house_price_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Import library and data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import some necessary librairies\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt  # Matlab-style plotting\n",
    "from mpl_toolkits.mplot3d import Axes3D # Plotting 3D graph\n",
    "\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "sns.set_style('darkgrid')\n",
    "import warnings\n",
    "def ignore_warn(*args, **kwargs):\n",
    "    pass\n",
    "warnings.warn = ignore_warn #ignore annoying warning (from sklearn and seaborn)\n",
    "\n",
    "\n",
    "from scipy import stats\n",
    "from scipy.stats import norm, skew #for some statistics\n",
    "\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x)) #Limiting floats output to 3 decimal points\n",
    "\n",
    "# importing data\n",
    "train = pd.read_csv('COMP4433 Project/train.csv')\n",
    "test = pd.read_csv('COMP4433 Project/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 81)\n",
      "(1459, 80)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     1460.000\n",
       "mean    180921.196\n",
       "std      79442.503\n",
       "min      34900.000\n",
       "25%     129975.000\n",
       "50%     163000.000\n",
       "75%     214000.000\n",
       "max     755000.000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['SalePrice'].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.hist(train['SalePrice'], bins=50)\n",
    "plt.xlabel('Predicted Sale Price')\n",
    "plt.ylabel('Freq')\n",
    "plt.title('Predicted Sale Price Distribution')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(2919, 80)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ntrain = train.shape[0]\n",
    "ntest = test.shape[0]\n",
    "y_train = train.SalePrice.values\n",
    "\n",
    "#Check the graphic again\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(train['GrLivArea'], train['SalePrice'])\n",
    "plt.ylabel('SalePrice', fontsize=13)\n",
    "plt.xlabel('GrLivArea', fontsize=13)\n",
    "plt.show()\n",
    "\n",
    "all_data = pd.concat((train, test),sort=True).reset_index(drop=True)\n",
    "all_data.drop(['SalePrice'], axis=1, inplace=True)\n",
    "\n",
    "\n",
    "\n",
    "all_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1 refer to axis=1 (column)\n",
    "all_data = all_data.drop('Id', 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['1stFlrSF', '2ndFlrSF', '3SsnPorch', 'BedroomAbvGr', 'BsmtFinSF1',\n",
      "       'BsmtFinSF2', 'BsmtFullBath', 'BsmtHalfBath', 'BsmtUnfSF',\n",
      "       'EnclosedPorch', 'Fireplaces', 'FullBath', 'GarageArea', 'GarageCars',\n",
      "       'GarageYrBlt', 'GrLivArea', 'HalfBath', 'KitchenAbvGr', 'LotArea',\n",
      "       'LotFrontage', 'LowQualFinSF', 'MSSubClass', 'MasVnrArea', 'MiscVal',\n",
      "       'MoSold', 'OpenPorchSF', 'OverallCond', 'OverallQual', 'PoolArea',\n",
      "       'ScreenPorch', 'TotRmsAbvGrd', 'TotalBsmtSF', 'WoodDeckSF', 'YearBuilt',\n",
      "       'YearRemodAdd', 'YrSold'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check numerical features\n",
    "numeric_features = all_data.select_dtypes(include=[np.number])\n",
    "print(numeric_features.columns)\n",
    "len(numeric_features.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show number of missing value in 36 numeric variables\n",
    "nulls = pd.DataFrame(numeric_features.isnull().sum().sort_values(ascending=False))\n",
    "nulls.columns = ['Null Count']\n",
    "nulls.index.name = 'Feature'\n",
    "\n",
    "y_pos = np.arange(len(nulls))\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.barh(y_pos, nulls['Null Count'], align='center', alpha=0.5)\n",
    "plt.yticks(y_pos, nulls.index)\n",
    "plt.xlabel('number of missing value')\n",
    "plt.title('number of missing value in 36 numeric variables')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IterativeImputer(add_indicator=False, estimator=None,\n",
       "                 imputation_order='ascending', initial_strategy='mean',\n",
       "                 max_iter=10, max_value=None, min_value=None,\n",
       "                 missing_values=nan, n_nearest_features=None, random_state=0,\n",
       "                 sample_posterior=False, tol=0.001, verbose=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use iterative imputer to impute\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "\n",
    "imp = IterativeImputer(max_iter=10, random_state=0)\n",
    "imp.fit(numeric_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1stFlrSF         0\n",
       "2ndFlrSF         0\n",
       "3SsnPorch        0\n",
       "BedroomAbvGr     0\n",
       "BsmtFinSF1       0\n",
       "BsmtFinSF2       0\n",
       "BsmtFullBath     0\n",
       "BsmtHalfBath     0\n",
       "BsmtUnfSF        0\n",
       "EnclosedPorch    0\n",
       "Fireplaces       0\n",
       "FullBath         0\n",
       "GarageArea       0\n",
       "GarageCars       0\n",
       "GarageYrBlt      0\n",
       "GrLivArea        0\n",
       "HalfBath         0\n",
       "KitchenAbvGr     0\n",
       "LotArea          0\n",
       "LotFrontage      0\n",
       "LowQualFinSF     0\n",
       "MSSubClass       0\n",
       "MasVnrArea       0\n",
       "MiscVal          0\n",
       "MoSold           0\n",
       "OpenPorchSF      0\n",
       "OverallCond      0\n",
       "OverallQual      0\n",
       "PoolArea         0\n",
       "ScreenPorch      0\n",
       "TotRmsAbvGrd     0\n",
       "TotalBsmtSF      0\n",
       "WoodDeckSF       0\n",
       "YearBuilt        0\n",
       "YearRemodAdd     0\n",
       "YrSold           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill the numeric features\n",
    "numeric_features_filled = imp.transform(numeric_features)\n",
    "# convert numpy array to dataframe\n",
    "numeric_features_filled = pd.DataFrame(data = numeric_features_filled,columns = numeric_features.columns)\n",
    "\n",
    "numeric_features_filled.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Alley', 'BldgType', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1',\n",
      "       'BsmtFinType2', 'BsmtQual', 'CentralAir', 'Condition1', 'Condition2',\n",
      "       'Electrical', 'ExterCond', 'ExterQual', 'Exterior1st', 'Exterior2nd',\n",
      "       'Fence', 'FireplaceQu', 'Foundation', 'Functional', 'GarageCond',\n",
      "       'GarageFinish', 'GarageQual', 'GarageType', 'Heating', 'HeatingQC',\n",
      "       'HouseStyle', 'KitchenQual', 'LandContour', 'LandSlope', 'LotConfig',\n",
      "       'LotShape', 'MSZoning', 'MasVnrType', 'MiscFeature', 'Neighborhood',\n",
      "       'PavedDrive', 'PoolQC', 'RoofMatl', 'RoofStyle', 'SaleCondition',\n",
      "       'SaleType', 'Street', 'Utilities'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find categorical features\n",
    "categorical_features = all_data.select_dtypes(exclude=[np.number])\n",
    "print(categorical_features.columns)\n",
    "len(categorical_features.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show number of missing value in category variables\n",
    "nulls = pd.DataFrame(categorical_features.isnull().sum().sort_values(ascending=False))\n",
    "nulls.columns = ['Null Count']\n",
    "nulls.index.name = 'Feature'\n",
    "\n",
    "y_pos = np.arange(len(nulls))\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.barh(y_pos, nulls['Null Count'], align='center', alpha=0.5)\n",
    "plt.yticks(y_pos, nulls.index)\n",
    "plt.xlabel('number of missing value')\n",
    "plt.title('number of missing value in 43 category variables')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a copy\n",
    "categorical_features_filled = categorical_features.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features_filled[\"PoolQC\"] = categorical_features_filled[\"PoolQC\"].fillna(\"None\")\n",
