3bKBOuvuqFXU2EpY/0d2/u5HLX+H65aCdJN39p1V1/Qbq/ECS2ya5sao+ldnfTnf3HTZQ\n64bu/sSK22hduvvdVfX8JK/K7H/iod19cAOllv+uH5nktd39sara6APKg7r7q+aW31JV79xAnS/p\n7m+fHkDT3Z+qDd5YC7qdPtvdN1TVf0jyc939P2r2atJ6/b/Tz++dbzHJfdZycHf/uySpqtclOa+7\n3z0t3y/JD66jj9ck+d3MBnyeObf++nUE/2Vf2t3fNv1+PpLkm6b7tT/J7MnAen1td3/N8u3b3dfW\nxqZXLur/9RbLQXvyj1nfq/zzj2M/k8895vxT1vc4tqjHw5t094GqOq67b0zy8qpa7ytjX5bkUUnu\nlNmTq/me/uMG+tnM48afZ/b3d0I+/1WN6zN7VXO9vRxI8mNV9ezMruOFST5bVRdm9ni93v+TdRG2\nk+/q7idX1Se6++c2U2j6JX5bZs/g759kX1X91+5+0wbKPT/JHyf50ykw3SfJBzdQ58+r6iuX78TX\nq7v/OMkfV9UruvvvNlJjrtZFSS6qqm/t7tVeplqrm+6ou/uGJOfVbGrMWzJ7YrBWF1fVK5Kcn+S3\nqur7k/xmkocl+fsN9HV9VT0ryZOSPLSqjstsRGe9rpz+ll41LT8pG/vdf6yqviSzB/9ML5V+ZAN1\nrqrZfMmeHiS/L9NLg2sx93L4H9VsKtRvZu5Vlu5+xzr7eVtVvSTJazO7bt+e2f/a16ynXndvaL73\nIbynZlM1jquq0zK7jdY7teFlSb4ks/uOL03yO1X1i9394nX28rs1mzp0Y5LvrdlUqXW/qjW5saq+\npLs/MPV4n2zsidZnquqL8rm/xS/ZaE8Lup3+dQr+5+ZzoWLd/6vdvaF5rKv48vn76O5+T1U9YB19\nfCKzVzCfMN3v3D2zx/fbVdXtunvd92dTwL5keeRvWt7Ik7Z/rapb5HO/+7sm+ewG+lnU/+vvVdWl\nmd1/JLP7j0vWcfyduvvfVdWPd/d6XyW8yQIfD5f983T/fHlV/VRm9/XrHaT52u7+zqp6Tnefv8l+\nNvW4MeWNv8vnXg3btKq6f2aj24/M7NWtX8vs1aO3JFnz/9uGLvtY/1Kbms0pOiuzlzqWsmJkdD3P\ndqrq55M8s7s/NS3fK8mvdvdq8+iGqtmc1s7sDve0JFdm9uC2PBqwrpfcq+pLMxtpOTVzT9K6e90v\nc9dsruS3rlJrTf/cVfXqJK/u7t9bsf67kvxyd6/5QbOqnprkezJ78D4+s5dPfzvJC6cHsDWbXhb8\njiSXdfefVNUpSZa6+5XrrHPnJP8tszuBSvK/kvxEd1+3zjr3yeemn1yXWWB/Und/aJ11Tshsms03\nTf38fpL/0t1reimvqv7oZjb3ev+GFlVvGrl7YpJ7d/dPVtXJSe7R3W9bTz9Trf/L3pnHW1eOb/x7\nvYXSRMmYBkmhkZKUIYSiDCkSEjLzJqISpSJSfihKhko0KlJUmtM8zyIa/PIzpkilyfX7437Wu9fe\n7z7nrGet5wzv274+n/M5Z6+9172evc5az7qf+77u634skZ5+Vdp0KrCX7f9k2Pg4EWWtnJIlgK/a\nfk+L8TwR+EeK3i4KLGH7jy3svIJIt95M/O+XA7atZ14a2tkI2BV4DnH9rA+8y/bZLcbU+TwpCs4/\nAFxo+0hF8ddbbH+pxXhWJb7XnDR7i3v+SIJe8UNi3n47sKjtrTLtfISgNP2FnkObNd9L+i6wve1/\nD2xfkaA0NqK21PZ7J/BGgtrwfSIg9XnbR2XaKXm/bk5cgwLOrbKaDfe9yvaakq6w3YZXP2iv0/Ow\nZmc54v/+aIKutwTwrRTRbWrjWuB5wMVdv1vX50bNzpuALwNPTHZaZTQkXQ7cRdDGjrN9f+29422/\nacydC2DkbEsfI5ytZwB/pN/Ztu1G6cBJGNeyRPHN8vTfgI0uCE1QmJcbpU6p44MY4NvavjzHTrJ1\nCj0eed1WsYK16YSkxen/n01qemoiKCgos2y3oVqUHMczbN880bYpHM+BhEPyctvPToucX9rO5ZIi\n6cXABSl9W217XouofWekCPJsYDnbH1TwkldySwpPcgZWJubGG+sPqUw7SwEvTHYusv33NnZKIZ2n\nZW3/poON3YggzXOI6OjGRDYyi2KnUFj5IJEVhVhgH5izWEt2fgesm+vQZNiXWzgNkp5Lz+k63fZ1\nLWwUu1+7IC2M1gOWBn5ff4t2gazOz8OUzTjM9ttzjj3EzleA9xER8Xvrb9GeYtcJ6Zre1HbjqPgY\ndoY9f1aw3SZrnH/8R7qzXUHSgbY/2NHG0sCnmTvK0Sb6exVRXHcttZSb7TMy7Rxu+x0TbWtg53Lb\nWRXk49i6zna2usYENg+2/b6C9jayfVrmPu8nKCn3kVKmZCzYJB1S228Qbhq5U38h6zBDXx3v/SH2\nvjFk8z+By2yfkGFnrkhQm+sqRTJ3o+eYnAPs0SITcYUTl9T2Wmnb1e7nKDe1dS+hPLSl7b/U7WfY\nWIng3A7OH1kL/uQMXAu8zfaqKep+fvUdG9p4ue0z1V8kNQe2j29oZ9zv32YxUuI8KdRj9gUebXsF\nBWVjD9ubZY7lWmAN4Erba0h6EpHN3HSCXScFKeuzkYNeV9Juq/k1OYFXtLmnhtgqcr+WiJSmLOap\nwFzXS4tAVpHnoYIas6ntBwrYOsH26zvaOIyIZN+VXj8e2M/2u8ffcy4759suIcNc5PnTFiPOdg+f\nkbTkWG82jE7+iKhKfy2RotwG+FvL8TyQ6xSNgefWX6TJr83FdaKkDwE/oZ9v2yZq24lHPgbWLmgL\nItW0bOY+nwSe2yFiN0yZYVlge/KUXypu48pExX9Vyb0pETHLxULAKkSVO0TK83rgPZI2tL39eDur\nbIU7RCr6OiIdDaGOcAihRJODB9P9UNERlqYFlzThN8BXCO74e2xfAFnFuhDfYTeiin9DglvYpohw\nJdtbSdoCwPa9KQWfg5cSPMZhTqMJ3n0TFFHbGECJ87Q7c6vHtOFf32f7v5IeShmtv9KwOBJA0jGO\nYsSK9teH3CgpQfc5W9LP6Z+nuz5LWs2vth+WdIOkp7kFjWkApe7XfegYKXUIH3ReQCSUeh7eCpyv\nUO6oK+Nk/++7OtoJq1eOdrJ5p0J1JReXSTqaoHfWr+mmC/7Sz59WGDnbPVwBPJ3gtoqoxq2KSppW\nly/lUKCY7V5h4Tktx7O/QhP5VPovsEZVuIpCvV0IWaFKa1PAAwSPNxeVqkZderBx1f0ANgDeJekW\nOvDIB/DXiT/SD42tgyxCTiwXv6c/9ZYF14pkFHzrXYjo7ZcI57+pnc8nG78EnlfRRyTtTs9hzsEz\nidTtQ8nOgQT/biMigjoRila4E8oWm9defz5lgnLxDWLx+CSFusWbCV5xG9j2SZJ+AxytqHDPTRsu\nbPuMlKq/DdhdoQCxW6adBxItoXJKViDu+8awXR1zj8E0a45T6nJqG3WUOE/D1GPapHkvU+hzf4eg\nAfybkCRtiqo5R1vN8UH8If08mloheQFkz681PAH4taQL6XcCcxfHpe7Xv3SlJMCcrMawayb3eVbq\nefh/6WcW/cph2VCoO4333ZpkAWZJerxTrVEKZrbxORcnnquvqm3LWfCXfv60wsjZ7uEU4Ge2fwEg\naWNC8ugTGTYeTL//JOm1xIW/TMvxPIvQst2YWqELvdT5uLC9N7C3pL1t79xyDHV7paruIbPJwliQ\ntFDFaXSSN5T0hIzI8ouJQqR/D2wXEfXKxc5ElOJi+hdIH2tqQNKziUK7tYhI6Qc6pISXpd/JeoCo\nAcjF0wgOX0XTWAR4aopaTcjfTVSTEyStZ7tN04dB3CdpA9vnASgaG92Xa8T2jxRFM69Im97Q4SGs\nZPMmBX+7je73fxSqDTcpCt3+SKS6c7EHMZ8tk1K5LwWyiywTjiMKpur4MfnZsU5qGwMocZ46q8cA\n2P5Q+vMgBfd28aYBkbT/n9Lv2xR1NivZPl3BJ89+PlcL7RIoML9WyC46HYaC92unSGkNVQ1EpRq1\nNeEUHpZpp8jzsOT/nsga/Zn4blVh6mK298mwsR/xPKz0y7cgVNayYLttr5Jq/9LPn1YYOds9rGP7\nA9UL2ydL2jPTxl4KPukniO6KixMUgDbYEljeLYuRKjiaWzyeUCSp8xuz6ASSPgz8aIB/tZXtNh2h\nngJcX4u4LkbwL3OlBS+VtJ3ti5KdzQku57Ma7n8RcG/KQvQhRShz8W0i9d7Hs28KSVUzgn2JavKH\ngcWr6FsLys7hhEzeT4iF2huJOoBc7EPISZ1NTLwvAb6oKLw8PcPO7yTtwtxFv1kcPoKi9YN0r0Fk\no9romUM0j6hS0wu3tEGdD237HmBLRZFzDrZP4/kYsCdBkXhn050lzbL9X9unJKfkRcT/a0f36wo3\nsVU69fprhcpFXW2j7cKm03lK+CixqL2fkIA7NdnKQqLnbA08w/YekpaV9AJnKmRI2o4oTFuSUEZa\nhihIf8V4+w2xcxqwxcA8fZTtV+fYSeg6vwJRZ6RQpqioKJd1oNqVuF+7RkorrD/AJd5JwS/Olcwr\n8jxU8PWHUZHaNMZ6te11a68PTEGkxs627R+keWhDYh56k+0bcgeiQtxvyj1/WmFUIJmgKC74Ff0P\ng5fkTFKS1rd9/kTbGto6luhw2KliXyGHN5uYvK8i1AAuzL0BleSOBrZd6Yyiq/p+BL2hSnPPIibg\nLKkhRUv17xO8y6qT3HvdrhFIZ0i6wHZul8f6/rdSK6ysNlev3UIZR1Gg9uL08lzbbRp3IOmpBDf6\nRiKyfXuLBdsFxD02WHXfWGM2XStvtn2MgiOL7VYtiRXa7FsQ0VsBbwCOdYZ2rqRP2d5Hw4tIc7Ma\nW9g+dqJt4+x/FTFndI7eKBozvYEoAKvTre4mnLdcDfEiahvJVqfzVBIqpJCR/ncvICTXquK/a22v\nlmun4DxdZH5NTvr/EPe+iEXgx50ht5fsdL5fGx5n55QZnuhzVwEfqWXYXkRI7WVlbAo+D+vZpoWI\n2pqHbH8qx06ydQHwTeAo4lm0FfDhNs83hQxpPdCXpfk+7Pptc02XeP50gu3RT1zjSxKakFemn68R\nLdNzbFzRZFtDW2cSXSh/Tqy4jyfa1ObauZa40K9Kr1cBjm5h5xrS4iy9XoBYjbf5blcNs9/S1hsI\nB+D/gGe2tPEkIlW+FvCkDtfQF4jo1FPS9bRk7jXU8DjPbfi5ZYf9tDjee9N1dCdwFkHZOLPE/73l\n9z+3kJ1fAwvVXi8M/DrTxqbp9zbDfjJtdZo/gHUJvvB3gMcXOkfrlbBT8qfLeSL0uQFOJBYRfT9t\nx0KokVTbrm5h5+K6HSLylj0nEo7EsrXXy7V9BqX9S8yvV9fn1TTftjlHne/XttfXGJ97fvput6af\nqwinOfd4xZ6HQ+yc03K/5YETCB/kbwTlZvlMG5sBNxE8/VuIRWm2z5DO8eNrr5cEri1xnqfyZ0Qj\nSXCk6GfDHMWORdwwYiZpPWK1vrT6ZdcWJ09Foo5sbtMY+I/t/0hC0mNs3yhp5RZ2TgWOkXQQsdL9\nAMELbYObFfrmB6bXHyKq6LOgjp3kFJXRBxLi/1Wl/DKS7gI+5HxZsrel33WOfNsi0vFwOHPzaIfh\n5/Qi5AsDKxCqGc8dc4/hmE2omlzk6Jy2CtF0JxcnSdrEqS6iA06T9ElC+adecJVLs7mVWIhW0dXH\n0K+bOyFsn5h+z+FppujmXU4z/ERQ1IdsAjxtIEK+ONCYr2/7YknrEvfmZZJOpl82tHGUvYYrE4Xs\nufRHp3Llu25heIo7R7mjxHmq+LX7Nj3uBCilkHFOSnEvrGgA9CFiQZCLzwDnqVeY/xIiAJCNrvNr\nDbOc5DAT/kZee/QKt9Lxfm2IRso2jh4Ta6QMm5wpPVpDqedhXU1tFrEYeHKbATkan3VVJNmTyKSf\nbnstSRsSEfJc1LnfJii2bfyjUs+fVhg52wmSjiAeUg8T0YElJH3V9lca7P5ook34gvRXAf+LqJjO\nhsvx3G5XVMv/lHBS7iSiFLn4NPB+IhVcdYP6bgs7EOf5G0QluYEzaPdAuI5Iaxq4RdILgRyZo0OA\n99u+uL4x2TmETGknly0iHQ9NHwZ9KehEKXl/i+OVWrDNBnaR9AC9YmI7v1FC5eh9uLat8aJG0v7p\n8/cD1yeeqwl1lfNyBpJS28ekc/IYomhqTeAhSW+z3YTT/n/AZUQkqN4k6m6Cu5+DJYmF0d+SrbZS\nhhUOJ6hDryYKL7emHde6Lh23EEEHGFNqdQx0Pk+2L0/O8Xbu2AAkoVLIeKK6KWTsRBSxXkvco7+g\nxfzq4Os/j17zoI+3fG5A9/m1wmmSfgEckV6/lXh+NELJ+7Uhmi6SZxPPibuB76TzvpPtxt8tYdjz\nsI1KxuVpfxGLz1toWRitaPe+F5HFPIV4Fm5v+4cZZh60fYekWYp6krMkfTl3LA7u92XAy+nA/ab3\n/LmfeP5MaaOeEWc7Qb0WrFsTK8JPA5c7r83tch5H0F7S/rY/2tBWEZ7bgM2XElHcU1xA+H5eh6Sb\nbK80xnu/s/3MTHuPop+XejbwbdsPjrlTC6hDm+A2+yoKLLclCtNeTtBJHmV7kzZjmG5IGreYsh6l\nbmDremBV25b0PiJy80oiEniY7caqNpIeNd61Iuk490seDr7/AUKa8yvEddd5cq+4kZKusb16usZP\ndbuiq0Hb5zmz9Xfar9N5Sp8p2QBkFaKQUcAZbqloI+nRBM3PwG9yxqZJaB5UCpJELK42IM7RucCP\nMzI/xe7XhsdrxAdWamIBmqcAACAASURBVKgj6dXEwv+zwCEt5tcitV6qqcfUtj3GLUQWav7QGwkq\n0ceBs5zRQEjS6WnfLxF8/78SQhSNed+Jv36NCzfBmw6MIts9PCo9SN4AHGD7QUlZD6vxHO2EnC5I\nnyMuzKob3ZOIaEC2s50m4g2ISfz8Ng8YSa8j0kLLEddN61WhQqf3o8xdFZzbva1rJ7mTFc0ffgD8\nb9r2dELZoA1F5kDgUUCl0PKOtO29LWx1xgClaRZBPclusmT7jenP3RUV70vQkkIkaTNqixHbwxr5\njLXvSkT6f0UiAvhJt2iUUfjh/EDNaXg1UTz4MKG+kTW/NliUTXRdv5jgWI+rPCLpubavbzisakx3\nKbSx/0wL+cgBZ3AWEelupQVc4DxBoQYgkvYlHKxcasWgndcS6iO/J+bWFSS93/bJ4+85B+O19jax\nSM4dU5GOpsSceqLtY5LdhYmC/f8dd6/e8Yo60w3QtNC2yjBuQlwDV6eFRS72Z25a4LBtE+GCIftc\n2MIOxHMM4rsdafsfLb7a6wnKz/ZERmwJIjvWGI6GUVdLWtaZhZWDkDRUNtmZhf5tMXK2e/g2MQFf\nDZyr0DxtpXJQCEV4bupVcFeyRodIalPB/TWiQ9+1BSJmPyWatJxItzT3IXToJGf7Y4kH+npCS1rA\n7cA3W/K61hlY+Z8p6eoWdiZC08VS3Zl5iOBwd6q89hCZxKaQ9CWC4vCjtGm2Qi97p4Ymvk8sjM4l\nqAT7k981sj6eEgvI+5MT+hfiGqw3anls27GNgXHvO9tbN7TTlPMPcLCCg74rUUS4KBEIyEXdGaxS\n3FuO8dmuaDI/lWoAciNxjhYk5qMjW3J39wM2tP07AEkrEvdrI2fbqXlQYXSaX2s4nsjMVvgvMQ9l\n9TIoFfBRNAz7OrBeGsuFRNb4ZsLgFxuaulzROGwFYGeFZF/j55kK1XopWsc/jeD7r0Xvf7Q47eeg\nEyXdSNBIPqSoRchSDrJ9TxrbC4B/EBmxO1qM5SkEfegS+hfGWcE5+hvyLZTGdTktFqKt4GmszpzJ\nP8QFu2Dt9TYFbOYoC+xH8Pbenn5OAvZtccwiFdyECsWsQuf24kJ2Lk+/r61t+9U0XjNXEN0Nq9fP\nyPmf1/Z73pCfFevXY0M7WzTZNoXn55r6NUQ8UBpX3TNQTd7m3A7s/zui+EsdbKxLOFx3AJ+tba8i\nQkWvr0J2rixhJ/OYzxiybYVJOlbOPLsYsGiBY65MpMtvI7jJG2buf+7Aaw1ua2jnUYT++I/Tz0cI\nyleb71Rkfh28b9O2Nmokne/XZOciIuu4YPp5e5tnEr1s4ePS66WIFuXV++OqRhHNpnYD/pR+Vz87\nEM2Nmo5jm/R8vjv9rn5+RvCb256nxwMLpL8XAZ5ce2+jBvu/l+hmeijR6OdW4N0txvHSYT9droFk\n9+ml5+jxfkaR7THg+G/UK9xnk98ZahA5UYFP0s9zO4yYPHNxK2UquD8F/EJR5V7vutWmYObrknYj\naDF1W7m8wk6d5FKh1HuJlObJrmkHS9rV+dH/HYGzJN1M/M+WI6JBufgWMYlfk+ysmv5eStIH3LwA\nZ2fmTokO2zaVeBwR5YBIK+ZgoYHITV8kp8X187/AdelebwVHce0qQ7b/glgsl0SbqOIwNOXKLkBI\nbv09vX408WDfwfazM4/5Y8p0omyCCc9TykYcTirSlPR34J1uTq+p21qAuAZWIaTSrgZ2SDSQtzY0\nc30qIjyG+P9sQTSVeRNkdTcsSWUr1dH0DtVUIFKEOlc5CArcrwmyfXjt9Q/T98uC7f8SAZbq9R3E\norvCuBkkR5bwHEmHemIK6njjOAw4TNLmLqgZ7dRmPf19D7WoMvBl4LQJTOwIrJXOC5KWIqgu388c\nR+ts6gS4nXi2TglGznZzNH7QSVokXZyD+HpTG7Yt6UxitWpCjaTxJKPyFdxfINqaL0Sor3TBasRD\n4OX0t6LPTed07ST37bT/JcD+ks6xXaXz3kRUYzeGQ0FmJSLKJeBGt+sAeivwnurBL+k5xMS1J5GS\nHdfZViEpuUnA3oSU3FkwpxPlzuPv0oc/0a+G8Ofa6zbXT7EFpELx553MXYfQRm5vLHy6oK1xIemt\nxP1xj6SbgN0J5+FSgn/Z1E7pTpRN0OQ8HUwsGs4CkPQyQp88q2mHpK8SlKYzgC+61znyy8rrQrsQ\nQUV6aXr9N2IhsCl53Q1LUtlKdOqEUNs4UlLFa/8bGddQDZ3uV/Wk8c6StBO9hi1vISg7pTGuzyDp\na7a3Bw4YVh/mfJrESZLextxzUG5HyyZo4g/dTvgvFe6mIU+/70ChgrM/8GzC91gAuMf59KHKJ4LI\nSqxJLIynBCNnuzkmdHQVHaS+S/Aal5W0BiEt9yEA24c2PZjmViM5SFKOGsll6ffl9BdVnt10DANY\n0varJv5YI7yRSCt3VQJY3valxCJgWwBJWwAXj7tXDy9wUpuRdADwLUnHE4oS2VHEVGD7fmoFgJLa\nqJGsUo+w2b5B0lq2b25YpFJSSq4YbB+paPm+DnF+P237zxn7l+alllxA/oJIT19LZh2CpGsZPr9U\nnNTViT9yJcXGQpP7blfg+bZ/l4obLwTemjH/VFgZeB2R0di0tv1uMuXNxjlPAGSep0UqRzvtc7ak\nRXLGk3AtsKvte4e8l8NJ/oTzdeKH4WFJK9r+PczhJz88wT5joev8CoDtm4C104IUp7bbLdD1fq1L\n40G/DKqJBUVJTOQzlNZ8PwH4J/E92wR5ctAk8PdH4GJJJ6TPvx64pOKnZwQ1DiDkIo8lCqvfCQxV\nEZsAl9X+foigkGR3926LkfRfQ6iBHJCkiwmd1Z+513L3OreQrUnRiFd5QI3EGdI7DY8zoUxW+tyX\niK6BnR/4ko4GPuoJVBMa2JlLxm7YtnH2v9H2KgPbPkeoSjzRY8gCjmPvu0QKt6IbvQN42HZWCjed\nn38QkReIyMsTkr3z3LAVtKQFbU9nJHsuSFqduSMvTaN2dTurMrdKwg8ybVxme+2JP9nIVhc5xuXG\nez83xazhMnD/BG5rej0Mfp9h90rmmNZzxzbytfNU6atXzsrWwL05ETyFnOUVNRtvB9a2/YbMMR1H\nFHufkmgFrZCyB1cRRYknt6VKSHpFstFHZasvLDJsdZ1fNyFoH39Ir3chWojfRhQk5l7Xxe7XqUCX\nOaHl8Vr5Gi2PNeF3S1TRMWG7UWO06v+uJD+atl3gdq3jH03IskLIaxaV5R0Po8h2czRaAdn+34Ho\nY9uoQqmuWxOhqYzTh4FPqXtDEoh2vTdKupT+dGCjtFlBmsRlkl5je46Mne09JP0fvW5eOSiVwn0X\n0UVse+KBeR7B4X+QSOWOC0nH2N6SoGwMS0821o4vCUnfJwqcrqefPpTlbKdJ/GWEs/0LYGPiHGU5\n28Dpkl5VKGJ8uKTtiELm+jU9YbSy7nQkh3Il26crJNLazNElOP9PVL9CwqL11xmp+0/Z3gd4m6S5\nusfl0Gyq86TQIK7LqO4k6XzyZMXeTXRBra69c4n7LhcHElHf/SUdCxxq+8YWdp5F6LO/O9k6Otn6\nbY6RElS2gvPr3iRajkLa8N3EwmgtgqL0mpxxUeh+lTSUCpO7WG+ARplbSesTNK1BlZVcicULJK1m\n+9rM/drg1ok+UHemldlVdwD3Jif5KkWznT8RBZtZSFSxqlBTwNMlbeMpkv4bRbZrSBPCYFvinGjJ\njwkO6QFEB6+PEdGSpkUydVv7ERyletet39j+RK6tCY4zpavvdMyXDtvuhoUQiZ6zJvFwrcuQ3U0I\n7985dMeWkLSR7YmKQZB0BaH2UU/h/ngazu9TbP9prIhpbkSpFCTdYPs5BexcS3Q0u9LRVOJJwHdt\nbzrBroN27iYm7c4dxRQtzb8A3EUvxZr1wEzO+vsIytaKyWk6yPYrMsdyFLCnx+D8216zgY1SUalN\nbZ+oMRqTuIWGsqSrgI/YPi+9fhHwrSbfq2ZjC9vHTrQtw94SBP3sMwQv9TvAD9tEzhRtrX9IXJtX\nE10JG2UFVKCxVqn5VakxSvr7e8BNtr+UXjdqHDNgr8j9quDuVliIaEh0he1G3Z7HyBzNgTMLtRUS\nex8n6B9zgnPOlMmTdAPwTEJW834GaGi5aJs91DhddYGmXXXr9pYj6hkeTZynJYj7/XeZdi5Px/9N\nev0sgkoyGUXacx9/5GwHJB1EFINsSPCu3wxcYrtxu1NFe/WvExGKqqX57NybJtmaq+tW2wfBBMfJ\nSQu2bkgyxNaTCO4uxHnOppSoQCe5hsdpdI7GSOG+2/aZmccr0kxC0pdtf3qibVOF9MDdz+1a7dbt\nXGL7BWny3JBwAq6z/dwS42w5pt8D67p9a+zKiXwBIUNW0dCutb1arp1Bx1O9jnBzvTevQdLzCUWD\nSs3mLuI+a+zkdKVIDOy3FEFDeQdRL/EjYt5ezfbLWtj4C0FN+RnhpBxre4WGdopQ2ZKtrh1NryGC\nTveRdNWdCkhLLbxLIC2UDs/IrI5HybEzu6tKutj2ujn7jGGnWHBlrOxhkwWJ5u6q+zZiQZPdVTfZ\nWwS4z4mmpVD/eYyH10mMZ2cODWW8bZOFEY2khxc52hFfY/vzKbLcOL2dLoB3uHljiXGR0i3HpB8U\neIvto0vYr6FRxZ26NySp29qSaCl9djr+/pJ2tJ0lbdggWpObhhsLjc5RiRRuwiGUaSaxEXMrM2w8\nZNtU4TDgQkl/plvk5TJFsdV3iGjQvwlFmSyobEex64GsyX8I7rf9gBINTdEopU005DeSDqSf8//b\nFGXKirSm6M+BwJNsr6rg3G/mTFlMSScy93f5J1G09G0PtJkeD7YvB9aQtDgRMGrcRKYgRaKydzwh\n+Xc40f79T+mtoyVdNvaec+HCZOMNtm+vbb8sBYKaopgaSYH5dX/gSuL/fFPN0V6DUBLKQuH7tY57\nySi4c/lC7bMkfYXwN1pL4dq+TdIGBA3tEEUjmkVbjunN9LKH21bZw4b7DnbVPdItu+omnEEEMP+d\nXi9MBDJzOduXpYBPvU7j8nE+XxQjZ7uH+9LveyU9ldDLbBRNALD9sKTXEw5Sa0halEgDPo2IbJxF\nVE1/imhQU9rZbup4bQKsWVtdHkZMpNnONpFqXaeKZqdJ4XTa6YiPh1Jpm8Z2knN9DQT9RMFZ3Sjz\neAsnx10pKrG7pF8RDviEkPRBgvP9jBRdqrAYDWsPJgnfJyJt2YoddTip+xAKPacAi9u+Zrx9xkDJ\njmIPE5zCs+h/YOZI/52jKCJbWNJGxP/wxBZjeRcdOP8D+A5xnr4NYPsaSUeQKYtJZHuWBo5Mr99C\nRHCflY7xjqaG0qJhc1KhbbU4cTPKX2mlngPGylw5r5hvV6d25hUqWovtL2fYKalGMhHGnRdtf0fS\nqUSNTt1x/DvB385Fkft1YOE3i4jeZmeNS1B2EqqodkVnEC2kTFM0em0i2HMIkeH4IbD+ePuNgfsc\nrdIfSovav9I8eFW6q+5CtitHG9v/ltTGzgeJ2rOPEef4HNrVZrXCyNnu4aQULfsKMTGY5iu5Cucr\nJOSOpr+taM4K9fC074XEhbEj4SRtabtxpESTIyfWpSFJHbMGaCN3MDnFn1MCSS8HDgKeSrSi/yJR\nrCeCx5uLrs0kjiB4cnvTvxi622XkxdriD7Z/1tVIolhtTchH7iFpWUkvcE/juBE8wPGW9HRgn5bD\n+mn66YKdgPcQi5H3E+nb3DkI2/cRHWj3G/L2v4dsGw+PtX2J+ou+2yjcrGW7Hpk8UdK5tl+S0s45\naC1xZvtq4GpJR1ROkaJ46+luV+vx7EQ/uatmayvb35pgv0HsRMpi1tCmAVW9sRbEgqRNY60isP0H\nSRs6JAQr/In4bllzY8H7tS619xCh0nP7WB8eB6UaCJ09ZFubQNEbieLTKwBs/5+ihXwbdMkeziYC\nZ0sD/2P7FgCFOs2VLcZyj6TnVX5UopHdN8E+c5CCeUsn+uJX00/FSV+cEJ+YdIyc7QTblcbmcZJO\nIlZTjdOTCVVaox5hyV2hPrPiaKb04d+B5Wz/K3Msr8v8/ETo2pCkjlNSxKMe5SrdbQ/aUS+G4dYJ\n3t+PKGy7kKBpXES07m7cxGgAg80kXk507muEdN3+kyjYQtITiUjQopIWdZLimgbcmKKiJ9If/c2V\n/vsWERl/OXGv3Q0cR68GoC1adxSzfZi6y0otDHzf9ndgDjVtYTLpKSrE+U/4u6QVSQ9/SW8mnKVc\nLC1pWfdk4JYl5CyhoWpDDcvYzlWyGMRpihqUBQnJvb+pv6lVU2xnu2rWgu07FYWujZzt0rQWInP1\nbYIjS/q7k+TiOGg6v742XTfvJRr1HFpoTG3v101cppalFGWnvgBeiHh2/7qFnQcST7q6V9voxgPd\nsocu31V3e+BYhUoYwFMIn6Ep9md4BPtpwC4Ep3zSMXK2E1Ja4hPAsra3S9GyFzujCLAQl2vOAzpR\nU25p4WgXV5xwx4YkA7Z2VHSTq4o/D3Z+swwAFPJoyzpVGA+g0eQpaU/g804axClt9nXb26bxvmm8\n/eMjPjv9/VNJf+vgaFOLAs1pJtEGkjYlVvFPJdKAyxGT+HQVEi5MONn15kjZ0n9EIeLzJF0Jcxyc\n7CYXKthRTGVkpUpxE0tx/iGyawcDq0j6I1Ho9vYWdj4BnKcoJBVB0ftQcghyFUlKSJwtYftfkt4L\nHGJ7twHKVVPMSnSvysFZgLyGK6VpLT8A/kWvQctWRLZ0ixa2isyvtrdUdDa8lohIvtMtWnAXvF9L\n1bIUoezY7stASdqXoJDm4hhJ3wYelxZ87yYi09kolT1UR4W39PlLFZ1o67VQOYGM1YZdb7ZPVdTm\nTQlGznYPhxCT3Xrp9e1ECq+xs62QvJkLmRfXGpKqVL+AxdLriv6x5Ni7Dh1TkVanCevQ46f9l3Z8\n0grnEwsL06K4DeY4k/sS32sFSWsCezhVlWdQZBYkOl1tCzyZOF/7j79LHx6n/lbUqr9uGrnV8CKy\nOXB++969CDWA022vpZAUm0vreKpQLV7GgqSdbe/dwNSDyampHJylaccBL9lRbD+iCVWfrBQ9HmYT\nlOImduL812H7ZuCVySmeZfvuifYZw84vUsR9FXoPzKoo8muZ5jYA3iWpi8TZgpKeAmxJ1JC0xamE\nk3MQcT1+ADhl/F16qNFa/gH83B0a4ySsPBBtPatltLXY/Jqc0A8Sz4tVgC0UikKNqQAJne5Xla9l\nqVN25jQQamFnEI+lRXG/7X0VtR7/IhzTz7mBZO0Y6Jw91BgKb7kDSXPgDkSGfztJK0laOSMQ+qiW\n7xXFyNnuYUXbb1FqvGD7vrS6y8E9tb/bpoO6to0exLBWp8/MNaK51Ug+JulFtrOpJCqkRkI0AnhB\nsoPtqyQtnzse2ztLOoNoQ3wn8BLnaXieQ38r6vrrnMhtqba9FR60fYekWZJm2T5LUk6x1VRjC4L+\nMBG+AfyEaLzyBWIS3zX3YG6h8TwOHlWP/tn+raKAKgeduIk1dOX8z8FgAEF5xYiDeD697qGrS2rb\nSGTjFvsM4vOEo3xeipw9A7iphZ1PE/z6D8Icuddsnj2RFv+aoiPlIbbb0AggqH4vtH0RgKR1aV8U\nvTsF5leifuSjtn+ZrssdgUvJpIAUuF8b1bJIerwb8PddSH1K/fVVCxBc55z+HgcAR9i+IDnXbR3s\nOkpkDzspvNXQNRB6k6RNEo1lDhQUrpvH2Kc4Rs52Dw+klFkVLVuR/OKbEumgiXhWbSglv5O0gEN+\n5xBJF+TaYGw1kja87VJqJA/Z/mf+mqgfCkmprxMT3GrAAZLebfv/xt8zMFHENgOfs/0KldPCvkuh\nbnMu8CNJf6UdD3Sq0FRi8UcKje1XpH3e0MY5UbnObTC3rNTW5MtKzaYbN7FCJ87/AEoEEJB0OLAi\nwY+uUu0mv+tnvZNkVYuQM47q3lI9Ep4i+Nma/Gk+PJCOqga2366gr21FzNEmnIwjM7MJ6wLvlFTV\nZSxLSK5dS370v8j8CrzAqf4pna8vSzoh10jX+9UDtSzj4AyiA+tE43kUsdCao0YiqY0aSb2+6iHg\nLxWlsSFuAvZLmZqjiWvmqswxDKJE9rDKXLVSeKuhayD044QAxpb05uS1Cee9dG3bmBg52z3sRqT/\nni7pR4Rczrs62myTDrqeuMCHXUwmJs8cFGl1mjDT1EiuU3ABF0gRho8BbRYS+xKdH28ASBSQMxlS\n5DEROnLUnqLorrmZogtg3zXgTN1V4PXEhPdxwvlbgryW1lONCSvwU2TsGturAm3aYtfxPYZ0bmuJ\nQVmpc2lYJAdzvtejiWuuLTcRKMf5T7ZK8UnXBp5TcZu7QFHYuB/tahE2kbQr7ZQ+ho2l2ILNwSE/\njuDqb0+oS+wo6Ru2m9LauhaO1lFkfk0O+yoMFOySf/+WvF/HQ1NHrogaiTvWVznqg76uaGrzVmKx\nthBBYzvK9m9bmC2RPTxRcyu8teGQdwqEpizjakQhZJVNOQd4vzP0/bti1EGSOcUAyxBV/y8kbraL\nnNkNbox00J4ZE2VxaHir0286FXU0tCFiItmT0P2eo0Zi+6jx9h3D3leA1elXI7kmN5qbuFyfoVdw\ndyqwV+4NVIv617ct5fx2uZ26kCoq9t9DcFIHZR7tzM5k8xrUsIVzWgzv7I6qKirUua1m79GEo2xa\nqJFIutD2ehN/csz9S3P+hx3j8cQ13bgJSNrvWOBj7jV96TKGq4lofV8tgu33Ndj3K4Ry0CLEfF9p\nGrdt/V2q1fZmxMJoRSI7cpjtv6Y57te2h3YHnEwUnF93TTZWSTZeTdB3Jio8H7RT9H4d5zhNOwZf\nPcCPH7ptOiBpLaKvweq2F2hpYxV62cMzcrKHKXjwQtsXpNePoZ3CGwoe+q7EYu2XpECoe6IERdB1\n/p3Q/sjZDki63HZOMdMwG/UJsU06aNDeEsTkW4+SZkUWJM32gDLGsG0N7FxOpFwqNZKL3VKNJNmr\nq5Gc60w1kpTi+pLtHSf8cDN7naumEzdt9drvRYHjbb9qwp377XzWPSnKbEi6m/E11tsUx046JO1i\n+4sNPncmcR1eQr+efZYzqahDWICOnduSrZcxoEYCbOMMNRJJnycaIh3fJgKcsiJjwu0UIIbySW0f\nkGnnLEI94hL6z3X2AkDSZbbXTk73Wo7mG5c4ow20pBNsvz732EPslGq1/QPgu8OuF0mvsH1G12Nk\njqfY/JquoTWBK2yvkegO357O+3WC4zR1tq8gMqJ1NZIfN9l3MpBoLa8hotuvIKK3R9rO0v8fyB52\nGU8x51XSUnQIhDY8RqNgT1uMaCQ9XCRpHfeL7+diL9t9ndAkHT64rQkkvYeowH0aIZm0DqHf/LJM\nU9sQfOQ63jVk20S4iNC37dSUJE3ip9p+Je2KJYA5soidFke1MRWpmqYcR21NRQOAU9xCncB220YG\nk4rE+9uOXpEcALbfnX5P6GgnfL7QkConqd7pL7tzW0IJNZIdiIjrQ5L+Q/7iqDTnH7rzSSvsXmY4\nQIFaBNuvV7SgrtQVLrbdprlFkVbbwJ8GHe3q/zjVjjaUnV+JboQPK7oRLka0am9TF1Hyfh0PTWkk\nM6KBUIr8bgW8lnhuHQW8z/Y94+44BtLi9WrVdPFb4peSNqdl8KCCpGrxUmXFlk2ByNu6BDOHYFIj\nzyNnu4cNgfdLuo2IlrWRk+rjDEpakLyHbR3bE5PKhbZfLOm5ZHCmFMUEbyMkm+oO8uKEE5iLEuen\nmsTvlbREm5TSAK5M3+1Y+iOcuU58qarpUhy1A4lJe/+Ufj/Udld+8kzACcCviGLY1pzLwQitgjf7\nNiKSk4P3OArj6rbaOAFQQI2kwCKpGOdfUiUxOlict7hCRSSrE6ntcwac20vcX7eRg861CJK2IGo1\nzoZOikilHMBS2s8lUWp+vTLNi98n6HH/or99eyN4gj4WCl37xoolGiiwrTmWrxi+x1yYygZC42EX\nQmnlk+Pdl2qospLwFOB6SV2yh12DBxW+RRSsXpNsrJr+XkrSB5zXBXvaMKKRJAxQQObADYoXJO1M\nXPD1bm8iOqMd7HbyeJfaXkfSVUQ19wM5aY70fVZgiMwRkSLKWhF2OT9DbB1DpIROo/9G/limnUOG\nDykipRl2Lra9rqSLgDcRi5HrnMFLLclRq9lcgohYfAb4X8Jx/6FbFM3NBEi6yvaahWytSTjYWxKN\nVo5rQW2YK13clk4m6fuEk1VXI1nQmUo1Ck70SvQ7AY2oKCrI+VdoWI9ZqO3MAkDNLff5YqCNc1sE\niYKykQcUkTzFfFv1tJ9XBOpyo4sB59tu00CoCLrOr8Mio5KeSXQjLEr9SLab0j+GFtjazmr2lZ5j\n/6Inh7sV8HjbrRoITTaanp/02aGUtDZUtK5IgYM9bV+fXj+HyCrsSUTNSz1TRjSSqYA7yEk5GnHs\nLWnvNo71GPhTigacCJyqaHrwl4wx3QbcBqw3EFH6dZvUSxunehz8PP10Qq4jMw5OGhKRztLKTam3\n/UhaoA691WzN1QqJo/Z2ojD1SmJC34CgBb2srd1pxkkaonfaFIma8VbioXYHIXOliSJeQ+ysQmSh\nllB/M6LFybz3a+ikRpLG9V5C/m8ZQiLvhUSkrJGTnBzXH6sj5z/ZakN/Gg+d5T5VthahiCJSmlu/\nCDzV9sbJEVjP9vcammik/TwdKDC//pQBCT3n9S/IRVP6x56UafZVrIHQFKGxXF4Jp1rSGbZfMdG2\nBlilcrTT2G6QtJbtm5UpS5mChivZPl2hcLKge9Ka2XTfHIyc7YSxVrvktba+pE6PSA7cy5xZoAB9\n6ZrPSnoFkSpt3M2yQsF0aTHkpPrGQ4q8zPXwzY1sA/sk5/g4SScRDlcbSaBSHLXjicr9w4FN3VNw\nOFrSYMRyXsJsYBdJDxDdQyHPSbqRoKFsWj20JbVpab0ywUV+HP3NiO4mOOXZSNfPV9MPaWzrk9dQ\nZDapNsP2hmlRjaOKNAAAIABJREFU0Iaf3onzP4gu0fYaOju3BWg2dZwi6VT6FZHaLAIPJfSwqy6U\nvyUWgU2dbdu+VdKHB9+QtOR0OtwF5tfOAt2ZaDrnlmr2VbKB0FSg8TNJHTpPK2QHHws8Ic0d1XWw\nOOFf5eI3kg4kuOgQ9+pvU/a4cZZX0cL+fcCSRCZpGeAgEg3I9nUtxtYYI2e7hxKr3d1cU9WwfZek\n3YgVfhYkHWr7XcnOGdU28rW/d6VMA5nOUL+ywVxwJv+b/sXHQoQubaNGNAO4kBSBqSLSikrz3Kry\nUhy1A2yfOewN22sP2z4voICztDkR2T5L0inE5Jv9QLd9AnCCpPVsd+JYKgp+tyQKmU+xfZ2k19Gj\nleWkJf9j+z+SkPQY2zdKWrnFsIpx/rtG22so5dwWge0d1a+IdLAzFZESnmD7mEQlxPZDknLqEY4g\nFn6XMzdtx7QrJCyFrvPr0yR9Y6w3c2mDDdB0LqgKbH9Ft2ZfJRsIzTQM6zzdlFb5fqLm7KnEdV39\nX/4FfLPFWN5FUK22T7bOAz5JONo5Wc0PEx1RLwawfVNiMkwJRs52DyVWu8MiNW3Pcd+NmjjB64zx\n2fFQqoFMCVTKBlUUp85vvXfuj48P28fVX0s6klhINIKkJxNO0sIKXdL6CvyxLcZTKvL27MSvuyuN\n8/GEjnAWLWEmImWQ5nRcs904W5OcoZ9IWgR4A1Eg96QU9fiJ8wtlPiDp1wPneb/MzMj3CJm/S4Bv\nKAqI1wN2apHRuj1lw34KnCbpTlosHm2fDpxe4/yfJqkt579ItD05t5sTGrldnNvOUCFFpIR7EuWr\narjxQqJLYSPYfl36XZq20xld51fgPvK7qI6JtAC9f2BbPfrfNKpcFdhuT7dmXyUbCE0FsgITbtl5\n2r0mOx91gR4jtu8jWAf7DXn73xmm7nfUvgFzBCymrGhx5Gz3UKK19WWSvkqs3gx8lMzJRtKnCe7e\nYomnXd0gpnlqso6TZ0pEqcaLX9/2+rW3dpJ0Pt27G65EXofNVxOr5mWIG7m+At8l9+AFOWrb2Z4T\nAbB9Z0qBzdPOtkIndx16BUWzJW1ge6dxdpsLDkmrHxH36ZLAFsQ9k+tsr1452snunWnRlYO1k53/\npvTp34FnuoUGve03pj93V+hSL0F0tc2GynH+S0XbK+ftuAk/OMlwWUWkHYiOmiumOWxpQjq0EdST\nNRsKT0IhYQfkzq93lKIMJhwv6Q3VYlGh130SSfHL9keaGLF9Twq0vIDoiHyqM5sQJTsl65g6Q9FZ\n8Xbb9yt0/1cHflCb43KeQyU6Tz+x5qwjaXHg67m1AIrupXsz0IHU+V1az5G0CxFc24iIlp+YaaM1\nRs52D68nVuJdWlt/FPgsqXCLePjPxcWbAPsQjl9fwYwHOhxmwIQk0Zx0KZEKnk4skpys8wAkvYgW\nLeQ1d8HUn8mQykoPgsMkbT4YxRk4zjbjPTQmgaM2S5Iq3neKxD26hZ2Zhk2ANSsesaTDCEcwy9lW\nT5auwrGErFguZqkmh5Xs5s6JD1TfJzmlv23jaFdIztcGxHV9vu0HWtgoyfnvFG0fco/OeYvpbbD0\nH+BaSZ0UkWxfoVBuWJn4TrmdQ4dF6+aYp7yGdGN0nV8JNa6S+ClwbMqQPJ1Y5Hwy10iiRn0OOJNe\nHdMettvMITMJxwFrKxRfvkecnyOIeTdXrvMdRAb8I4RP9HSCxpeDBYg6tm2BJxMc8DaR7kOA3YD/\nIWgj29KuHmAnQq3pWoLq8gsyhRC6YCT91xCa5FaeYxxzE/pT7tlRLg2XN7tmOvlkimYJ3ycWNCbS\nru+eYVGcORh2Dgfen02Po/ZH+iPk33G+JN1XiAYJBxHn5wPA/9r+RP7oZw4kXUMUDP8jvV6SuK6z\nrkVJtxKT/53EuX4cEXn5K5EVaJRNkvROYGd69QtbAF+wffjYe81l4156km2iJ+GWrUMv6XNpDBW1\n4Q3Asbb3amoj2Xn5WJz/LkhO5RIEN720IzWlkLTNsO25kdh0DQ2z84M245rfIWl327t32P/DBH1j\neeD9zuyonGz8huitcEd6vRRwge1WGZuZguo5JWlHIiO1vyZJzk7ScbYndL4lvZKIHt8JvMQt1GiU\n5FglXWt7tbTtV7ZfnD3wacQost0cE0qCpdTvsOrt7OiEpL2ICNcRadOnUjS4UWMb9fRbn5GcnAqL\nMc0V08kZWiOlldQ2lVuQtjHhocZ7szRHjYgevZ+Qk6syJFO2Ap9E7E1U8J9FfK+XEM5uLk4hONqn\nAkh6FfEAPoag2jRqn237B5IuJ6IlAt5k+4bMsTw78/PjYSui/fh/YA7t5gogy9mmAOdf0s+Jueen\nibYzLRq7kwXbhymkv5Z1rRlRC9TraBYiUvVXANnOtqRVmTtVPm1O+yTNr5uR2U1U0g71l8RC+yrg\nhQo1kK8O33NM3E5/s6a7iT4G8zoeVDSz24aeylJWY60MTEjhkPQSolP1HsBqwAGS3m07tw7lP4qa\ntZskfYQIaGUXNmq4QMM/iZ4Ee7WhEuVg5Gw3R5MUQD2ltRCRdmnbTnQz4sFb8Z2+T0ziTbtIzlj9\nVnXUpp0E2sZEaJr+KcJRS7SEA9PPfAPbR0o6m3BQBHy6JeVibdsfqNn9paQv2t5BIQeVM6brJf2N\n5OAos0VxrQ7h48Axtv+Yc/wB3Eq/7ORjgN+3sFOC838woUbwNUlnEjUfv5jXI9oVJG1KSKI+GlhB\n0SRpD+d1yMP2RwfsLkGv8DtnPLsRXPrnEOntjQnVhSl3tid5fm2T/h8sPP/JGNub4o/AxZJOIOb2\n1xN0hx0AWjjvMwXbElnQL9i+RdIKwA8n6VhNnon7AltUAQyF+s+ZBMUtB9sT1+PHCNW4DYkFRS5O\nJjoXVwHMt6bf/yIkPDcdsk8xjJztghiSvj5fUpdo0OJE+gUyJ5YULf4n7cT6JxuH0k2btrS00ERo\n+oAowlFT6DPvTmi9L0iPkjCdMmCtIWmVVFhXUXFuT7+fKumpLehD/1AUEtd1V+9M3PbGutIqo61f\nYXFCZ/0faVw/tt24CVXC/USL5NOIh9lGwHlK8mkZfOLOnH/35BEXJhb+2wAHSfoFcKTt03LszUDs\nThTInQ1g+6rknHTFvTSXSKvjzcAawJW2t00BienKZk3m/JrdndV2G6358fB7+hexJ6TfJXXcpxyO\nZi+fBFaRtBpRP/ClaRzSevVaM9vH5/pDae7a0vaOhPJIl0ZLg8IM10o63/b6kia9U+vI2W6OCR0u\n9RduzSImlie3PN4+wBWSzkjHfhlR1DE/oJM2bWnaRj0aPQYa0W5s75KigBfTgaNGLDo+Tjzo2hbG\nziTsQDQTGFYM1qYI7G1EwcxPYY7u6tuIxc6WGXZKdZKrHILPS1qdcP7PkXS7Q16uKX5CL2oHyRFs\ngVOBYyTVOf+tVE0csltHE8WVqwOHEY73Ai3HNlPwkO1/qr8DXXYBk6QTa/vNIiLTx7QYz30ORZuH\nUkbsr0yTxnap+TXVIAzbng6T1+U0LUK3GKBHHWX71Tl26s57oicsavtfOTZmIiS9lqjz+T0xL64g\n6f22T56Mw40zjq/Z3t6h+jM7XU8V9iOjV0iy8fx68KADFpW0ru2L0zhfACya3mvLQGiMkbPdHE1a\nedYbEzwE3EJUvzZGlca2/cPEbV032ftcxxT1TEInbdoa/ivpcV24qQm/k/Rj4JBhnF03lJQqyFH7\n5yRNkNMC2+9Lf25c8ZErpJR1rr2/E8o/w5CzuCnVSa6OvxKqDXeQySucqDhPDYuSKMj5TxHWLYmU\n61MI5ZeubbxnAq6T9DZgAYW02MeA7GI7IlVe4SHgNtu3j/XhcXCZQvXlO8Rz5N+Edvt0ouv8es+Q\nbYsQz8SliMVuDpb23FKdbbi7RxAL0IeJc72EpK/a/kqurRmG/YAN3euuuyLwc4I+kYUhTvLgtvFU\naV5S+3sb4plYoY0ww5VElu1Y+pWDcjXy3wt8XyHxLCJT815F34a9W4wrCyM1kgQNl6iqyPOfsH3z\nBPvPItImnYoPNYHyxfyARCfYn0jXX0/SprV9zbg7zm3nKttrDmzLrr6WtBjhTGxLRKe+T0RMsqId\nki4B3jXAUfui7SyOmqIwbgFClWJOE4cWdIsZhWHXdpvrXdKziPqI5akFDJxZiCzpdELxY2/gCYSj\nvI7tF+XYSbY+SES0lybUTY4etnDrgjbXdodjbUdE+VcmrsOjus5tMwmSHkvQ2F5FPHhPBfYcXAxO\nByQtDyyeOx9OwjiKzK9pv8WIBknvISL/+7m/2VoTG5cDb6xqKiQtRxRK584fV9leU9LWRPb508Dl\nnrc7PiLpXNsvqb0WcE59W4atYXN1o/99/XOD+7Sc7w8ZstnOaz5Wt7cE4fveNeGHC2IU2e7hq4R+\n7BHE5PtWggLyG8L5etl4O6cU4L5E97guaFNAMq/hBiJdfi9RCf5TgrediyJ61LbvJiJK30nR6SOB\n/0nR7j0zqCCdOWoJlZpGvTX7tGrudoEKd+okoqsHEdHaLjSbEtr6FZYDtrd9VYfxTIRGkRGV4fy/\nCPgSQbFpzIOfV2D7XuAzKZPhNAdkY4wgDfTOeSMd8TTvzLXN9rltxlUInefXRK3cgbi/DgOe56Rr\n3wKfIWoYqjn1JQQ9LRePkvQoYqF9gO0HJc0PUcfrFTUVxxDX5BbApSno0ygSrFAzeRtBQflZ7a3F\niGxdE8xKWZBZtb+rOT+bfuZMgYGxoCie35wUpKkoZLa7NtNrdvxRZDsg6WLb6w5su8j2CyVdbXuN\nBjY+D1wDHN+WX6ToXHnUWO87s+nCTISkY4gUTtVJcCvg8ba3yLRTRI86PUReS0S2lyfUBH4EvJiI\nTD9rgv2/Znv79Hdf+k3SobbflTOe+Q0KTeN3EYuHS+kvuDosNx2opLvacUz1lt1FkdKSbyRS7q8t\naLdRVEjSjQzh/LujtJU6aiTPJEhahwiiVEVxldZ/bsffPQja0OHEdb01sJjtfTLt1DvZLUQUb16e\nm60pia7za9r/TYSyzTdt57TWHsvmE+g1ZbsoUcpybXyMiGZfTcz7ywI/9Dym2zyIMSLAFRpFglO2\nYAWGqJgB19iekNus6IPwX4YHDnMX/RXV8D1EJrwui5kV2ZZ0CnGfD86L4zWWKoaRs50g6UKiQ1HV\n4OLNwA7J2Z4rnTaGjbsJTtpDhHxXdpc0SbcxTiHkRLzOeQHDFi9NFzQD+8wiuKmvoMZNdWa3TUk3\nA2cB3/NAkwRJ35hogVN3ggYdopZps07SiDMVmqBTZ4ad3QnKx0/op9lkSVqmyM073L1lN4rWxpsQ\nUaHXEN3cjrddrB1wRhp3rsBBoePPNxQ3Re+BD9v+VXq9AfCtXCrBGEGazudf0tOBfWxPm5pU1/lV\n0n+J+/Mh+qP/rbuHKhSE6o3eTsq1McSmgAUqR1ITdAx+pCA9hyod+UtyaT8Fx3EscCMxt+5BLGh/\nbXt2pp3rbK86CUNshBGNpIetCSL/t4iJ4SLg7Qrpq0YFcrZLSAfd8Qi40a9UNCO4CEDSurRotONy\netTvdGodX0HS+rbPb5hJ0Bh/t8WhdJNGnKl4vqIpRr3g6hNu2KiphkpjdcfaNpOv3tC5ZbekjYjM\nzKuJBdvhwAtKpT4H0LRV9lkpqlia8z8/UdzurhxtANvnpWBJLh5O3N+jiGtwK8ooCN0OTJtjAN3n\nV9uzSo5HUcuyDr2M6Ow0T7dpjDUHKQtdj9jOJigv8wQkfcr2PpL2Z3hTvexsuKQtiOLfs2FOS/sd\nbf943B3HttclK/ZM21tIer2jGdURRI1FLi6QtJrta1uOoxNGke2CUIGOWxV1pfzoph/qdXB6FFF4\n9Yf0ejnghtxVp6RbGD655KapOhXuSbqa4PTPIkT7X0bPMTmrRcT+UtvrqL/QpFF2ZSZjWGR2OqOl\nGt6y287o2peid78iCmNvSdtuzrkGNbyzGfQigLnR1rOGbHZXSoJCsWWe5m+rp/X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XSzUfJI2y9LetD2r9aIJ0ZLav4jIobLxCfbki6zfVVbv+1XSZ/t8SfpEdtv7DhXbWy3pDuAd1JG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\niD5Jsh0RERER0Sf/D3QKinjrozhbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c0822adeb8>"
      ]
     },
     "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": 26,
   "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-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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(\"Feature ranking:\")\n",
    "\n",
    "#for f in range(X.shape[1]):\n",
    "#    print(\"%d. feature %d (%f)\" % (f + 1, indices[f], importances[indices[f]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features : 40\n"
     ]
    },
    {
     "data": {
      "image/png": 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8Uo0yxo1New7R1TfA7LwMvv2nt/neU++O6pfEfzyzg9+t38sXLyjnhhVzWVSS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\nCmJIuAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c0820d41d0>"
      ]
     },
     "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": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfe_selection = [i for indx,i in enumerate(X.columns) if rfecv.support_[indx] == True]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE on test data 0.0673898629459\n",
      "MSE on test data 0.0235090542452\n",
      "R Squared data  0.0032629647817\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": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE on test data 0.0673804651554\n",
      "MSE on test data 0.0235486623898\n",
      "R Squared data  0.00158365840807\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": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=-1, normalize=True)\n",
    "model.fit(X_train[xgboost_selection], Y_train)\n",
    "Y_pred = model.predict(X_test[xgboost_selection])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -6.36787406e-05,   1.39876737e-05,   6.36686514e-09,\n",
       "         3.96738060e-14,  -7.74089698e-06,   2.27060201e-05,\n",
       "        -2.53978214e-15,  -6.29052205e-04,  -5.89445148e-07,\n",
       "        -3.25127105e-06,  -3.22805538e-03,   3.15502659e-04,\n",
       "        -5.66590795e-05,  -2.29072188e+06,  -1.96282869e-08,\n",
       "        -1.04289673e-06,   4.24904016e-08,   2.51168864e-04,\n",
       "        -2.29072188e+06,   8.38427062e-04,  -8.13590345e-03,\n",
       "         6.34461337e+08,  -8.44636748e-03,   9.21877951e-04,\n",
       "        -1.48461247e-05,   2.29072187e+06,   3.36156584e-05,\n",
       "        -1.34138210e-03,  -1.00275892e-02,   1.46790057e-02,\n",
       "         6.34461337e+08])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE on test data 0.0672949357839\n",
      "MSE on test data 0.0234268220576\n",
      "R Squared data  0.00674944560748\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": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE on test data 0.0673337661087\n",
      "MSE on test data 0.0234355811979\n",
      "R Squared data  0.00637807552076\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": 55,
   "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 0.0673509182388\n",
      "MSE on test data 0.0235474197972\n",
      "R Squared data  0.00163634185799\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": 56,
   "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 0.0672174974435\n",
      "MSE on test data 0.0234892581242\n",
      "R Squared data  0.00410228084881\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": 57,
   "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 0.0672046155465\n",
      "MSE on test data 0.0234912860864\n",
      "R Squared data  0.00401629929306\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": 461,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best params {'alpha': 0.001, 'copy_X': True, 'fit_intercept': True, 'l1_ratio': 0, 'normalize': True}\n",
      "MAE on test data 0.0675360249797\n",
      "MSE on test data 0.0235838967882\n",
      "R Squared 8.97901549788e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "#ElasticNet\n",
    "#rfe_selection, tree_selection, xgboost_selection\n",
    "from sklearn.linear_model import ElasticNet\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 = {'l1_ratio':[0],'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(ElasticNet(positive=False,random_state=3192),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\",r2_score(Y_test,Y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best params {'n_estimators': 100}\n",
      "MAE on test data 0.067559295451\n",
      "MSE on test data 0.0231946974088\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",
    "\n",
    "parameters_n_estimators = {'n_estimators':[80,100]}\n",
    "              \n",
    "              \n",
    "\n",
    "              \n",
    "grid = GridSearchCV(GradientBoostingRegressor(loss='ls',criterion='friedman_mse',learning_rate=0.1,\n",
    "                                             subsample=0.8,min_samples_split=2000,min_samples_leaf=50,\n",
    "                                             max_depth=8,max_features='sqrt',random_state=3192),param_grid=parameters_n_estimators, 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",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. grid best_score_ 0.0106341652973\n",
      "best params {'n_estimators': 80}\n",
      "best score 0.0106341652973\n",
      "MAE on test data 0.0677507663608\n",
      "MSE on test data 0.0231984200579\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",
    "              \n",
    "grid1 = GridSearchCV(GradientBoostingRegressor(subsample=0.8,max_features=11,min_samples_leaf=50,min_samples_split=50,n_estimators=80,\n",
    "                                               max_depth=9,loss='ls',criterion='friedman_mse',learning_rate=0.1,\n",
    "                                             random_state=3192),\n",
    "                     param_grid=parameters_n_estimators, 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": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. grid best_score_ 0.0130622055628\n",
      "best params {'n_estimators': 160}\n",
      "best score 0.0130622055628\n",
      "MAE on test data 0.0676446785057\n",
      "MSE on test data 0.0231641319322\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",
    "param_grid={'n_estimators':[160,200]}\n",
    "              \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=0.05,\n",