    "categorical_features_filled[\"MiscFeature\"] = categorical_features_filled[\"MiscFeature\"].fillna(\"None\")\n",
    "categorical_features_filled[\"Alley\"] = categorical_features_filled[\"Alley\"].fillna(\"None\")\n",
    "categorical_features_filled[\"Fence\"] = categorical_features_filled[\"Fence\"].fillna(\"None\")\n",
    "categorical_features_filled[\"FireplaceQu\"] = categorical_features_filled[\"FireplaceQu\"].fillna(\"None\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features_filled = categorical_features_filled.apply(lambda x:x.fillna(x.value_counts().index[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alley            0\n",
       "BldgType         0\n",
       "BsmtCond         0\n",
       "BsmtExposure     0\n",
       "BsmtFinType1     0\n",
       "BsmtFinType2     0\n",
       "BsmtQual         0\n",
       "CentralAir       0\n",
       "Condition1       0\n",
       "Condition2       0\n",
       "Electrical       0\n",
       "ExterCond        0\n",
       "ExterQual        0\n",
       "Exterior1st      0\n",
       "Exterior2nd      0\n",
       "Fence            0\n",
       "FireplaceQu      0\n",
       "Foundation       0\n",
       "Functional       0\n",
       "GarageCond       0\n",
       "GarageFinish     0\n",
       "GarageQual       0\n",
       "GarageType       0\n",
       "Heating          0\n",
       "HeatingQC        0\n",
       "HouseStyle       0\n",
       "KitchenQual      0\n",
       "LandContour      0\n",
       "LandSlope        0\n",
       "LotConfig        0\n",
       "LotShape         0\n",
       "MSZoning         0\n",
       "MasVnrType       0\n",
       "MiscFeature      0\n",
       "Neighborhood     0\n",
       "PavedDrive       0\n",
       "PoolQC           0\n",
       "RoofMatl         0\n",
       "RoofStyle        0\n",
       "SaleCondition    0\n",
       "SaleType         0\n",
       "Street           0\n",
       "Utilities        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_features_filled.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply Box Cox Transformation for Numeric Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Skew in numerical features: \n",
      "\n",
      "There are 36 skewed numerical features to Box Cox transform\n",
      "   1stFlrSF  2ndFlrSF  3SsnPorch  BedroomAbvGr  BsmtFinSF1  BsmtFinSF2  \\\n",
      "0   856.000   854.000      0.000         3.000     706.000       0.000   \n",
      "1  1262.000     0.000      0.000         3.000     978.000       0.000   \n",
      "2   920.000   866.000      0.000         3.000     486.000       0.000   \n",
      "3   961.000   756.000      0.000         3.000     216.000       0.000   \n",
      "4  1145.000  1053.000      0.000         4.000     655.000       0.000   \n",
      "\n",
      "   BsmtFullBath  BsmtHalfBath  BsmtUnfSF  EnclosedPorch  ...  OverallCond  \\\n",
      "0         1.000         0.000    150.000          0.000  ...        5.000   \n",
      "1         0.000         1.000    284.000          0.000  ...        8.000   \n",
      "2         1.000         0.000    434.000          0.000  ...        5.000   \n",
      "3         1.000         0.000    540.000        272.000  ...        5.000   \n",
      "4         1.000         0.000    490.000          0.000  ...        5.000   \n",
      "\n",
      "   OverallQual  PoolArea  ScreenPorch  TotRmsAbvGrd  TotalBsmtSF  WoodDeckSF  \\\n",
      "0        7.000     0.000        0.000         8.000      856.000       0.000   \n",
      "1        6.000     0.000        0.000         6.000     1262.000     298.000   \n",
      "2        7.000     0.000        0.000         6.000      920.000       0.000   \n",
      "3        7.000     0.000        0.000         7.000      756.000       0.000   \n",
      "4        8.000     0.000        0.000         9.000     1145.000     192.000   \n",
      "\n",
      "   YearBuilt  YearRemodAdd   YrSold  \n",
      "0   2003.000      2003.000 2008.000  \n",
      "1   1976.000      1976.000 2007.000  \n",
      "2   2001.000      2002.000 2008.000  \n",
      "3   1915.000      1970.000 2006.000  \n",
      "4   2000.000      2000.000 2008.000  \n",
      "\n",
      "[5 rows x 36 columns]\n",
      "   1stFlrSF  2ndFlrSF  3SsnPorch  BedroomAbvGr  BsmtFinSF1  BsmtFinSF2  \\\n",
      "0    11.693    11.686      0.000         1.541      11.170       0.000   \n",
      "1    12.792     0.000      0.000         1.541      12.063       0.000   \n",
      "2    11.892    11.725      0.000         1.541      10.200       0.000   \n",
      "3    12.014    11.354      0.000         1.541       8.274       0.000   \n",
      "4    12.511    12.271      0.000         1.820      10.971       0.000   \n",
      "\n",
      "   BsmtFullBath  BsmtHalfBath  BsmtUnfSF  EnclosedPorch  ...  OverallCond  \\\n",
      "0         0.730         0.000      7.483          0.000  ...        2.056   \n",
      "1         0.000         0.730      8.898          0.000  ...        2.603   \n",
      "2         0.730         0.000      9.917          0.000  ...        2.056   \n",
      "3         0.730         0.000     10.468          8.798  ...        2.056   \n",
      "4         0.730         0.000     10.221          0.000  ...        2.056   \n",
      "\n",
      "   OverallQual  PoolArea  ScreenPorch  TotRmsAbvGrd  TotalBsmtSF  WoodDeckSF  \\\n",
      "0        2.440     0.000        0.000         2.603       11.693       0.000   \n",
      "1        2.260     0.000        0.000         2.260       12.792       9.010   \n",
      "2        2.440     0.000        0.000         2.260       11.892       0.000   \n",
      "3        2.440     0.000        0.000         2.440       11.354       0.000   \n",
      "4        2.603     0.000        0.000         2.750       12.511       8.014   \n",
      "\n",
      "   YearBuilt  YearRemodAdd  YrSold  \n",
      "0     14.188        14.188  14.195  \n",
      "1     14.145        14.145  14.194  \n",
      "2     14.184        14.186  14.195  \n",
      "3     14.048        14.136  14.192  \n",
      "4     14.183        14.183  14.195  \n",
      "\n",
      "[5 rows x 36 columns]\n"
     ]
    }
   ],
   "source": [
    "skewed_feats = numeric_features_filled.apply(lambda x: skew(x.dropna())).sort_values(ascending=False)\n",
    "print(\"\\nSkew in numerical features: \\n\")\n",
    "skewness = pd.DataFrame({'Skew' :skewed_feats})\n",
    "skewness.head(10)\n",
    "\n",
    "\n",
    "\n",
    "skewness = skewness[abs(skewness) > 0.75]\n",
    "print(\"There are {} skewed numerical features to Box Cox transform\".format(skewness.shape[0]))\n",
    "\n",
    "from scipy.special import boxcox1p\n",
    "skewed_features = skewness.index\n",
    "lam = 0.15\n",
    "for feat in skewed_features:\n",
    "    #all_data[feat] += 1\n",
    "    numeric_features_filled_box = boxcox1p(numeric_features_filled, lam)\n",
    "\n",
    "print(numeric_features_filled.head(5))\n",
    "print(numeric_features_filled_box.head(5))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply One Hot Encoding to Categorical Features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Alley_None</th>\n",
       "      <th>Alley_Pave</th>\n",
       "      <th>BldgType_2fmCon</th>\n",
       "      <th>BldgType_Duplex</th>\n",
       "      <th>BldgType_Twnhs</th>\n",
       "      <th>BldgType_TwnhsE</th>\n",
       "      <th>BsmtCond_Gd</th>\n",
       "      <th>BsmtCond_Po</th>\n",
       "      <th>BsmtCond_TA</th>\n",
       "      <th>BsmtExposure_Gd</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_CWD</th>\n",
       "      <th>SaleType_Con</th>\n",
       "      <th>SaleType_ConLD</th>\n",
       "      <th>SaleType_ConLI</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>Street_Pave</th>\n",
       "      <th>Utilities_NoSeWa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 214 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Alley_None  Alley_Pave  BldgType_2fmCon  BldgType_Duplex  BldgType_Twnhs  \\\n",
       "0           1           0                0                0               0   \n",
       "1           1           0                0                0               0   \n",
       "2           1           0                0                0               0   \n",
       "\n",
       "   BldgType_TwnhsE  BsmtCond_Gd  BsmtCond_Po  BsmtCond_TA  BsmtExposure_Gd  \\\n",
       "0                0            0            0            1                0   \n",
       "1                0            0            0            1                1   \n",
       "2                0            0            0            1                0   \n",
       "\n",
       "   ...  SaleType_CWD  SaleType_Con  SaleType_ConLD  SaleType_ConLI  \\\n",
       "0  ...             0             0               0               0   \n",
       "1  ...             0             0               0               0   \n",
       "2  ...             0             0               0               0   \n",
       "\n",
       "   SaleType_ConLw  SaleType_New  SaleType_Oth  SaleType_WD  Street_Pave  \\\n",
       "0               0             0             0            1            1   \n",
       "1               0             0             0            1            1   \n",
       "2               0             0             0            1            1   \n",
       "\n",
       "   Utilities_NoSeWa  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "\n",
       "[3 rows x 214 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# convert to string\n",
    "categorical_features_filled = categorical_features_filled.apply(lambda x:x.astype(str))\n",
    "\n",
    "# Create the encoder.\n",
    "onehot = pd.get_dummies(categorical_features_filled,drop_first=True)\n",
    "\n",
    "onehot.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_CWD</th>\n",
       "      <th>SaleType_Con</th>\n",
       "      <th>SaleType_ConLD</th>\n",
       "      <th>SaleType_ConLI</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>Street_Pave</th>\n",
       "      <th>Utilities_NoSeWa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.693</td>\n",
       "      <td>11.686</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.541</td>\n",
       "      <td>11.170</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7.483</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.792</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.541</td>\n",
       "      <td>12.063</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.730</td>\n",
       "      <td>8.898</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.892</td>\n",
       "      <td>11.725</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.541</td>\n",
       "      <td>10.200</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.000</td>\n",
       "      <td>9.917</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.014</td>\n",
       "      <td>11.354</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.541</td>\n",
       "      <td>8.274</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.000</td>\n",
       "      <td>10.468</td>\n",
       "      <td>8.798</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.511</td>\n",
       "      <td>12.271</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.820</td>\n",
       "      <td>10.971</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.000</td>\n",
       "      <td>10.221</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 250 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   1stFlrSF  2ndFlrSF  3SsnPorch  BedroomAbvGr  BsmtFinSF1  BsmtFinSF2  \\\n",
       "0    11.693    11.686      0.000         1.541      11.170       0.000   \n",
       "1    12.792     0.000      0.000         1.541      12.063       0.000   \n",
       "2    11.892    11.725      0.000         1.541      10.200       0.000   \n",
       "3    12.014    11.354      0.000         1.541       8.274       0.000   \n",
       "4    12.511    12.271      0.000         1.820      10.971       0.000   \n",
       "\n",
       "   BsmtFullBath  BsmtHalfBath  BsmtUnfSF  EnclosedPorch  ...  SaleType_CWD  \\\n",
       "0         0.730         0.000      7.483          0.000  ...             0   \n",
       "1         0.000         0.730      8.898          0.000  ...             0   \n",
       "2         0.730         0.000      9.917          0.000  ...             0   \n",
       "3         0.730         0.000     10.468          8.798  ...             0   \n",
       "4         0.730         0.000     10.221          0.000  ...             0   \n",
       "\n",
       "   SaleType_Con  SaleType_ConLD  SaleType_ConLI  SaleType_ConLw  SaleType_New  \\\n",
       "0             0               0               0               0             0   \n",
       "1             0               0               0               0             0   \n",
       "2             0               0               0               0             0   \n",
       "3             0               0               0               0             0   \n",
       "4             0               0               0               0             0   \n",
       "\n",
       "   SaleType_Oth  SaleType_WD  Street_Pave  Utilities_NoSeWa  \n",
       "0             0            1            1                 0  \n",
       "1             0            1            1                 0  \n",
       "2             0            1            1                 0  \n",
       "3             0            1            1                 0  \n",
       "4             0            1            1                 0  \n",
       "\n",
       "[5 rows x 250 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data_processed = pd.concat([numeric_features_filled_box, onehot], axis=1)\n",
    "all_data_processed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_processed = all_data_processed[:ntrain]\n",
    "test_processed = all_data_processed[ntrain:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Modelling\n",
    "Remark:\n",
    "<br>\n",
    "fit(train_processed, y_train)\n",
    "<br>\n",
    "predict(test_processed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from joblib import dump, load\n",
    "k_fold = model_selection.KFold(n_splits=5, random_state=123)\n",
    "X = train_processed\n",
    "Y = y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Construct Grid Search CV "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.5, 0.6000000000000001]\n",
      "[10, 50, 90]\n",
      "[10, 100, 1000]\n",
      "[50, 60, 70, 80, 90]\n"
     ]
    }
   ],
   "source": [
    "# set grid search parameter value\n",
    "max_features = [i*0.1 for i in range(5, 7, 1)]\n",
    "max_depth = [i for i in range(10, 100, 40)]\n",
    "max_leaf_nodes = [10, 100, 1000]\n",
    "n_estimators = [i for i in range(50, 100, 10)]\n",
    "\n",
    "print(max_features)\n",
    "print(max_depth)\n",
    "print(max_leaf_nodes)\n",
    "print(n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model is not exist, now create & save model\n",
      "------------------------------------------------------------\n",
      "Model Summary:\n",
      "<bound method BaseSearchCV.fit of GridSearchCV(cv=KFold(n_splits=5, random_state=123, shuffle=False),\n",
      "             error_score='raise-deprecating',\n",
      "             estimator=RandomForestRegressor(bootstrap=True, criterion='mse',\n",
      "                                             max_depth=None,\n",