    "                                             random_state=3192), 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": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. grid best_score_ 0.0144108371168\n",
      "best params {'n_estimators': 600}\n",
      "best score 0.0144108371168\n",
      "MAE on test data 0.0672333029135\n",
      "MSE on test data 0.0231136916807\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",
    " #0.01 for 600 trees\n",
    "param_grid={'n_estimators':[400,600]}\n",
    "\n",
    "              \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=0.01,\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": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. grid best_score_ 0.014735098935\n",
      "best params {'n_estimators': 1200}\n",
      "best score 0.014735098935\n",
      "MAE on test data 0.0671729438311\n",
      "MSE on test data 0.0231143494218\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": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. grid best_score_ 0.0146750159587\n",
      "best params {'max_features': 22, 'n_estimators': 1200}\n",
      "best score 0.0146750159587\n",
      "MAE on test data 0.0671271394835\n",
      "MSE on test data 0.0230853233993\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": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. grid best_score_ 0.00819476636465\n",
      "best params {'max_features': 22, 'n_estimators': 1200}\n",
      "best score 0.00819476636465\n",
      "MAE on test data 0.0674352477477\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'mean_squared_error' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-33-703d79ef6472>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[0mpcaY_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrid1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpcaX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"MAE on test data\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmean_absolute_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpcaY_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpcaY_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"MSE on test data\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmean_squared_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpcaY_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpcaY_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'mean_squared_error' is not defined"
     ]
    }
   ],
   "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": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE 0.0674869447615\n"
     ]
    }
   ],
   "source": [
    "from  xgboost import XGBRegressor\n",
    "#xgboost.XGBRegressor\n",
    "model = XGBRegressor()\n",
    "model.fit(pca2X_train, pca2Y_train)\n",
    "# make predictions for test data\n",
    "pca2y_pred = model.predict(pca2X_test)\n",
    "print(\"MAE\",mean_absolute_error(pca2Y_test, pca2y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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, feature_names=X_train.columns.values)\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": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-mae:0.44902+0.000236343\ttest-mae:0.449067+0.00107069\n",
      "[3]\ttrain-mae:0.332976+0.00018352\ttest-mae:0.33313+0.000978562\n",
      "[6]\ttrain-mae:0.249973+0.000195519\ttest-mae:0.250216+0.000915948\n",
      "[9]\ttrain-mae:0.190991+0.000177632\ttest-mae:0.191324+0.000987241\n",
      "[12]\ttrain-mae:0.149545+0.000147298\ttest-mae:0.149981+0.000983443\n",
      "[15]\ttrain-mae:0.120863+0.000122481\ttest-mae:0.121372+0.000989643\n",
      "[18]\ttrain-mae:0.101459+0.000108328\ttest-mae:0.102043+0.00092335\n",
      "[21]\ttrain-mae:0.0887032+0.000111775\ttest-mae:0.0893728+0.000826536\n",
      "[24]\ttrain-mae:0.0805252+0.000118143\ttest-mae:0.0812878+0.0007733\n",
      "[27]\ttrain-mae:0.075467+0.000128888\ttest-mae:0.0763094+0.000715993\n",
      "[30]\ttrain-mae:0.0723806+0.00013198\ttest-mae:0.0733034+0.000668994\n",
      "[33]\ttrain-mae:0.070507+0.000131466\ttest-mae:0.0715058+0.000615435\n",
      "[36]\ttrain-mae:0.0693624+0.000134457\ttest-mae:0.0704412+0.000591247\n",
      "[39]\ttrain-mae:0.0686484+0.000125141\ttest-mae:0.0698088+0.000583612\n",
      "[42]\ttrain-mae:0.0681952+0.000116362\ttest-mae:0.0694528+0.000582354\n",
      "[45]\ttrain-mae:0.06788+0.000132053\ttest-mae:0.0692104+0.000559385\n",
      "[48]\ttrain-mae:0.0676532+0.000134388\ttest-mae:0.0690592+0.000544001\n",
      "[51]\ttrain-mae:0.0674984+0.000118077\ttest-mae:0.0689802+0.000544019\n",
      "[54]\ttrain-mae:0.0673804+0.000117012\ttest-mae:0.0689236+0.000570584\n",
      "[57]\ttrain-mae:0.0672958+0.000128402\ttest-mae:0.0688968+0.000564378\n",
      "[60]\ttrain-mae:0.0672238+0.000123796\ttest-mae:0.0689074+0.000551483\n",
      "[63]\ttrain-mae:0.0671492+0.000131773\ttest-mae:0.0689026+0.000535\n",
      "[66]\ttrain-mae:0.0670968+0.000117632\ttest-mae:0.0689134+0.000526094\n",
      "[69]\ttrain-mae:0.0670394+0.000107105\ttest-mae:0.06893+0.000527927\n",
      "[72]\ttrain-mae:0.0669934+0.000109515\ttest-mae:0.0689422+0.000530934\n",
      "[75]\ttrain-mae:0.066947+0.0001046\ttest-mae:0.0689558+0.000521842\n",
      "[78]\ttrain-mae:0.0669116+0.000101145\ttest-mae:0.0689746+0.000521577\n",
      "[81]\ttrain-mae:0.0668754+0.000101476\ttest-mae:0.0689852+0.000525026\n",
      "[84]\ttrain-mae:0.066823+0.000109008\ttest-mae:0.0689964+0.00051785\n",
      "[87]\ttrain-mae:0.0667988+9.99628e-05\ttest-mae:0.0690326+0.000526441\n",
      "[90]\ttrain-mae:0.0667658+0.000100553\ttest-mae:0.0690546+0.000522909\n",
      "[93]\ttrain-mae:0.0667216+0.000105954\ttest-mae:0.0690808+0.000508167\n",
      "[96]\ttrain-mae:0.0666854+0.000114685\ttest-mae:0.0690942+0.00050476\n",
      "[99]\ttrain-mae:0.0666426+0.000115621\ttest-mae:0.069123+0.000499205\n",
      "[102]\ttrain-mae:0.0666078+0.000115083\ttest-mae:0.069167+0.000490111\n",
      "[105]\ttrain-mae:0.0665672+0.000112017\ttest-mae:0.069193+0.000476775\n",
      "MAE 0.0673114266197\n",
      " model.get_params()['n_estimators'] 58\n",
      "cvresult.shape[0] 58\n"
     ]
    }
   ],
   "source": [
    "#Choose all predictors except target & IDcols\n",
    "#from cv =58 seems to be the ideal number of estimator\n",
    "\n",
    "xgb1 = XGBRegressor(learning_rate =0.1,n_estimators=1000,max_depth=5,min_child_weight=1,gamma=0,subsample=0.8,colsample_bytree=0.8,\n",
    " objective= 'reg:linear',nthread=-1,scale_pos_weight=1,seed=3192)\n",
    "modelfit(xgb1, X_train, Y_train,X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. grid best_score_ 0.0683935537021\n",
      "best params {'max_depth': 3, 'min_child_weight': 300}\n",
      "best score -0.0683935537021\n",
      "MAE on test data 0.0672291569823\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'mean_squared_error' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-49-810d4e14f6d9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[0mY_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgsearch1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"MAE on test data\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmean_absolute_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mY_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"MSE on test data\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmean_squared_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mY_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'mean_squared_error' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score,mean_absolute_error,make_scorer,mean_squared_error\n",
    "\n",
    "param_test1 = {\n",
    " 'max_depth':range(3,10,2),\n",
    " 'min_child_weight':range(50,400,50)\n",
    "}\n",
    "\n",
    "gsearch1 = GridSearchCV(estimator = XGBRegressor(learning_rate=0.1,n_estimators=58,gamma=0,subsample=0.8,colsample_bytree=0.8,\n",
    "objective= 'reg:linear',nthread=-1,scale_pos_weight=1,seed=3192),\n",
    "param_grid = param_test1, scoring='neg_mean_absolute_error',n_jobs=-1, cv=5,verbose=2)\n",
    "\n",
    "gsearch1.fit(X_train, Y_train)\n",
    "print(\"5. grid best_score_\",abs(gsearch1.best_score_))\n",
    "print(\"best params\",gsearch1.best_params_)\n",
    "print(\"best score\",gsearch1.best_score_)\n",
    "\n",
    "Y_pred = gsearch1.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": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 15 candidates, totalling 75 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  2.6min\n",
      "[Parallel(n_jobs=-1)]: Done  75 out of  75 | elapsed:  8.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. grid best_score_ 0.0683657061202\n",
      "best params {'max_depth': 2, 'min_child_weight': 350}\n",
      "best score -0.0683657061202\n",
      "MAE on test data 0.0672289537926\n",
      "MSE on test data 0.0233347561181\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score,mean_absolute_error,make_scorer,mean_squared_error\n",
    "param_test2 = {\n",
    " 'max_depth':[2,3,4,5,6],\n",
    " 'min_child_weight':[250,300,350]\n",
    "}\n",
    "\n",
    "gsearch2 = GridSearchCV(estimator = XGBRegressor(learning_rate=0.1,n_estimators=58,gamma=0,subsample=0.8,colsample_bytree=0.8,\n",
    "objective= 'reg:linear',nthread=-1,scale_pos_weight=1,seed=3192),\n",
    "param_grid = param_test2, scoring='neg_mean_absolute_error',n_jobs=-1, cv=5,verbose=2)\n",
    "\n",
    "gsearch2.fit(X_train, Y_train)\n",
    "print(\"5. grid best_score_\",abs(gsearch2.best_score_))\n",
    "print(\"best params\",gsearch2.best_params_)\n",
    "print(\"best score\",gsearch2.best_score_)\n",
    "\n",
    "Y_pred = gsearch2.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": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:  1.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. grid best_score_ 0.0683639706322\n",
      "best params {'gamma': 0.4, 'max_depth': 2, 'min_child_weight': 350}\n",
      "best score -0.0683639706322\n",
      "MAE on test data 0.0671917728903\n",
      "MSE on test data 0.0233317191158\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score,mean_absolute_error,make_scorer,mean_squared_error\n",
    "param_test3 = {\n",
    " 'max_depth':[2],\n",
    " 'min_child_weight':[350],\n",
    " 'gamma':[i/10.0 for i in range(0,5)]\n",
    "}\n",
    "\n",
    "gsearch3 = GridSearchCV(estimator = XGBRegressor(learning_rate=0.1,n_estimators=58,subsample=0.8,colsample_bytree=0.8,\n",
    "objective= 'reg:linear',nthread=-1,scale_pos_weight=1,seed=3192),\n",
    "param_grid = param_test3, scoring='neg_mean_absolute_error',n_jobs=-1, cv=5,verbose=2)\n",
    "\n",
    "gsearch3.fit(X_train, Y_train)\n",
    "print(\"5. grid best_score_\",abs(gsearch3.best_score_))\n",
    "print(\"best params\",gsearch3.best_params_)\n",
    "print(\"best score\",gsearch3.best_score_)\n",
    "\n",
    "Y_pred = gsearch3.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": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-mae:0.449056+0.000234635\ttest-mae:0.449059+0.00106964\n",
      "[1]\ttrain-mae:0.406084+0.000221522\ttest-mae:0.40609+0.00104988\n",
      "[2]\ttrain-mae:0.367578+0.000196395\ttest-mae:0.367582+0.00102032\n",
      "[3]\ttrain-mae:0.333115+0.000188446\ttest-mae:0.33312+0.000981024\n",
      "[4]\ttrain-mae:0.302308+0.000187946\ttest-mae:0.302313+0.000942503\n",
      "[5]\ttrain-mae:0.274796+0.000191549\ttest-mae:0.274806+0.000924186\n",
      "[6]\ttrain-mae:0.250196+0.000212111\ttest-mae:0.250213+0.000928038\n",
      "[7]\ttrain-mae:0.228267+0.000222589\ttest-mae:0.228287+0.00092506\n",
      "[8]\ttrain-mae:0.20871+0.000205353\ttest-mae:0.20873+0.000991071\n",
      "[9]\ttrain-mae:0.191298+0.000200189\ttest-mae:0.19132+0.00101571\n",
      "[10]\ttrain-mae:0.175829+0.000159793\ttest-mae:0.175853+0.00105154\n",
      "[11]\ttrain-mae:0.162108+0.000174501\ttest-mae:0.162133+0.0010336\n",
      "[12]\ttrain-mae:0.149919+0.000181157\ttest-mae:0.14995+0.00101622\n",
      "[13]\ttrain-mae:0.139155+0.000165399\ttest-mae:0.139188+0.00101346\n",
      "[14]\ttrain-mae:0.129648+0.000145783\ttest-mae:0.129681+0.0010017\n",
      "[15]\ttrain-mae:0.121285+0.000159613\ttest-mae:0.121321+0.000997937\n",
      "[16]\ttrain-mae:0.113948+0.00015585\ttest-mae:0.113986+0.000956566\n",
      "[17]\ttrain-mae:0.107511+0.000166946\ttest-mae:0.107551+0.000944586\n",
      "[18]\ttrain-mae:0.101917+0.00015638\ttest-mae:0.101962+0.000931597\n",
      "[19]\ttrain-mae:0.0970402+0.000161682\ttest-mae:0.0970864+0.000903255\n",
      "[20]\ttrain-mae:0.09283+0.000140982\ttest-mae:0.0928852+0.000872728\n",
      "[21]\ttrain-mae:0.0891854+0.000155119\ttest-mae:0.0892496+0.00082509\n",
      "[22]\ttrain-mae:0.0860346+0.000138199\ttest-mae:0.0861044+0.000815055\n",
      "[23]\ttrain-mae:0.0833504+0.000133549\ttest-mae:0.0834234+0.000806085\n",
      "[24]\ttrain-mae:0.0810466+0.000145912\ttest-mae:0.081123+0.000786652\n",
      "[25]\ttrain-mae:0.0790848+0.000158349\ttest-mae:0.079165+0.000759345\n",
      "[26]\ttrain-mae:0.0774396+0.000165502\ttest-mae:0.077524+0.000753838\n",
      "[27]\ttrain-mae:0.0760158+0.000154879\ttest-mae:0.0761018+0.000740724\n",
      "[28]\ttrain-mae:0.0748234+0.000153483\ttest-mae:0.0749124+0.000723328\n",
      "[29]\ttrain-mae:0.0738088+0.000151049\ttest-mae:0.0739008+0.000706293\n",
      "[30]\ttrain-mae:0.0729552+0.000151686\ttest-mae:0.0730554+0.000697512\n",
      "[31]\ttrain-mae:0.0722396+0.000148672\ttest-mae:0.0723428+0.00067821\n",
      "[32]\ttrain-mae:0.0716322+0.000148013\ttest-mae:0.071734+0.000675333\n",
      "[33]\ttrain-mae:0.0711192+0.000147303\ttest-mae:0.0712228+0.000671574\n",
      "[34]\ttrain-mae:0.0706818+0.000156049\ttest-mae:0.0707864+0.000658365\n",
      "[35]\ttrain-mae:0.0703198+0.00016566\ttest-mae:0.070425+0.000647469\n",
      "[36]\ttrain-mae:0.0700114+0.000156149\ttest-mae:0.0701168+0.000654076\n",
      "[37]\ttrain-mae:0.069751+0.00015622\ttest-mae:0.0698552+0.000653276\n",
      "[38]\ttrain-mae:0.0695262+0.000144453\ttest-mae:0.0696322+0.000661132\n",
      "[39]\ttrain-mae:0.0693402+0.000135862\ttest-mae:0.0694504+0.000662944\n",
      "[40]\ttrain-mae:0.0691722+0.000138134\ttest-mae:0.0692846+0.000662186\n",
      "[41]\ttrain-mae:0.0690426+0.000138787\ttest-mae:0.0691562+0.000660916\n",
      "[42]\ttrain-mae:0.068927+0.000129737\ttest-mae:0.0690426+0.000662951\n",
      "[43]\ttrain-mae:0.0688174+0.000126942\ttest-mae:0.068935+0.000665533\n",
      "[44]\ttrain-mae:0.0687322+0.000134139\ttest-mae:0.0688506+0.000657286\n",
      "[45]\ttrain-mae:0.0686558+0.000136598\ttest-mae:0.0687756+0.000652761\n",
      "[46]\ttrain-mae:0.068586+0.000141979\ttest-mae:0.068708+0.000643791\n",
      "[47]\ttrain-mae:0.0685314+0.000143937\ttest-mae:0.0686566+0.000642516\n",
      "[48]\ttrain-mae:0.0684766+0.000143646\ttest-mae:0.0686036+0.000641047\n",
      "[49]\ttrain-mae:0.0684302+0.0001413\ttest-mae:0.0685582+0.000641123\n",
      "[50]\ttrain-mae:0.0683892+0.000141913\ttest-mae:0.0685202+0.000636985\n",
      "[51]\ttrain-mae:0.068358+0.000141779\ttest-mae:0.0684908+0.000635154\n",
      "[52]\ttrain-mae:0.0683328+0.000144538\ttest-mae:0.0684666+0.000633267\n",
      "[53]\ttrain-mae:0.0683084+0.000140395\ttest-mae:0.0684426+0.000637244\n",
      "[54]\ttrain-mae:0.0682802+0.000141604\ttest-mae:0.0684168+0.000635153\n",
      "[55]\ttrain-mae:0.068259+0.000151013\ttest-mae:0.0683982+0.000623293\n",
      "[56]\ttrain-mae:0.0682464+0.000152159\ttest-mae:0.0683858+0.000622503\n",
      "[57]\ttrain-mae:0.0682328+0.000152291\ttest-mae:0.0683738+0.000618921\n",
      "[58]\ttrain-mae:0.0682188+0.000150798\ttest-mae:0.0683604+0.000619359\n",
      "[59]\ttrain-mae:0.0682082+0.000148888\ttest-mae:0.0683486+0.000618338\n",
      "[60]\ttrain-mae:0.0681954+0.000152006\ttest-mae:0.0683374+0.000616917\n",
      "[61]\ttrain-mae:0.0681872+0.000154176\ttest-mae:0.0683272+0.000615812\n",
      "[62]\ttrain-mae:0.0681772+0.000154633\ttest-mae:0.068319+0.000613207\n",
      "[63]\ttrain-mae:0.0681654+0.000153052\ttest-mae:0.0683096+0.000614405\n",
      "[64]\ttrain-mae:0.0681592+0.000152711\ttest-mae:0.0683036+0.000614782\n",
      "[65]\ttrain-mae:0.0681534+0.000153723\ttest-mae:0.0682986+0.000612465\n",
      "[66]\ttrain-mae:0.0681456+0.00015388\ttest-mae:0.0682922+0.000609302\n",
      "[67]\ttrain-mae:0.0681376+0.00015638\ttest-mae:0.0682844+0.000606875\n",
      "[68]\ttrain-mae:0.0681366+0.000155934\ttest-mae:0.068285+0.000604163\n",
      "[69]\ttrain-mae:0.0681348+0.000152971\ttest-mae:0.0682858+0.000605903\n",
      "[70]\ttrain-mae:0.0681326+0.000149766\ttest-mae:0.0682828+0.000608978\n",
      "[71]\ttrain-mae:0.068132+0.00014818\ttest-mae:0.0682804+0.000605911\n",
      "[72]\ttrain-mae:0.0681294+0.000144955\ttest-mae:0.0682784+0.000609492\n",
      "[73]\ttrain-mae:0.0681266+0.000148937\ttest-mae:0.0682764+0.000605646\n",
      "[74]\ttrain-mae:0.0681278+0.000152294\ttest-mae:0.0682794+0.000601844\n",
      "[75]\ttrain-mae:0.0681246+0.00015179\ttest-mae:0.0682782+0.000601316\n",
      "[76]\ttrain-mae:0.0681212+0.000152302\ttest-mae:0.0682754+0.000601347\n",
      "[77]\ttrain-mae:0.0681202+0.000153275\ttest-mae:0.0682754+0.000601051\n",
      "[78]\ttrain-mae:0.0681178+0.000151116\ttest-mae:0.0682742+0.000602446\n",
      "[79]\ttrain-mae:0.0681148+0.000152417\ttest-mae:0.0682712+0.000600943\n",
      "[80]\ttrain-mae:0.0681132+0.000147901\ttest-mae:0.0682702+0.000604808\n",
      "[81]\ttrain-mae:0.0681162+0.000146602\ttest-mae:0.0682738+0.000605613\n",
      "[82]\ttrain-mae:0.068113+0.000147298\ttest-mae:0.0682722+0.000605941\n",
      "[83]\ttrain-mae:0.0681114+0.000147598\ttest-mae:0.0682714+0.000605141\n",
      "[84]\ttrain-mae:0.0681114+0.00015095\ttest-mae:0.0682734+0.000606358\n",
      "[85]\ttrain-mae:0.0681124+0.00015038\ttest-mae:0.0682742+0.000607067\n",
      "[86]\ttrain-mae:0.0681128+0.000151247\ttest-mae:0.0682738+0.000606627\n",
      "[87]\ttrain-mae:0.0681166+0.000154457\ttest-mae:0.0682796+0.000605994\n",
      "[88]\ttrain-mae:0.0681172+0.000152328\ttest-mae:0.0682798+0.000607963\n",
      "[89]\ttrain-mae:0.0681182+0.000151275\ttest-mae:0.0682838+0.000612418\n",
      "[90]\ttrain-mae:0.0681208+0.00015565\ttest-mae:0.0682872+0.000606046\n",
      "[91]\ttrain-mae:0.0681192+0.000155963\ttest-mae:0.068287+0.000606296\n",
      "[92]\ttrain-mae:0.0681176+0.00015391\ttest-mae:0.0682852+0.000608537\n",
      "[93]\ttrain-mae:0.0681164+0.000155045\ttest-mae:0.0682842+0.000608343\n",
      "[94]\ttrain-mae:0.0681134+0.000151371\ttest-mae:0.068282+0.000610829\n",
      "[95]\ttrain-mae:0.0681136+0.000150483\ttest-mae:0.0682816+0.000610874\n",
      "[96]\ttrain-mae:0.068112+0.000148336\ttest-mae:0.0682808+0.000613202\n",
      "[97]\ttrain-mae:0.0681108+0.000147992\ttest-mae:0.0682798+0.00061274\n",
      "[98]\ttrain-mae:0.06811+0.000146801\ttest-mae:0.0682818+0.000611241\n",
      "[99]\ttrain-mae:0.0681094+0.000146069\ttest-mae:0.0682818+0.000611951\n",
      "[100]\ttrain-mae:0.0681118+0.000146232\ttest-mae:0.0682852+0.000611732\n",
      "[101]\ttrain-mae:0.0681114+0.000147159\ttest-mae:0.0682882+0.000608374\n",
      "[102]\ttrain-mae:0.0681116+0.000148379\ttest-mae:0.0682878+0.000607804\n",
      "[103]\ttrain-mae:0.0681116+0.000150214\ttest-mae:0.0682884+0.000606816\n",
      "[104]\ttrain-mae:0.0681124+0.000148095\ttest-mae:0.0682898+0.000609323\n",
      "[105]\ttrain-mae:0.0681166+0.000145061\ttest-mae:0.0682964+0.000615142\n",
      "[106]\ttrain-mae:0.0681176+0.00014445\ttest-mae:0.0682966+0.000615703\n",
      "[107]\ttrain-mae:0.0681184+0.000145932\ttest-mae:0.0682986+0.000614493\n",
      "[108]\ttrain-mae:0.0681208+0.000145728\ttest-mae:0.0682998+0.000614497\n",
      "[109]\ttrain-mae:0.0681202+0.000144927\ttest-mae:0.0683004+0.000614734\n",
      "[110]\ttrain-mae:0.0681262+0.000146295\ttest-mae:0.0683068+0.000614985\n",
      "[111]\ttrain-mae:0.0681252+0.000146328\ttest-mae:0.0683052+0.000615298\n",
      "[112]\ttrain-mae:0.0681246+0.000150812\ttest-mae:0.0683048+0.000611655\n",
      "[113]\ttrain-mae:0.068123+0.000152343\ttest-mae:0.0683044+0.000609512\n",
      "[114]\ttrain-mae:0.0681218+0.000149272\ttest-mae:0.0683036+0.000612166\n",
      "[115]\ttrain-mae:0.0681182+0.000150231\ttest-mae:0.0683004+0.000611205\n",
      "[116]\ttrain-mae:0.0681194+0.000150339\ttest-mae:0.0683016+0.00061083\n",
      "[117]\ttrain-mae:0.0681194+0.00015412\ttest-mae:0.0683022+0.000608512\n",
      "[118]\ttrain-mae:0.0681168+0.000154285\ttest-mae:0.0682996+0.000607707\n",
      "[119]\ttrain-mae:0.0681168+0.000154539\ttest-mae:0.0683008+0.000607425\n",
      "[120]\ttrain-mae:0.068121+0.000157631\ttest-mae:0.0683042+0.000605323\n",