      "                                             max_features='auto',\n",
      "                                             max_leaf_nodes=None,\n",
      "                                             min_impurity_decrease=0.0,\n",
      "                                             min_impurity_split=None,\n",
      "                                             min_samples_leaf=1,\n",
      "                                             min_samples_split=2,\n",
      "                                             min_weight_fraction_leaf=0.0,\n",
      "                                             n_estimators='warn', n_jobs=None,\n",
      "                                             oob_score=False, random_state=None,\n",
      "                                             verbose=0, warm_start=False),\n",
      "             iid='warn', n_jobs=-1,\n",
      "             param_grid={'max_depth': [10, 50, 90],\n",
      "                         'max_features': [0.5, 0.6000000000000001],\n",
      "                         'max_leaf_nodes': [10, 100, 1000],\n",
      "                         'n_estimators': [50, 60, 70, 80, 90]},\n",
      "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
      "             scoring='neg_mean_squared_log_error', verbose=0)>\n"
     ]
    }
   ],
   "source": [
    "# Check if object exist\n",
    "try:\n",
    "    load('RF.joblib') \n",
    "except FileNotFoundError:\n",
    "    print(\"The model is not exist, now create & save model\")\n",
    "    \n",
    "    from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "    \n",
    "    rf_grid = {'max_features': max_features, 'max_depth': max_depth,\n",
    "              'max_leaf_nodes': max_leaf_nodes, 'n_estimators': n_estimators}\n",
    "    rf_model = GridSearchCV( RandomForestRegressor(),\n",
    "                             rf_grid, cv=k_fold, scoring='neg_mean_squared_log_error',\n",
    "                             return_train_score=True, n_jobs=-1)\n",
    "    rf_model.fit(X,Y)\n",
    "    # Save model in current folder \n",
    "    dump(rf_model, 'RF.joblib')\n",
    "else:\n",
    "    rf_model = load('RF.joblib')\n",
    "    print(\"Reload existing model\")\n",
    "\n",
    "print('------------------------------------------------------------')\n",
    "print('Model Summary:')\n",
    "print(rf_model.fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_axis = rf_model.cv_results_['param_n_estimators'].data\n",
    "Y_axis = rf_model.cv_results_['param_max_depth'].data\n",
    "test_axis = abs(rf_model.cv_results_['mean_test_score'])\n",
    "train_axis= abs(rf_model.cv_results_['mean_train_score'])\n",
    "\n",
    "plt.figure(figsize=(8,5))\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.scatter(X_axis, Y_axis, test_axis, c='red', label='test data')\n",
    "ax.scatter(X_axis, Y_axis, train_axis, c='blue', label='train data')\n",
    "\n",
    "plt.title('Grid Search Result of RF')\n",
    "ax.set_xlabel('n_estimators')\n",
    "ax.set_ylabel('max_depth')\n",
    "ax.set_zlabel('MSLE')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.04072179 0.04082328 0.04053426 0.04105495 0.04130339 0.02255225\n",
      " 0.0220335  0.02239177 0.02214585 0.02165145 0.0213847  0.02123347\n",
      " 0.02141642 0.02114962 0.02108772 0.04055512 0.04064544 0.04090196\n",
      " 0.04065752 0.04073181 0.02227217 0.02259806 0.02244699 0.02237921\n",
      " 0.02243914 0.02139494 0.0213858  0.02117013 0.02127029 0.02142275\n",
      " 0.04135632 0.0406183  0.04152557 0.0406587  0.04116614 0.02289854\n",
      " 0.02244257 0.02237049 0.02210604 0.02217711 0.02096769 0.02092945\n",
      " 0.02109531 0.0208704  0.02048156 0.04117825 0.0408191  0.04074113\n",
      " 0.04051377 0.04032831 0.02233196 0.0224088  0.02223774 0.02252136\n",
      " 0.02214805 0.02069921 0.02085126 0.02044538 0.02087714 0.02085647\n",
      " 0.04046108 0.04115694 0.04082807 0.04097391 0.04089888 0.02245627\n",
      " 0.02190182 0.02232311 0.02220752 0.02213793 0.02074477 0.02084033\n",
      " 0.02095299 0.02087543 0.02055688 0.04070176 0.04072096 0.04085471\n",
      " 0.04111725 0.04079078 0.0228437  0.02242091 0.02188105 0.02205268\n",
      " 0.02202954 0.02139244 0.0208405  0.02067538 0.02100646 0.02090419]\n"
     ]
    }
   ],
   "source": [
    "# print the mean test score\n",
    "print(test_axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSLE in:\n",
      "Test Data: 0.020445381173809056\n",
      "Train Data: 0.003606418729153768\n",
      "------------------------------------------------------------\n",
      "Parameters:\n",
      "max_features: 0.6000000000000001\n",
      "param_max_depth: 50\n",
      "max_leaf_nodes: 1000\n",
      "param_n_estimators: 70\n"
     ]
    }
   ],
   "source": [
    "# print the best estimator summary \n",
    "print('MSLE in:')\n",
    "print('Test Data:',abs(rf_model.cv_results_['mean_test_score'][rf_model.best_index_]))\n",
    "print('Train Data:',abs(rf_model.cv_results_['mean_train_score'][rf_model.best_index_]))\n",
    "print('------------------------------------------------------------')\n",
    "print('Parameters:')\n",
    "print('max_features:',rf_model.cv_results_['param_max_features'][rf_model.best_index_])\n",
    "print('param_max_depth:',rf_model.cv_results_['param_max_depth'][rf_model.best_index_])\n",
    "print('max_leaf_nodes:',rf_model.cv_results_['param_max_leaf_nodes'][rf_model.best_index_])\n",
    "print('param_n_estimators:',rf_model.cv_results_['param_n_estimators'][rf_model.best_index_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.42007637, 0.49627156, 0.58343806, 0.67559175, 0.77406864,\n",
       "        0.86509233, 1.06614599, 1.24855876, 1.57555418, 1.91506901,\n",
       "        1.18972106, 1.3837965 , 1.61337156, 1.82026706, 2.16207223,\n",
       "        0.59632359, 0.6293396 , 0.73131332, 0.85799479, 0.94946032,\n",
       "        1.09068184, 1.31169052, 1.54726949, 1.77620978, 2.00647378,\n",
       "        1.35119696, 1.64266019, 2.11457415, 2.36551247, 2.77388721,\n",
       "        0.52132092, 0.62074904, 0.69214706, 0.75240798, 0.85553341,\n",
       "        0.98401384, 1.24727621, 1.48223877, 1.60324244, 1.822861  ,\n",
       "        1.78882751, 2.17956777, 2.49233031, 2.94831004, 3.17310777,\n",
       "        0.57466187, 0.67040563, 0.78011265, 0.88682618, 0.97898026,\n",
       "        1.198592  , 1.4321682 , 1.67252464, 2.06910338, 2.3120223 ,\n",
       "        2.19416685, 2.5459897 , 3.0879457 , 3.73475566, 3.85206165,\n",
       "        0.52243767, 0.60471268, 0.70084047, 0.79108262, 0.87585635,\n",
       "        1.00630732, 1.21117663, 1.4229569 , 1.67507248, 1.90185843,\n",
       "        1.76925263, 2.14986634, 2.48883038, 3.00516591, 3.18823395,\n",
       "        0.58155026, 0.68835797, 0.79846392, 0.88862157, 1.00810275,\n",
       "        1.1764565 , 1.43323913, 1.70115399, 1.99117155, 2.18427081,\n",
       "        2.06003709, 2.47347431, 2.96051407, 3.50550432, 3.30027146]),\n",
       " 'std_fit_time': array([0.00635193, 0.00370975, 0.01030655, 0.01388552, 0.01818481,\n",
       "        0.01140808, 0.0101973 , 0.00941281, 0.03766186, 0.12404286,\n",
       "        0.08156181, 0.05600762, 0.05678542, 0.06330547, 0.04633548,\n",
       "        0.05598718, 0.00898485, 0.00752042, 0.02848616, 0.01393404,\n",
       "        0.00911072, 0.0160856 , 0.01013951, 0.02084652, 0.01762459,\n",
       "        0.01850159, 0.03753885, 0.1190019 , 0.14390338, 0.04645852,\n",
       "        0.03746832, 0.0180282 , 0.02139998, 0.01267209, 0.02986417,\n",
       "        0.00737306, 0.019704  , 0.02874398, 0.01464445, 0.01588908,\n",
       "        0.02399945, 0.01641935, 0.03748137, 0.12376041, 0.13718807,\n",
       "        0.03333789, 0.01063779, 0.00298567, 0.00622506, 0.01580146,\n",