      "[121]\ttrain-mae:0.0681216+0.000155442\ttest-mae:0.0683062+0.000607782\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[122]\ttrain-mae:0.068123+0.000155558\ttest-mae:0.0683076+0.000606596\n",
      "[123]\ttrain-mae:0.0681248+0.000157894\ttest-mae:0.0683088+0.000605849\n",
      "[124]\ttrain-mae:0.0681254+0.000158549\ttest-mae:0.0683118+0.000604153\n",
      "[125]\ttrain-mae:0.068126+0.000161398\ttest-mae:0.0683128+0.00060135\n",
      "[126]\ttrain-mae:0.0681286+0.000160056\ttest-mae:0.0683142+0.000601404\n",
      "[127]\ttrain-mae:0.0681288+0.000160603\ttest-mae:0.068314+0.000601266\n",
      "[128]\ttrain-mae:0.0681272+0.000158704\ttest-mae:0.0683122+0.000605071\n",
      "[129]\ttrain-mae:0.0681258+0.000159249\ttest-mae:0.068311+0.000604306\n",
      "MAE 0.0670999004934\n",
      "n_estimators 81\n"
     ]
    }
   ],
   "source": [
    "#recaliberate n_estimators with the values found above\n",
    "\n",
    "\n",
    "xgb23 = XGBRegressor(learning_rate =0.1,n_estimators=1000,\n",
    "                    max_depth=2,\n",
    "                    min_child_weight=350,\n",
    "                    gamma=0.4,subsample=0.8,\n",
    "                    colsample_bytree=0.8,objective= 'reg:linear',\n",
    "                    nthread=-1,scale_pos_weight=1,seed=3192)\n",
    "modelfit(xgb23, X_train, Y_train,X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  5.5min\n",
      "[Parallel(n_jobs=-1)]: Done  80 out of  80 | elapsed: 11.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. grid best_score_ 0.0682545138676\n",
      "best params {'colsample_bytree': 0.9, 'subsample': 0.9}\n",
      "best score -0.0682545138676\n",
      "MAE on test data 0.0670887563823\n",
      "MSE on test data 0.0233007607533\n"
     ]
    }
   ],
   "source": [
    "#n_estimators 81\n",
    "\n",
    "from sklearn.metrics import r2_score,mean_absolute_error,make_scorer,mean_squared_error\n",
    "\n",
    "param_test4 = {\n",
    " 'subsample':[i/10.0 for i in range(6,10)],\n",
    " 'colsample_bytree':[i/10.0 for i in range(6,10)]\n",
    "}\n",
    "\n",
    "gsearch4 = 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),\n",
    "param_grid = param_test4, scoring='neg_mean_absolute_error',n_jobs=-1, cv=5,verbose=2)\n",
    "\n",
    "gsearch4.fit(X_train, Y_train)\n",
    "print(\"5. grid best_score_\",abs(gsearch4.best_score_))\n",
    "print(\"best params\",gsearch4.best_params_)\n",
    "print(\"best score\",gsearch4.best_score_)\n",
    "\n",
    "Y_pred = gsearch4.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": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  3.8min\n",
      "[Parallel(n_jobs=-1)]: Done  45 out of  45 | elapsed:  6.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. grid best_score_ 0.0682478101382\n",
      "best params {'colsample_bytree': 0.9, 'subsample': 0.95}\n",
      "best score -0.0682478101382\n",
      "MAE on test data 0.0670416222198\n",
      "MSE on test data 0.0233097766307\n"
     ]
    }
   ],
   "source": [
    "#0.9 seems to be the right fit\n",
    "from sklearn.metrics import r2_score,mean_absolute_error,make_scorer,mean_squared_error\n",
    "\n",
    "param_test5 = {\n",
    " 'subsample':[i/100.0 for i in range(85,100,5)],\n",
    " 'colsample_bytree':[i/100.0 for i in range(85,100,5)]\n",
    "}\n",
    "\n",
    "gsearch5 = 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),\n",
    "param_grid = param_test5, scoring='neg_mean_absolute_error',n_jobs=-1, cv=5,verbose=2)\n",
    "\n",
    "gsearch5.fit(X_train, Y_train)\n",
    "print(\"5. grid best_score_\",abs(gsearch5.best_score_))\n",
    "print(\"best params\",gsearch5.best_params_)\n",
    "print(\"best score\",gsearch5.best_score_)\n",
    "\n",
    "Y_pred = gsearch5.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": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  3.8min\n",
      "[Parallel(n_jobs=-1)]: Done  45 out of  45 | elapsed:  6.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. grid best_score_ 0.0682391348096\n",
      "best params {'reg_alpha': 0.05}\n",
      "best score -0.0682391348096\n",
      "MAE on test data 0.0670037948915\n",
      "MSE on test data 0.0232998202001\n"
     ]
    }
   ],
   "source": [
    "param_test6 = {\n",
    " 'reg_alpha':[0, 0.001, 0.005, 0.01, 0.05,0.5,0.95,0.99,1]\n",
    "}\n",
    "\n",
    "gsearch6 = 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_test6, scoring='neg_mean_absolute_error',n_jobs=-1, cv=5,verbose=2)\n",
    "\n",
    "gsearch6.fit(X_train, Y_train)\n",
    "print(\"5. grid best_score_\",abs(gsearch6.best_score_))\n",
    "print(\"best params\",gsearch6.best_params_)\n",
    "print(\"best score\",gsearch6.best_score_)\n",
    "\n",
    "Y_pred = gsearch6.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": 68,
   "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:  7.8min\n",
      "[Parallel(n_jobs=-1)]: Done  55 out of  55 | elapsed: 10.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. grid best_score_ 0.0682391348096\n",
      "best params {'reg_alpha': 0.05}\n",
      "best score -0.0682391348096\n",
      "MAE on test data 0.0670037948915\n",
      "MSE on test data 0.0232998202001\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": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-mae:0.449081+0.000243059\ttest-mae:0.449085+0.00105742\n",
      "[1]\ttrain-mae:0.406109+0.000222786\ttest-mae:0.406112+0.00104042\n",
      "[2]\ttrain-mae:0.367607+0.000196661\ttest-mae:0.367611+0.00101446\n",
      "[3]\ttrain-mae:0.333128+0.00018466\ttest-mae:0.333137+0.000980052\n",
      "[4]\ttrain-mae:0.302299+0.000169096\ttest-mae:0.302312+0.000963977\n",
      "[5]\ttrain-mae:0.274769+0.000152337\ttest-mae:0.274783+0.000960734\n",
      "[6]\ttrain-mae:0.250176+0.000155122\ttest-mae:0.250194+0.000960227\n",
      "[7]\ttrain-mae:0.228242+0.000152083\ttest-mae:0.228263+0.000966358\n",
      "[8]\ttrain-mae:0.208696+0.000149989\ttest-mae:0.208721+0.000990478\n",
      "[9]\ttrain-mae:0.191299+0.000148793\ttest-mae:0.191332+0.00101006\n",
      "[10]\ttrain-mae:0.175844+0.000150079\ttest-mae:0.175878+0.00102724\n",
      "[11]\ttrain-mae:0.162116+0.000150116\ttest-mae:0.162149+0.00103249\n",
      "[12]\ttrain-mae:0.149937+0.000150195\ttest-mae:0.149974+0.00102798\n",
      "[13]\ttrain-mae:0.139175+0.000149982\ttest-mae:0.139213+0.00102728\n",
      "[14]\ttrain-mae:0.129674+0.000141816\ttest-mae:0.129715+0.00101436\n",
      "[15]\ttrain-mae:0.121303+0.000149795\ttest-mae:0.121355+0.000975266\n",
      "[16]\ttrain-mae:0.113962+0.000141758\ttest-mae:0.114014+0.000948362\n",
      "[17]\ttrain-mae:0.107534+0.000154716\ttest-mae:0.107587+0.000920388\n",
      "[18]\ttrain-mae:0.101927+0.000162339\ttest-mae:0.101984+0.000897818\n",
      "[19]\ttrain-mae:0.0970466+0.000159397\ttest-mae:0.0971094+0.000872222\n",
      "[20]\ttrain-mae:0.0928246+0.000158624\ttest-mae:0.092893+0.00084963\n",
      "[21]\ttrain-mae:0.0891738+0.000162682\ttest-mae:0.0892504+0.00082043\n",
      "[22]\ttrain-mae:0.0860348+0.000167013\ttest-mae:0.0861164+0.000804239\n",
      "[23]\ttrain-mae:0.0833442+0.000161238\ttest-mae:0.083431+0.000795685\n",
      "[24]\ttrain-mae:0.081051+0.000158469\ttest-mae:0.0811426+0.000774619\n",
      "[25]\ttrain-mae:0.079091+0.000156913\ttest-mae:0.0791878+0.000752862\n",
      "[26]\ttrain-mae:0.0774346+0.000157392\ttest-mae:0.0775362+0.000748705\n",
      "[27]\ttrain-mae:0.076021+0.000160784\ttest-mae:0.0761246+0.000733908\n",
      "[28]\ttrain-mae:0.0748224+0.000157534\ttest-mae:0.0749266+0.000720308\n",
      "[29]\ttrain-mae:0.073811+0.000156332\ttest-mae:0.0739174+0.000706067\n",
      "[30]\ttrain-mae:0.072954+0.000154908\ttest-mae:0.073058+0.000696607\n",
      "[31]\ttrain-mae:0.0722384+0.000156612\ttest-mae:0.0723452+0.000685261\n",
      "[32]\ttrain-mae:0.0716328+0.000149315\ttest-mae:0.0717418+0.000683571\n",
      "[33]\ttrain-mae:0.071117+0.000147502\ttest-mae:0.0712264+0.000676407\n",
      "[34]\ttrain-mae:0.07068+0.000144737\ttest-mae:0.0707892+0.00066752\n",
      "[35]\ttrain-mae:0.0703128+0.000147264\ttest-mae:0.0704242+0.000663453\n",
      "[36]\ttrain-mae:0.0700008+0.000152041\ttest-mae:0.0701152+0.000653787\n",
      "[37]\ttrain-mae:0.069738+0.000148728\ttest-mae:0.069855+0.000649584\n",
      "[38]\ttrain-mae:0.069517+0.000146526\ttest-mae:0.0696356+0.000647136\n",
      "[39]\ttrain-mae:0.0693298+0.000147693\ttest-mae:0.0694512+0.000640479\n",
      "[40]\ttrain-mae:0.0691654+0.000147108\ttest-mae:0.0692898+0.000642089\n",
      "[41]\ttrain-mae:0.069033+0.000143551\ttest-mae:0.0691588+0.000641534\n",
      "[42]\ttrain-mae:0.068916+0.000141892\ttest-mae:0.0690474+0.000638729\n",
      "[43]\ttrain-mae:0.0688114+0.00014427\ttest-mae:0.0689444+0.000634842\n",
      "[44]\ttrain-mae:0.0687262+0.000142595\ttest-mae:0.0688628+0.000631004\n",
      "[45]\ttrain-mae:0.0686494+0.000143143\ttest-mae:0.068785+0.000628956\n",
      "[46]\ttrain-mae:0.0685844+0.000144738\ttest-mae:0.0687232+0.000624695\n",
      "[47]\ttrain-mae:0.0685254+0.000145556\ttest-mae:0.0686646+0.000624939\n",
      "[48]\ttrain-mae:0.0684706+0.00014515\ttest-mae:0.0686102+0.000624633\n",
      "[49]\ttrain-mae:0.0684252+0.000143877\ttest-mae:0.0685652+0.00062609\n",
      "[50]\ttrain-mae:0.0683838+0.000146065\ttest-mae:0.068528+0.000623985\n",
      "[51]\ttrain-mae:0.0683524+0.000144881\ttest-mae:0.0684974+0.000624121\n",
      "[52]\ttrain-mae:0.06832+0.000140117\ttest-mae:0.0684646+0.000626648\n",
      "[53]\ttrain-mae:0.068293+0.000139236\ttest-mae:0.0684382+0.00062563\n",
      "[54]\ttrain-mae:0.068269+0.000138662\ttest-mae:0.0684148+0.000625965\n",
      "[55]\ttrain-mae:0.0682504+0.000147486\ttest-mae:0.068397+0.000615931\n",
      "[56]\ttrain-mae:0.0682298+0.000147501\ttest-mae:0.0683784+0.000616033\n",
      "[57]\ttrain-mae:0.0682144+0.00014987\ttest-mae:0.068364+0.000610278\n",
      "[58]\ttrain-mae:0.0682022+0.00015139\ttest-mae:0.068354+0.000608234\n",
      "[59]\ttrain-mae:0.0681908+0.000152032\ttest-mae:0.068343+0.000609353\n",
      "[60]\ttrain-mae:0.068177+0.000157856\ttest-mae:0.0683294+0.000603117\n",
      "[61]\ttrain-mae:0.0681692+0.000157154\ttest-mae:0.0683232+0.000601236\n",
      "[62]\ttrain-mae:0.0681614+0.000158006\ttest-mae:0.0683154+0.000599973\n",
      "[63]\ttrain-mae:0.068155+0.000157251\ttest-mae:0.0683082+0.000601004\n",
      "[64]\ttrain-mae:0.0681464+0.000156868\ttest-mae:0.0683002+0.000601116\n",
      "[65]\ttrain-mae:0.068141+0.000158229\ttest-mae:0.0682952+0.000599156\n",
      "[66]\ttrain-mae:0.0681354+0.000157406\ttest-mae:0.0682894+0.000599404\n",
      "[67]\ttrain-mae:0.0681286+0.000157342\ttest-mae:0.0682842+0.000599088\n",
      "[68]\ttrain-mae:0.068125+0.000157343\ttest-mae:0.0682812+0.000598891\n",
      "[69]\ttrain-mae:0.0681188+0.00015589\ttest-mae:0.0682754+0.000599857\n",
      "[70]\ttrain-mae:0.0681154+0.000155978\ttest-mae:0.0682736+0.000599651\n",
      "[71]\ttrain-mae:0.068113+0.000156968\ttest-mae:0.0682714+0.000598155\n",
      "[72]\ttrain-mae:0.0681126+0.000153025\ttest-mae:0.0682696+0.000601718\n",
      "[73]\ttrain-mae:0.0681104+0.000154549\ttest-mae:0.0682674+0.000600416\n",
      "[74]\ttrain-mae:0.0681066+0.000154933\ttest-mae:0.0682638+0.000599332\n",
      "[75]\ttrain-mae:0.0681044+0.000154565\ttest-mae:0.0682614+0.000599316\n",
      "[76]\ttrain-mae:0.0680986+0.000152585\ttest-mae:0.0682554+0.000602064\n",
      "[77]\ttrain-mae:0.0680962+0.00015135\ttest-mae:0.0682526+0.000602493\n",
      "[78]\ttrain-mae:0.0680948+0.00015061\ttest-mae:0.0682516+0.000603523\n",
      "[79]\ttrain-mae:0.0680958+0.000152098\ttest-mae:0.068253+0.000601975\n",
      "[80]\ttrain-mae:0.068095+0.00015142\ttest-mae:0.0682526+0.000602679\n",
      "[81]\ttrain-mae:0.0680946+0.000151225\ttest-mae:0.0682528+0.000602142\n",
      "[82]\ttrain-mae:0.068094+0.000151816\ttest-mae:0.0682516+0.000602257\n",
      "[83]\ttrain-mae:0.0680942+0.000152216\ttest-mae:0.0682522+0.000601953\n",
      "[84]\ttrain-mae:0.068093+0.000151417\ttest-mae:0.0682528+0.000602075\n",
      "[85]\ttrain-mae:0.0680922+0.000150627\ttest-mae:0.0682516+0.000602885\n",
      "[86]\ttrain-mae:0.0680904+0.000149539\ttest-mae:0.06825+0.0006033\n",
      "[87]\ttrain-mae:0.0680918+0.000150566\ttest-mae:0.0682502+0.000602699\n",
      "[88]\ttrain-mae:0.0680906+0.000149987\ttest-mae:0.0682492+0.000603568\n",
      "[89]\ttrain-mae:0.0680898+0.000149703\ttest-mae:0.0682486+0.00060386\n",
      "[90]\ttrain-mae:0.0680908+0.000151509\ttest-mae:0.06825+0.000602526\n",
      "[91]\ttrain-mae:0.0680906+0.000151135\ttest-mae:0.0682504+0.000602364\n",
      "[92]\ttrain-mae:0.0680898+0.000150841\ttest-mae:0.0682496+0.000602301\n",
      "[93]\ttrain-mae:0.0680902+0.000151417\ttest-mae:0.0682502+0.000602429\n",
      "[94]\ttrain-mae:0.0680874+0.000148933\ttest-mae:0.0682488+0.000603151\n",
      "[95]\ttrain-mae:0.0680872+0.000149322\ttest-mae:0.068248+0.000602864\n",
      "[96]\ttrain-mae:0.0680892+0.000149947\ttest-mae:0.0682508+0.00060229\n",
      "[97]\ttrain-mae:0.0680898+0.000150534\ttest-mae:0.0682518+0.000602785\n",
      "[98]\ttrain-mae:0.0680864+0.000149503\ttest-mae:0.0682472+0.000605821\n",
      "[99]\ttrain-mae:0.068086+0.000150087\ttest-mae:0.0682468+0.000605295\n",
      "[100]\ttrain-mae:0.0680838+0.000148219\ttest-mae:0.0682446+0.000606286\n",
      "[101]\ttrain-mae:0.0680842+0.000148743\ttest-mae:0.068245+0.000605494\n",
      "[102]\ttrain-mae:0.0680836+0.000148145\ttest-mae:0.0682442+0.000605837\n",
      "[103]\ttrain-mae:0.068085+0.000149949\ttest-mae:0.0682464+0.000603633\n",
      "[104]\ttrain-mae:0.068084+0.000148463\ttest-mae:0.0682454+0.000605081\n",
      "[105]\ttrain-mae:0.068081+0.000146348\ttest-mae:0.0682448+0.000605306\n",
      "[106]\ttrain-mae:0.068082+0.000146126\ttest-mae:0.068246+0.000604794\n",
      "[107]\ttrain-mae:0.0680834+0.000145652\ttest-mae:0.0682472+0.0006056\n",
      "[108]\ttrain-mae:0.0680812+0.000150537\ttest-mae:0.0682464+0.000603458\n",
      "[109]\ttrain-mae:0.0680802+0.000150324\ttest-mae:0.0682454+0.0006035\n",
      "[110]\ttrain-mae:0.0680818+0.000150915\ttest-mae:0.0682474+0.000603805\n",
      "[111]\ttrain-mae:0.0680804+0.000149765\ttest-mae:0.068246+0.000604361\n",
      "[112]\ttrain-mae:0.0680808+0.000150272\ttest-mae:0.0682458+0.000604512\n",
      "[113]\ttrain-mae:0.0680798+0.000150368\ttest-mae:0.0682458+0.00060357\n",
      "[114]\ttrain-mae:0.0680806+0.000150355\ttest-mae:0.0682494+0.00060329\n",
      "[115]\ttrain-mae:0.068081+0.000150701\ttest-mae:0.0682494+0.000603264\n",
      "[116]\ttrain-mae:0.0680828+0.000151095\ttest-mae:0.0682526+0.000602348\n",
      "[117]\ttrain-mae:0.068083+0.000150705\ttest-mae:0.0682522+0.000603139\n",
      "[118]\ttrain-mae:0.068082+0.000149174\ttest-mae:0.0682512+0.000604649\n",
      "[119]\ttrain-mae:0.0680806+0.000148338\ttest-mae:0.0682498+0.000605347\n",
      "[120]\ttrain-mae:0.0680812+0.00014838\ttest-mae:0.0682506+0.000605383\n",
      "[121]\ttrain-mae:0.0680836+0.000148589\ttest-mae:0.068253+0.000605677\n",
      "[122]\ttrain-mae:0.0680836+0.000148489\ttest-mae:0.0682534+0.00060541\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[123]\ttrain-mae:0.0680836+0.000149545\ttest-mae:0.0682534+0.000604529\n",
      "[124]\ttrain-mae:0.0680836+0.000149533\ttest-mae:0.0682538+0.000604535\n",
      "[125]\ttrain-mae:0.0680852+0.000150874\ttest-mae:0.0682548+0.000602849\n",
      "[126]\ttrain-mae:0.068085+0.000151432\ttest-mae:0.068254+0.000602668\n",
      "[127]\ttrain-mae:0.068084+0.000149175\ttest-mae:0.0682526+0.000602786\n",
      "[128]\ttrain-mae:0.0680836+0.000148093\ttest-mae:0.068252+0.000603633\n",
      "[129]\ttrain-mae:0.068081+0.000147006\ttest-mae:0.0682498+0.000604854\n",
      "[130]\ttrain-mae:0.0680802+0.000148244\ttest-mae:0.0682496+0.000604174\n",
      "[131]\ttrain-mae:0.0680802+0.000148323\ttest-mae:0.068249+0.000604578\n",
      "[132]\ttrain-mae:0.0680794+0.000147454\ttest-mae:0.0682484+0.000605244\n",
      "[133]\ttrain-mae:0.0680804+0.000146042\ttest-mae:0.0682492+0.000606996\n",
      "[134]\ttrain-mae:0.068079+0.000145788\ttest-mae:0.0682482+0.000607405\n",
      "[135]\ttrain-mae:0.068079+0.00014749\ttest-mae:0.0682484+0.000605776\n",
      "[136]\ttrain-mae:0.0680784+0.000147611\ttest-mae:0.0682478+0.000605752\n",
      "[137]\ttrain-mae:0.068079+0.00014511\ttest-mae:0.0682488+0.000608728\n",
      "[138]\ttrain-mae:0.068081+0.000146599\ttest-mae:0.0682514+0.000607533\n",
      "[139]\ttrain-mae:0.068081+0.000147328\ttest-mae:0.0682514+0.000606914\n",
      "[140]\ttrain-mae:0.0680814+0.000146447\ttest-mae:0.0682514+0.000607742\n",
      "[141]\ttrain-mae:0.0680822+0.000147422\ttest-mae:0.068252+0.000606848\n",
      "[142]\ttrain-mae:0.0680814+0.000147933\ttest-mae:0.0682516+0.000605768\n",
      "[143]\ttrain-mae:0.068084+0.000150667\ttest-mae:0.0682552+0.000603996\n",
      "[144]\ttrain-mae:0.0680848+0.000150597\ttest-mae:0.068256+0.000603527\n",
      "[145]\ttrain-mae:0.0680854+0.000150222\ttest-mae:0.0682564+0.000603996\n",
      "[146]\ttrain-mae:0.0680844+0.000149164\ttest-mae:0.0682554+0.000605048\n",
      "[147]\ttrain-mae:0.0680854+0.000149878\ttest-mae:0.0682566+0.000604517\n",
      "[148]\ttrain-mae:0.0680842+0.000150162\ttest-mae:0.0682562+0.000605711\n",
      "[149]\ttrain-mae:0.068084+0.000149903\ttest-mae:0.068256+0.000605313\n",
      "[150]\ttrain-mae:0.0680844+0.000149802\ttest-mae:0.0682566+0.000605537\n",
      "[151]\ttrain-mae:0.0680842+0.000149852\ttest-mae:0.0682562+0.000605706\n",
      "MAE 0.0670068360186\n",
      "n_estimators 103\n"
     ]
    }
   ],
   "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": 76,
   "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:  7.3min\n",
      "[Parallel(n_jobs=-1)]: Done  55 out of  55 | elapsed: 11.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. grid best_score_ 0.0682375721942\n",
      "best params {'learning_rate': 0.09}\n",
      "best score -0.0682391348096\n",
      "MAE on test data 0.067070536223\n",
      "MSE on test data 0.023314471898\n"
     ]
    }
   ],
   "source": [
    "param_test8 = {\n",
    " 'learning_rate':[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",
    "gsearch8 = GridSearchCV(estimator = XGBRegressor(n_estimators=103,\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",
    "param_grid = param_test8, scoring='neg_mean_absolute_error',n_jobs=-1, cv=5,verbose=2)\n",
    "\n",
    "gsearch8.fit(X_train, Y_train)\n",
    "print(\"5. grid best_score_\",abs(gsearch8.best_score_))\n",
    "print(\"best params\",gsearch8.best_params_)\n",
    "print(\"best score\",gsearch7.best_score_)\n",
    "\n",
    "Y_pred = gsearch8.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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed: 10.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE 0.0711470231413\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "#xgb – this is the direct xgboost library. I will use a specific function “cv” from this library\n",
    "from xgboost.sklearn import XGBRegressor\n",
    "#this is an sklearn wrapper for XGBoost. This allows us to use sklearn’s Grid Search with parallel processing \n",
    "from sklearn import cross_validation, metrics   #Additional scklearn functions\n",
    "from sklearn.model_selection import GridSearchCV  #Perforing grid search\n",
    "\n",
    "param_grid={'max_depth':[5],'learning_rate':[0.1],'n_estimators':[1000],\n",
    "           'objective':['reg:linear'],'gamma':[0],'min_child_weight':[1],'max_delta_step':[0],\n",
    "           'subsample':[0.8], 'colsample_bytree':[0.8], 'colsample_bylevel':[1], \n",
    "            'reg_alpha':[0],'reg_lambda':[1], 'scale_pos_weight':[1],'base_score':[0.5]}\n",
    "\n",
    "\n",
    "grid1 = GridSearchCV(XGBRegressor(nthread=-1,silent=True,seed=3192,missing=None),\n",
    "                     param_grid=param_grid, scoring='neg_mean_absolute_error',cv=5,refit='neg_mean_absolute_error',\n",
    "                    verbose=4,n_jobs=-1,pre_dispatch='n_jobs')\n",
    "\n",
    "\n",
    "grid1.fit(X_train, Y_train)\n",
    "# make predictions for test data\n",
    "y_pred = grid1.predict(X_test)\n",
    "print(\"MAE\",mean_absolute_error(Y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid1.best_estimator_ XGBRegressor(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,\n",
      "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
      "       min_child_weight=1, missing=None, n_estimators=1000, nthread=-1,\n",
      "       objective='reg:linear', reg_alpha=0, reg_lambda=1,\n",