       "        0.00747377, 0.01491342, 0.01209991, 0.06848044, 0.02746208,\n",
       "        0.02417072, 0.04658478, 0.11573815, 0.18709034, 0.14542066,\n",
       "        0.02932629, 0.01495901, 0.02164119, 0.01344654, 0.01153517,\n",
       "        0.01713594, 0.00486014, 0.01336292, 0.06516758, 0.07572281,\n",
       "        0.05300305, 0.02906546, 0.06628427, 0.1314631 , 0.08010634,\n",
       "        0.03123443, 0.0157742 , 0.0175333 , 0.00971023, 0.00460911,\n",
       "        0.01482207, 0.01595697, 0.0245581 , 0.02052129, 0.05488311,\n",
       "        0.01406646, 0.0137    , 0.06858106, 0.16383607, 0.28335068]),\n",
       " 'mean_score_time': array([0.00797892, 0.00698147, 0.0081779 , 0.0085772 , 0.01008196,\n",
       "        0.00644984, 0.0083777 , 0.01136971, 0.01097074, 0.01157303,\n",
       "        0.00917506, 0.00977397, 0.01236677, 0.01376295, 0.0137639 ,\n",
       "        0.0066843 , 0.0104373 , 0.00658226, 0.00877662, 0.00937495,\n",
       "        0.00797853, 0.00857706, 0.00977426, 0.0107708 , 0.0121676 ,\n",
       "        0.00897641, 0.01057224, 0.01176877, 0.01376314, 0.0161562 ,\n",
       "        0.0077796 , 0.00877638, 0.00877652, 0.0091754 , 0.0101727 ,\n",
       "        0.00857658, 0.01037192, 0.01017203, 0.01136956, 0.01256623,\n",
       "        0.01136923, 0.01236672, 0.0137629 , 0.01555891, 0.01715493,\n",
       "        0.00718083, 0.00797896, 0.00877666, 0.00937495, 0.00997348,\n",
       "        0.0083777 , 0.00917521, 0.01077061, 0.01575785, 0.01446438,\n",
       "        0.01097112, 0.01216831, 0.01376266, 0.01691194, 0.01715407,\n",
       "        0.00737991, 0.00738053, 0.00857654, 0.01097059, 0.01017251,\n",
       "        0.0083775 , 0.00957341, 0.01077099, 0.01435599, 0.01316457,\n",
       "        0.01246939, 0.0121675 , 0.01486344, 0.0174561 , 0.01755338,\n",
       "        0.0067822 , 0.00757966, 0.00857663, 0.0093749 , 0.01037169,\n",
       "        0.0089766 , 0.00937529, 0.01187015, 0.01216712, 0.01276679,\n",
       "        0.01077161, 0.01256595, 0.01496072, 0.01695571, 0.01119704]),\n",
       " 'std_score_time': array([2.74969127e-03, 6.30977562e-04, 9.77739362e-04, 4.88889342e-04,\n",
       "        6.87645120e-03, 7.49720369e-03, 4.89182325e-04, 2.86272741e-03,\n",
       "        1.09205778e-03, 3.71862533e-04, 3.99708844e-04, 3.99113214e-04,\n",
       "        2.32569414e-03, 2.12974114e-03, 7.46544637e-04, 6.00941471e-04,\n",
       "        4.23243175e-03, 3.31378668e-03, 7.46658862e-04, 4.88830808e-04,\n",
       "        6.31279680e-04, 4.88092327e-04, 3.98659831e-04, 3.99709299e-04,\n",
       "        3.98922109e-04, 5.09122765e-07, 1.19707593e-03, 7.46467781e-04,\n",
       "        1.59540187e-03, 3.11542989e-03, 1.16303113e-03, 1.16281880e-03,\n",
       "        7.46378485e-04, 3.99423510e-04, 7.45690300e-04, 7.98261555e-04,\n",
       "        1.35261348e-03, 3.99876579e-04, 4.88733793e-04, 7.98535950e-04,\n",
       "        4.88909233e-04, 4.87974362e-04, 7.46340280e-04, 4.87897101e-04,\n",
       "        7.46544104e-04, 9.77155393e-04, 4.86280395e-07, 7.46671737e-04,\n",
       "        4.88831459e-04, 6.30525083e-04, 7.97915564e-04, 9.76961590e-04,\n",
       "        7.46110766e-04, 2.55413899e-03, 1.83909274e-03, 6.31203674e-04,\n",
       "        3.99518223e-04, 7.46340234e-04, 3.36637945e-03, 3.98350304e-04,\n",
       "        7.97260230e-04, 4.88675027e-04, 7.98535808e-04, 2.60017762e-03,\n",
       "        3.98564692e-04, 4.89142509e-04, 4.88774146e-04, 1.16314600e-03,\n",
       "        3.48149601e-03, 9.77077518e-04, 2.81991481e-03, 3.98850566e-04,\n",
       "        1.42415815e-03, 3.06226714e-03, 4.88460925e-04, 3.99184779e-04,\n",
       "        4.88110802e-04, 4.89006584e-04, 4.88674980e-04, 4.89201368e-04,\n",
       "        1.09236227e-03, 4.88169115e-04, 2.00920710e-03, 3.99160855e-04,\n",
       "        7.46085456e-04, 3.99161539e-04, 4.89960531e-04, 1.66906703e-03,\n",
       "        1.99468165e-03, 7.75004951e-04]),\n",
       " 'param_max_depth': masked_array(data=[10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
       "                    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
       "                    10, 10, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,\n",
       "                    50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,\n",
       "                    50, 50, 50, 50, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90,\n",
       "                    90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90,\n",
       "                    90, 90, 90, 90, 90, 90],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_max_features': masked_array(data=[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "                    0.5, 0.5, 0.5, 0.5, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.6000000000000001, 0.5, 0.5, 0.5,\n",
       "                    0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "                    0.5, 0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "                    0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "                    0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001, 0.6000000000000001,\n",
       "                    0.6000000000000001],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_max_leaf_nodes': masked_array(data=[10, 10, 10, 10, 10, 100, 100, 100, 100, 100, 1000,\n",
       "                    1000, 1000, 1000, 1000, 10, 10, 10, 10, 10, 100, 100,\n",
       "                    100, 100, 100, 1000, 1000, 1000, 1000, 1000, 10, 10,\n",
       "                    10, 10, 10, 100, 100, 100, 100, 100, 1000, 1000, 1000,\n",
       "                    1000, 1000, 10, 10, 10, 10, 10, 100, 100, 100, 100,\n",
       "                    100, 1000, 1000, 1000, 1000, 1000, 10, 10, 10, 10, 10,\n",
       "                    100, 100, 100, 100, 100, 1000, 1000, 1000, 1000, 1000,\n",
       "                    10, 10, 10, 10, 10, 100, 100, 100, 100, 100, 1000,\n",
       "                    1000, 1000, 1000, 1000],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_n_estimators': masked_array(data=[50, 60, 70, 80, 90, 50, 60, 70, 80, 90, 50, 60, 70, 80,\n",
       "                    90, 50, 60, 70, 80, 90, 50, 60, 70, 80, 90, 50, 60, 70,\n",
       "                    80, 90, 50, 60, 70, 80, 90, 50, 60, 70, 80, 90, 50, 60,\n",
       "                    70, 80, 90, 50, 60, 70, 80, 90, 50, 60, 70, 80, 90, 50,\n",
       "                    60, 70, 80, 90, 50, 60, 70, 80, 90, 50, 60, 70, 80, 90,\n",
       "                    50, 60, 70, 80, 90, 50, 60, 70, 80, 90, 50, 60, 70, 80,\n",
       "                    90, 50, 60, 70, 80, 90],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'max_depth': 10,\n",
       "   'max_features': 0.5,\n",
       "   'max_leaf_nodes': 10,\n",
       "   'n_estimators': 50},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.5,\n",
       "   'max_leaf_nodes': 10,\n",
       "   'n_estimators': 60},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.5,\n",
       "   'max_leaf_nodes': 10,\n",
       "   'n_estimators': 70},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.5,\n",
       "   'max_leaf_nodes': 10,\n",
       "   'n_estimators': 80},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.5,\n",
       "   'max_leaf_nodes': 10,\n",
       "   'n_estimators': 90},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.5,\n",
       "   'max_leaf_nodes': 100,\n",
       "   'n_estimators': 50},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.5,\n",
       "   'max_leaf_nodes': 100,\n",
       "   'n_estimators': 60},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.5,\n",
       "   'max_leaf_nodes': 100,\n",