      "       scale_pos_weight=1, seed=3192, silent=True, subsample=0.8)\n",
      "grid1.best_params_ {'base_score': 0.5, 'colsample_bylevel': 1, 'colsample_bytree': 0.8, 'gamma': 0, 'learning_rate': 0.1, 'max_delta_step': 0, 'max_depth': 5, 'min_child_weight': 1, 'n_estimators': 1000, 'objective': 'reg:linear', 'reg_alpha': 0, 'reg_lambda': 1, 'scale_pos_weight': 1, 'subsample': 0.8}\n",
      "grid1.scorer_ make_scorer(mean_absolute_error, greater_is_better=False)\n"
     ]
    }
   ],
   "source": [
    "print(\"grid1.best_estimator_\",grid1.best_estimator_)\n",
    "print(\"grid1.best_params_\",grid1.best_params_)\n",
    "print(\"grid1.scorer_\",grid1.scorer_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculating coefficients\n",
    "\n",
    "#coeff = DataFrame(x_train.columns)\n",
    "\n",
    "#coeff['Coefficient Estimate'] = Series(lreg.coef_)\n",
    "\n",
    "#coeff\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KLyv19HAhawDVauuoGJQ3NCT19x+4oPXISDEtcaQMgGqwx16zgYEDoT5ubKyoA0AVCPaa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16ec82f5198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Interpretation of Regression Plots #Take a look at the residual vs fitted values plot. #residual plot\n",
    "x_plot = plt.scatter(y_pred, (y_pred - Y_test), c='b')\n",
    "plt.hlines(y=0, xmin= -1000, xmax=5000)\n",
    "plt.title('Residual plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "checking the magnitude of coefficients\n",
    "\n",
    "predictors = x_train.columns\n",
    "\n",
    "coef = Series(lreg.coef_,predictors).sort_values()\n",
    "\n",
    "coef.plot(kind='bar', title='Modal Coefficients')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x15ff98446a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(df_train.ac_id, df_train.logerror, 'ro')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1351,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "#dropping N-AvRoomSize','N-LivingAreaProp because has some infinity values\n",
    "df_train=df_train.drop(['N-LivingAreaProp'],axis=1)\n",
    "# drop assessmentyear , it's a single value\n",
    "df_train=df_train.drop('assessmentyear',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1352,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "df_train=df_train.drop('parcelid',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1353,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# ac_id 5 leves , order has no meaning ( One Hot Encoder)\n",
    "# heatingorsystemtypeid , 12 levels , no order ( One Hot Encoder)\n",
    "# propertyzoningdesc, 1996 levels, no order \n",
    "# propertycountylandusecode, 77, no order \n",
    "# propertylandusetypeid, 14 ,no order \n",
    "# regionidcity, 178 levels, no order \n",
    "# regionidneighborhood, 495 levels, no order \n",
    "# regionidzip, 388 levels, no order\n",
    "# yearbuilt, 130 -> N-life, use as a number (may be create bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1354,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape 1 (90275, 56)\n",
      "shape 2 (90275, 69)\n"
     ]
    }
   ],
   "source": [
    "# EncodeLabel, One Hot Encoder\n",
    "#(Note that the LabelEncoder must be used prior to one-hot encoding, as the OneHotEncoder cannot handle categorical data. \n",
    "#Therefore, it is frequently used as pre-cursor to one-hot encoding.\n",
    "#Alternatively, it can encode your target into a usable array. If, for instance, train were your target for classification, \n",
    "#you would need a LabelEncoder to use it as your y variable.)\n",
    "\n",
    "#Identify numerical columns with more and less stages\n",
    "\n",
    "\n",
    "# has_hottub_or_spa, 2 levels no order ( One Hot Encoder) # True or falase\n",
    "# taxdelinquencyflag -> 2 layers, no order (No, Y)\n",
    "# fireplaceflag >- 2 layers , yes, no\n",
    "# N-PropType, shriked to 3 levels(propertylandusetypeid) , no order  #String\n",
    "# pool_with_spa_tub_yes, 2 levels,no order # numbers\n",
    "# pool_with_spa_tub_no,  2 levels,no order # numbers\n",
    "# regionidcounty, 3 levels, no order # number\n",
    "#'N-HeatInd' >- 2 layers , 0 and 1\n",
    "#'N-ACInd'-> 2 layers , 0 and 1\n",
    "#fips 3 levels , county code , order has no meaning ( One Hot Encoder) #numbers\n",
    "\n",
    "# creates dummies and auto drops duplicates\n",
    "print (\"shape 1\",df_train.shape)\n",
    "df_train=pd.get_dummies(df_train, columns=['pool_with_spa_tub_yes','pool_with_spa_tub_no','regionidcounty','N-HeatInd','N-ACInd','fips','has_hottub_or_spa','taxdelinquencyflag','fireplaceflag','N-PropType'],\n",
    "               prefix=['pool_with_spa_tub_yes','pool_with_spa_tub_no','regionidcounty','N-HeatInd','N-ACInd','fips','has_hottub_or_spa','taxdelinquencyflag','fireplaceflag','N-PropType'])\n",
    "print (\"shape 2\",df_train.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1355,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# ac_id 5 leves , order has no meaning ( One Hot Encoder)\n",
    "# heating_system_id , 12 levels , no order ( One Hot Encoder)\n",
    "# propertyzoningdesc, 1996 levels, no order \n",
    "# propertycountylandusecode, 77, no order \n",
    "# propertylandusetypeid, 14 ,no order \n",
    "# regionidcity, 178 levels, no order \n",
    "# regionidneighborhood, 495 levels, no order \n",
    "# regionidzip, 388 levels, no order\n",
    "# yearbuilt, 130 -> N-life, use as a number (may be create bins)\n",
    "#rawcensustractandblock\n",
    "\n",
    "\n",
    "catcols3 = ['ac_id','propertycountylandusecode','heating_system_id',\n",
    "           'propertylandusetypeid','propertyzoningdesc','rawcensustractandblock','regionidcity',\n",
    "            'regionidneighborhood','regionidzip','yearbuilt']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1356,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_x2 = df_train.drop(catcols3+target, axis=1)\n",
    "train_y2=df_train['logerror'].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1357,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "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(train_x2, train_y2, feature_names=train_x2.columns.values)\n",
    "model3 = xgb.train(dict(xgb_params, silent=0), dtrain, num_boost_round=50)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZmZmZmZmllF86YpZj/YaNFI96rNBhWB7DSmuo9NikjsclvTw2jbMzPxDbzGxX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\nyyknbGZmZmZmZjnlhM3MzMzMzCyn/j8O8b6pgeJV4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2829366b7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##only numerical features\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(model3,  height=0.8, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1359,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "important=['total_finished_living_area_sqft', 'latitude', 'longitude','lot_area_sqft','structure_tax','total_bath','N-ValueRatio',\n",
    "'bedroomcnt','taxvaluedollarcnt','N-Dev-structure_tax','N-life','total_property_tax_2016','N-ValueProp','land_tax', \n",
    "'N-TaxScore','N-ExtraSpace','N-zip_count','building_quality_id','finished_living_area_entryfloor_sqft2','transaction_month',\n",
    "'N-Avg-structure_tax', 'N-structure_tax-2','N-city_count','N-TotalRooms','N-ExtraRooms','garage_sqft',\n",
    "'taxdelinquencyflag_No','roomcnt','N-PropType_Mixed','unitcnt','pool_sqft','garagecarcnt','fireplace_count','N-PropType_Home',\n",
    " 'taxdelinquencyflag_Y','poolcnt','N-structure_tax-3','N-PropType_Not Built']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1360,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X=df_train[important]\n",
    "Y=df_train['logerror']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1361,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#XGBoost models represent all problems as a regression predictive modeling problem that only takes numerical values as input.\n",
    "from sklearn.model_selection import train_test_split\n",
    "from numpy import loadtxt\n",
    "from numpy import sort\n",
    "from xgboost import XGBRegressor\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",
    "#metrics.mean_absolute_error(y_true, y_pred)\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.20, random_state=123)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1362,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE 0.0680350784233\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1363,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results [-0.00204293 -0.0044521   0.00909425 -0.01265424  0.00525764  0.0157698\n",
      "  0.0003503   0.01630476  0.01100558 -0.00202784]\n",
      "MAE 0.0680350784233\n"
     ]
    }
   ],
   "source": [
    "#k-fold cross validation evaluation of xgboost model\n",
    "from numpy import loadtxt\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "model = XGBRegressor()\n",
    "kfold = KFold(n_splits=10, random_state=7)\n",
    "model.fit(X_train, Y_train)\n",
    "results = cross_val_score(model, X_train, Y_train, cv=kfold)\n",
    "print(\"results\",results)\n",
    "y_pred = model.predict(X_test)\n",
    "print(\"MAE\",mean_absolute_error(Y_test, y_pred))\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "#y_pred = cross_val_predict(lasso, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1373,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d=abs(abs(Y)-np.mean(abs(df_train.logerror)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06407561558424611"
      ]
     },
     "execution_count": 1375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Cross-validation is an approach that you can use to estimate the performance of a machine\n",
    "#learning algorithm with less variance than a single train-test set split.\n",
    "#k values of 3, 5 and 10 are commo\n",
    "\n",
    "#The cross_val_score() function from scikit-learn allows us\n",
    "#to evaluate a model using the cross-validation scheme and returns a list of the scores for each\n",
    "#model trained on each fold.\n",
    "\n",
    "\n",
    "\n",
    "kfold = KFold(n_splits=10, random_state=7)\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(\"Accuracy: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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