       "   'n_estimators': 70},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.5,\n",
       "   'max_leaf_nodes': 100,\n",
       "   'n_estimators': 80},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.5,\n",
       "   'max_leaf_nodes': 100,\n",
       "   'n_estimators': 90},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.5,\n",
       "   'max_leaf_nodes': 1000,\n",
       "   'n_estimators': 50},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.5,\n",
       "   'max_leaf_nodes': 1000,\n",
       "   'n_estimators': 60},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.5,\n",
       "   'max_leaf_nodes': 1000,\n",
       "   'n_estimators': 70},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.5,\n",
       "   'max_leaf_nodes': 1000,\n",
       "   'n_estimators': 80},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.5,\n",
       "   'max_leaf_nodes': 1000,\n",
       "   'n_estimators': 90},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 10,\n",
       "   'n_estimators': 50},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 10,\n",
       "   'n_estimators': 60},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 10,\n",
       "   'n_estimators': 70},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 10,\n",
       "   'n_estimators': 80},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 10,\n",
       "   'n_estimators': 90},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 100,\n",
       "   'n_estimators': 50},\n",
       "  {'max_depth': 10,\n",
       "   'max_features': 0.6000000000000001,\n",
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       "  {'max_depth': 50,\n",
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       "   'n_estimators': 80},\n",
       "  {'max_depth': 50,\n",
       "   'max_features': 0.6000000000000001,\n",
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       "  {'max_depth': 90,\n",
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       "  {'max_depth': 90,\n",
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       "  {'max_depth': 90,\n",
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       "   'n_estimators': 70},\n",
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       "   'n_estimators': 80},\n",
       "  {'max_depth': 90,\n",
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       "   'n_estimators': 90},\n",
       "  {'max_depth': 90,\n",
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       "   'max_leaf_nodes': 1000,\n",
       "   'n_estimators': 50},\n",
       "  {'max_depth': 90,\n",
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       "   'max_leaf_nodes': 1000,\n",
       "   'n_estimators': 60},\n",
       "  {'max_depth': 90,\n",
       "   'max_features': 0.5,\n",
       "   'max_leaf_nodes': 1000,\n",
       "   'n_estimators': 70},\n",
       "  {'max_depth': 90,\n",
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       "  {'max_depth': 90,\n",
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       "   'max_leaf_nodes': 1000,\n",
       "   'n_estimators': 90},\n",
       "  {'max_depth': 90,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 10,\n",
       "   'n_estimators': 50},\n",
       "  {'max_depth': 90,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 10,\n",
       "   'n_estimators': 60},\n",
       "  {'max_depth': 90,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 10,\n",
       "   'n_estimators': 70},\n",
       "  {'max_depth': 90,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 10,\n",
       "   'n_estimators': 80},\n",
       "  {'max_depth': 90,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 10,\n",
       "   'n_estimators': 90},\n",
       "  {'max_depth': 90,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 100,\n",
       "   'n_estimators': 50},\n",
       "  {'max_depth': 90,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 100,\n",
       "   'n_estimators': 60},\n",
       "  {'max_depth': 90,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 100,\n",
       "   'n_estimators': 70},\n",
       "  {'max_depth': 90,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 100,\n",
       "   'n_estimators': 80},\n",
       "  {'max_depth': 90,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 100,\n",
       "   'n_estimators': 90},\n",
       "  {'max_depth': 90,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 1000,\n",
       "   'n_estimators': 50},\n",
       "  {'max_depth': 90,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 1000,\n",
       "   'n_estimators': 60},\n",
       "  {'max_depth': 90,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 1000,\n",
       "   'n_estimators': 70},\n",
       "  {'max_depth': 90,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 1000,\n",
       "   'n_estimators': 80},\n",
       "  {'max_depth': 90,\n",
       "   'max_features': 0.6000000000000001,\n",
       "   'max_leaf_nodes': 1000,\n",
       "   'n_estimators': 90}],\n",
       " 'split0_test_score': array([-0.03609456, -0.0351096 , -0.03583608, -0.0360881 , -0.03598258,\n",
       "        -0.01849453, -0.01906319, -0.01933865, -0.01896315, -0.01826172,\n",
       "        -0.0183994 , -0.01752259, -0.01782595, -0.01784485, -0.01790305,\n",
       "        -0.03587524, -0.03641861, -0.03622997, -0.03629607, -0.0367026 ,\n",
       "        -0.01864231, -0.01865531, -0.01943019, -0.01898097, -0.01841602,\n",
       "        -0.01774967, -0.01777575, -0.01788067, -0.01824476, -0.01828063,\n",
       "        -0.03566341, -0.03692271, -0.03574194, -0.03570396, -0.03507512,\n",
       "        -0.01933895, -0.01902251, -0.01842142, -0.01893272, -0.01817739,\n",
       "        -0.01745468, -0.01799288, -0.01754894, -0.01740635, -0.01783147,\n",
       "        -0.03569328, -0.03693631, -0.03675617, -0.03701803, -0.03598369,\n",
       "        -0.01891255, -0.01867812, -0.01853826, -0.01932569, -0.01914846,\n",
       "        -0.01750903, -0.01773784, -0.01779547, -0.01747196, -0.01763431,\n",
       "        -0.03566806, -0.03542218, -0.03581776, -0.0347684 , -0.03650467,\n",
       "        -0.01853411, -0.01784071, -0.01821047, -0.01853211, -0.01936176,\n",
       "        -0.01798336, -0.01803152, -0.017472  , -0.01757195, -0.017836  ,\n",
       "        -0.03543117, -0.03603551, -0.0371673 , -0.03591566, -0.03653612,\n",
       "        -0.01886708, -0.01858564, -0.01854195, -0.01871141, -0.01861658,\n",
       "        -0.01761327, -0.01757441, -0.01756815, -0.01801771, -0.01777986]),\n",
       " 'split1_test_score': array([-0.04538976, -0.04601123, -0.04617802, -0.04743033, -0.04739941,\n",
       "        -0.02657399, -0.02606016, -0.02635538, -0.02552006, -0.02548829,\n",
       "        -0.02463085, -0.02454695, -0.02568857, -0.02552009, -0.02453957,\n",
       "        -0.04611287, -0.04644929, -0.04689538, -0.04527696, -0.04663209,\n",
       "        -0.0254582 , -0.02569382, -0.02514144, -0.02594433, -0.02582754,\n",
       "        -0.02460815, -0.0244421 , -0.02530318, -0.02497451, -0.02387404,\n",
       "        -0.0475117 , -0.04585178, -0.0470734 , -0.04726493, -0.04715635,\n",
       "        -0.02698217, -0.02543226, -0.02679243, -0.02620956, -0.02575901,\n",
       "        -0.0242322 , -0.02570742, -0.02505332, -0.02470105, -0.02419351,\n",
       "        -0.04681909, -0.04545811, -0.04472487, -0.04437241, -0.04565423,\n",
       "        -0.02661687, -0.0250976 , -0.02575028, -0.02582749, -0.02582004,\n",
       "        -0.02406188, -0.02402753, -0.02334279, -0.02454682, -0.02409118,\n",
       "        -0.04598922, -0.04710862, -0.04638249, -0.04633262, -0.0449344 ,\n",
       "        -0.02621735, -0.02614915, -0.02646651, -0.02663069, -0.02604703,\n",
       "        -0.02463797, -0.02461627, -0.02323853, -0.02473605, -0.02388678,\n",
       "        -0.04517678, -0.04554585, -0.04632155, -0.04585384, -0.0453951 ,\n",
       "        -0.02659251, -0.02548368, -0.02599242, -0.02553064, -0.025837  ,\n",
       "        -0.02446297, -0.02478682, -0.02498584, -0.02468221, -0.02459471]),\n",
       " 'split2_test_score': array([-0.0414503 , -0.04109938, -0.04092616, -0.04033415, -0.04219583,\n",
       "        -0.02288075, -0.02208423, -0.02181679, -0.02292975, -0.02236469,\n",
       "        -0.02229624, -0.02064537, -0.02131569, -0.02132696, -0.022106  ,\n",
       "        -0.04118616, -0.04065133, -0.04120578, -0.04028661, -0.04067098,\n",
       "        -0.02379996, -0.02293546, -0.0224894 , -0.02232362, -0.02247776,\n",
       "        -0.02098473, -0.02154938, -0.02100911, -0.02155661, -0.02141794,\n",
       "        -0.04149426, -0.04053118, -0.04262858, -0.04126498, -0.04231618,\n",
       "        -0.02282608, -0.02329434, -0.02257251, -0.02268492, -0.0224577 ,\n",
       "        -0.02117589, -0.02134754, -0.02193904, -0.02166238, -0.02030909,\n",
       "        -0.0420935 , -0.04185421, -0.04090259, -0.04089656, -0.0395089 ,\n",
       "        -0.02170565, -0.02259596, -0.02238342, -0.02278218, -0.02225601,\n",
       "        -0.02137928, -0.0205232 , -0.0200897 , -0.02032086, -0.02106015,\n",
       "        -0.04104927, -0.04202007, -0.04065207, -0.04279023, -0.04149947,\n",
       "        -0.02273346, -0.0216121 , -0.02243385, -0.0230045 , -0.02204039,\n",
       "        -0.02071738, -0.02077467, -0.02126261, -0.02121971, -0.02050723,\n",
       "        -0.04214801, -0.0400978 , -0.03944239, -0.04129018, -0.04077063,\n",
       "        -0.02289934, -0.02309453, -0.02154503, -0.0222395 , -0.02202159,\n",
       "        -0.02244143, -0.02036963, -0.02070402, -0.0208178 , -0.02116086]),\n",
       " 'split3_test_score': array([-0.04139445, -0.0416585 , -0.0408316 , -0.04130533, -0.04104622,\n",
       "        -0.01975662, -0.02074741, -0.02046421, -0.02027989, -0.020246  ,\n",
       "        -0.01881003, -0.01949793, -0.01960527, -0.01917909, -0.01920043,\n",
       "        -0.04072188, -0.04041478, -0.04114036, -0.04161319, -0.04100656,\n",
       "        -0.02095022, -0.02044796, -0.02018343, -0.02059156, -0.02141805,\n",
       "        -0.0200951 , -0.01942125, -0.01894015, -0.01926265, -0.02027797,\n",
       "        -0.04109073, -0.04024779, -0.04193324, -0.04023923, -0.04176508,\n",
       "        -0.02104203, -0.02037632, -0.02026471, -0.01990349, -0.02088502,\n",
       "        -0.02019749, -0.01750196, -0.01888511, -0.01856072, -0.01818839,\n",
       "        -0.04066975, -0.04124341, -0.0415218 , -0.04111799, -0.04133752,\n",
       "        -0.0209352 , -0.02113286, -0.02073364, -0.02090254, -0.02076963,\n",
       "        -0.01860994, -0.01938039, -0.01877821, -0.01900118, -0.01816815,\n",
       "        -0.04069337, -0.04090288, -0.04089258, -0.04101138, -0.04159902,\n",
       "        -0.02140222, -0.02060168, -0.02111557, -0.01978963, -0.0205236 ,\n",
       "        -0.01853051, -0.01880602, -0.01946077, -0.01894221, -0.01828019,\n",
       "        -0.04078385, -0.04105949, -0.04174714, -0.04125574, -0.04098995,\n",
       "        -0.02122118, -0.02129352, -0.01960198, -0.02041191, -0.02030391,\n",
       "        -0.01942094, -0.01892979, -0.01832316, -0.01838446, -0.0187327 ]),\n",
       " 'split4_test_score': array([-0.03927989, -0.04023768, -0.03889942, -0.04011686, -0.03989291,\n",
       "        -0.02505537, -0.02221251, -0.02398384, -0.02303639, -0.02189655,\n",
       "        -0.02278695, -0.02395453, -0.02264662, -0.0218771 , -0.02168954,\n",
       "        -0.03887944, -0.0392932 , -0.03903832, -0.03981475, -0.03864681,\n",
       "        -0.02251016, -0.02525774, -0.02499049, -0.02405555, -0.02405636,\n",
       "        -0.02353704, -0.02374052, -0.02271753, -0.02231291, -0.02326319,\n",
       "        -0.04102149, -0.03953806, -0.04025067, -0.03882041, -0.03951798,\n",
       "        -0.02430347, -0.02408739, -0.02380137, -0.02279949, -0.02360641,\n",
       "        -0.02177818, -0.02209743, -0.02205012, -0.0220215 , -0.02188535,\n",
       "        -0.04061565, -0.03860345, -0.03980024, -0.03916386, -0.03915721,\n",
       "        -0.02348953, -0.02453946, -0.02378308, -0.02376887, -0.02274609,\n",
       "        -0.02193591, -0.02258731, -0.02222074, -0.02304489, -0.02332853,\n",
       "        -0.03890545, -0.04033095, -0.04039545, -0.03996693, -0.03995682,\n",
       "        -0.02339424, -0.02330544, -0.02338916, -0.0230807 , -0.02271689,\n",
       "        -0.02185463, -0.02197321, -0.02333105, -0.02190722, -0.02227419,\n",
       "        -0.03996901, -0.04086616, -0.03959518, -0.04127085, -0.04026212,\n",
       "        -0.0246384 , -0.02364719, -0.02372385, -0.02336993, -0.02336862,\n",
       "        -0.02302358, -0.02254184, -0.02179572, -0.02313013, -0.0222528 ]),\n",
       " 'mean_test_score': array([-0.04072179, -0.04082328, -0.04053426, -0.04105495, -0.04130339,\n",
       "        -0.02255225, -0.0220335 , -0.02239177, -0.02214585, -0.02165145,\n",
       "        -0.0213847 , -0.02123347, -0.02141642, -0.02114962, -0.02108772,\n",
       "        -0.04055512, -0.04064544, -0.04090196, -0.04065752, -0.04073181,\n",
       "        -0.02227217, -0.02259806, -0.02244699, -0.02237921, -0.02243914,\n",
       "        -0.02139494, -0.0213858 , -0.02117013, -0.02127029, -0.02142275,\n",
       "        -0.04135632, -0.0406183 , -0.04152557, -0.0406587 , -0.04116614,\n",
       "        -0.02289854, -0.02244257, -0.02237049, -0.02210604, -0.02217711,\n",
       "        -0.02096769, -0.02092945, -0.02109531, -0.0208704 , -0.02048156,\n",
       "        -0.04117825, -0.0408191 , -0.04074113, -0.04051377, -0.04032831,\n",
       "        -0.02233196, -0.0224088 , -0.02223774, -0.02252136, -0.02214805,\n",
       "        -0.02069921, -0.02085126, -0.02044538, -0.02087714, -0.02085647,\n",
       "        -0.04046108, -0.04115694, -0.04082807, -0.04097391, -0.04089888,\n",
       "        -0.02245627, -0.02190182, -0.02232311, -0.02220752, -0.02213793,\n",
       "        -0.02074477, -0.02084033, -0.02095299, -0.02087543, -0.02055688,\n",
       "        -0.04070176, -0.04072096, -0.04085471, -0.04111725, -0.04079078,\n",
       "        -0.0228437 , -0.02242091, -0.02188105, -0.02205268, -0.02202954,\n",
       "        -0.02139244, -0.0208405 , -0.02067538, -0.02100646, -0.02090419]),\n",
       " 'std_test_score': array([0.00304182, 0.00348361, 0.00337164, 0.00365451, 0.00369661,\n",
       "        0.00306035, 0.00231194, 0.00251446, 0.00229801, 0.00239815,\n",
       "        0.00240315, 0.00266503, 0.00268129, 0.0026255 , 0.00232458,\n",
       "        0.00334735, 0.00326902, 0.00350345, 0.0029025 , 0.0033309 ,\n",
       "        0.00234372, 0.00271808, 0.00236457, 0.00246037, 0.00250305,\n",
       "        0.0024519 , 0.00252208, 0.00265691, 0.00236959, 0.00202877,\n",
       "        0.00375499, 0.00291062, 0.00366707, 0.00379775, 0.00393398,\n",
       "        0.00263713, 0.00238056, 0.00288691, 0.00255249, 0.00255417,\n",
       "        0.00220416, 0.00299129, 0.00263633, 0.00260616, 0.00236899,\n",
       "        0.00355856, 0.00292428, 0.00257957, 0.00242665, 0.00317183,\n",
       "        0.00259754, 0.00233882, 0.00247773, 0.00225334, 0.00222488,\n",
       "        0.00235974, 0.00223906, 0.00207243, 0.00259189, 0.00261631,\n",
       "        0.00335718, 0.00373636, 0.00335193, 0.00378255, 0.0027325 ,\n",
       "        0.00251383, 0.00276683, 0.00270881, 0.00283961, 0.00227767,\n",
       "        0.00240491, 0.00234833, 0.00225028, 0.00247943, 0.00230766,\n",
       "        0.00317617, 0.00302513, 0.00309389, 0.00314848, 0.00281501,\n",
       "        0.00267297, 0.00233745, 0.00271131, 0.00235468, 0.00248661,\n",
       "        0.00250432, 0.00256935, 0.00264665, 0.00260247, 0.00244873]),\n",
       " 'rank_test_score': array([72, 77, 64, 83, 88, 57, 35, 49, 39, 31, 25, 23, 29, 21, 19, 65, 67,\n",
       "        81, 68, 73, 44, 58, 54, 48, 52, 28, 26, 22, 24, 30, 89, 66, 90, 69,\n",
       "        86, 60, 53, 47, 37, 41, 17, 15, 20, 11,  2, 87, 76, 74, 63, 61, 46,\n",
       "        50, 43, 56, 40,  5,  9,  1, 13, 10, 62, 85, 78, 82, 80, 55, 33, 45,\n",
       "        42, 38,  6,  7, 16, 12,  3, 70, 71, 79, 84, 75, 59, 51, 32, 36, 34,\n",
       "        27,  8,  4, 18, 14]),\n",
       " 'split0_train_score': array([-0.0379948 , -0.03774624, -0.03830565, -0.03806274, -0.03816249,\n",
       "        -0.00850095, -0.00857001, -0.00876426, -0.00913172, -0.00872986,\n",
       "        -0.00548199, -0.00534675, -0.0054355 , -0.00551358, -0.00501843,\n",
       "        -0.03765096, -0.03781627, -0.03806578, -0.03856375, -0.03864684,\n",
       "        -0.00898804, -0.00869977, -0.00845948, -0.00852986, -0.00850064,\n",
       "        -0.00538266, -0.00509056, -0.00528732, -0.0052546 , -0.00504802,\n",
       "        -0.03833947, -0.03943447, -0.03871037, -0.03826561, -0.03769485,\n",
       "        -0.00846238, -0.00873673, -0.00843323, -0.00862204, -0.00859231,\n",
       "        -0.00388766, -0.00368254, -0.00368275, -0.00374899, -0.00361246,\n",
       "        -0.0379418 , -0.03790377, -0.03810662, -0.0388659 , -0.03790002,\n",
       "        -0.00856971, -0.00851847, -0.0083809 , -0.00877233, -0.00827839,\n",
       "        -0.00386003, -0.00386136, -0.00406909, -0.00368227, -0.00381707,\n",
       "        -0.03718995, -0.03734353, -0.03797216, -0.03776411, -0.03846957,\n",
       "        -0.00859826, -0.00878645, -0.00879334, -0.00881705, -0.00867067,\n",
       "        -0.00416971, -0.00387174, -0.00382716, -0.00399171, -0.00387983,\n",
       "        -0.03890712, -0.03780571, -0.03853368, -0.0383224 , -0.03874041,\n",
       "        -0.00907628, -0.00860244, -0.0085178 , -0.00862002, -0.00834021,\n",
       "        -0.00356092, -0.00391538, -0.00371288, -0.00365855, -0.00378538]),\n",
       " 'split1_train_score': array([-0.03439779, -0.0357882 , -0.03546134, -0.03583607, -0.03582152,\n",
       "        -0.00861417, -0.00846094, -0.00792564, -0.00792694, -0.00795896,\n",
       "        -0.00508995, -0.00456951, -0.00465434, -0.00496236, -0.00496798,\n",
       "        -0.03565542, -0.03562989, -0.03669206, -0.03458536, -0.0363898 ,\n",
       "        -0.00793169, -0.00761074, -0.00807155, -0.00818445, -0.00799316,\n",
       "        -0.00479799, -0.00459668, -0.00461122, -0.00471652, -0.00463328,\n",
       "        -0.03636268, -0.03541067, -0.03549082, -0.03653189, -0.03595321,\n",
       "        -0.00857818, -0.0080757 , -0.00836463, -0.00805238, -0.00801854,\n",
       "        -0.00357776, -0.00362761, -0.00334968, -0.00373995, -0.0030972 ,\n",
       "        -0.03612883, -0.03551444, -0.03449542, -0.03488231, -0.03547127,\n",
       "        -0.00808143, -0.00805776, -0.00794892, -0.00795052, -0.00782398,\n",
       "        -0.00346106, -0.00327298, -0.00329095, -0.0036074 , -0.00337384,\n",
       "        -0.03537528, -0.03616131, -0.03588082, -0.03587862, -0.03477663,\n",
       "        -0.00788403, -0.00824809, -0.00792861, -0.00832765, -0.00830001,\n",
       "        -0.00342813, -0.00368233, -0.00351643, -0.0034372 , -0.00337007,\n",
       "        -0.03540241, -0.03541945, -0.03537793, -0.0350761 , -0.03518647,\n",
       "        -0.00841824, -0.00784924, -0.00815502, -0.00812125, -0.00764922,\n",
       "        -0.00350395, -0.00366424, -0.00352697, -0.00351376, -0.00334642]),\n",
       " 'split2_train_score': array([-0.03665227, -0.03559141, -0.03609414, -0.03591942, -0.03628329,\n",
       "        -0.00884374, -0.00854777, -0.00850931, -0.00855694, -0.00840206,\n",
       "        -0.00554118, -0.0049234 , -0.0047763 , -0.0049753 , -0.00496335,\n",
       "        -0.03582944, -0.03533129, -0.03562604, -0.03522297, -0.03600199,\n",
       "        -0.00851025, -0.00817099, -0.00843295, -0.00833076, -0.00817961,\n",
       "        -0.00561467, -0.00473391, -0.00512168, -0.00466181, -0.00480498,\n",
       "        -0.03555212, -0.03580905, -0.0362848 , -0.03550597, -0.03677562,\n",
       "        -0.00860721, -0.00881531, -0.00857731, -0.00845664, -0.00846302,\n",
       "        -0.00395676, -0.00353092, -0.00362714, -0.00351071, -0.00352685,\n",
       "        -0.03603233, -0.03635277, -0.03544023, -0.03546882, -0.03519472,\n",
       "        -0.00839868, -0.00815663, -0.00845366, -0.00838695, -0.00847966,\n",
       "        -0.00395262, -0.00363928, -0.00351296, -0.00356143, -0.00387762,\n",
       "        -0.03617843, -0.03648256, -0.03627842, -0.03646243, -0.03581525,\n",
       "        -0.00813204, -0.00874582, -0.00874509, -0.00877162, -0.00856245,\n",
       "        -0.00394281, -0.00391558, -0.00368553, -0.00340446, -0.00339581,\n",
       "        -0.03652774, -0.03527271, -0.03507254, -0.03593356, -0.03540959,\n",
       "        -0.00807537, -0.00848887, -0.00833756, -0.00857881, -0.00847603,\n",
       "        -0.00386577, -0.00376971, -0.00372846, -0.0037161 , -0.00369501]),\n",
       " 'split3_train_score': array([-0.03684153, -0.03697789, -0.03628103, -0.03625633, -0.03626539,\n",
       "        -0.00929347, -0.00916822, -0.00932083, -0.00874691, -0.00868471,\n",
       "        -0.00551721, -0.00544475, -0.0054522 , -0.00526897, -0.00524495,\n",
       "        -0.03595866, -0.0363474 , -0.03626876, -0.03640218, -0.03594088,\n",
       "        -0.00906322, -0.00878975, -0.00872291, -0.00883585, -0.00864467,\n",
       "        -0.00539176, -0.00518705, -0.00534058, -0.00505573, -0.00504171,\n",
       "        -0.03635288, -0.0361948 , -0.03699786, -0.03588307, -0.03703169,\n",
       "        -0.00921005, -0.00910786, -0.00892864, -0.00896181, -0.00884603,\n",
       "        -0.00392231, -0.00393041, -0.00393656, -0.00384753, -0.00378911,\n",
       "        -0.0365651 , -0.03653427, -0.03644404, -0.03621833, -0.03615155,\n",
       "        -0.00874974, -0.00860764, -0.00867764, -0.008853  , -0.00890803,\n",
       "        -0.00355804, -0.00389218, -0.00366746, -0.00374327, -0.00371032,\n",
       "        -0.03614521, -0.03639636, -0.03665311, -0.03645985, -0.03658192,\n",
       "        -0.00887596, -0.00936611, -0.00900688, -0.0089963 , -0.0089083 ,\n",
       "        -0.00397125, -0.00395952, -0.00382707, -0.00399506, -0.00366172,\n",
       "        -0.03591423, -0.0359013 , -0.03678953, -0.03693117, -0.036137  ,\n",
       "        -0.00909599, -0.00892421, -0.00890367, -0.00888755, -0.00872525,\n",
       "        -0.00393439, -0.00376094, -0.00374395, -0.00406713, -0.00378292]),\n",
       " 'split4_train_score': array([-0.03591653, -0.03678629, -0.03578959, -0.03580034, -0.03627624,\n",
       "        -0.00842645, -0.00838952, -0.00849844, -0.00828579, -0.00822697,\n",
       "        -0.00498871, -0.00526955, -0.00505455, -0.00502676, -0.00521816,\n",
       "        -0.03540162, -0.03525357, -0.03479673, -0.03601847, -0.03424271,\n",
       "        -0.00860102, -0.00816293, -0.00821929, -0.00821319, -0.00810088,\n",
       "        -0.00505588, -0.00514792, -0.00510999, -0.005125  , -0.00486855,\n",
       "        -0.03631255, -0.03562998, -0.03629346, -0.03500623, -0.0361441 ,\n",
       "        -0.00837105, -0.00853659, -0.00855588, -0.00819638, -0.00850588,\n",
       "        -0.00363382, -0.0035261 , -0.00340468, -0.00363903, -0.00354514,\n",
       "        -0.03609844, -0.03515753, -0.03584677, -0.03587421, -0.03563545,\n",
       "        -0.00854455, -0.00846517, -0.0082809 , -0.00828091, -0.00808882,\n",
       "        -0.00343064, -0.00341842, -0.00349164, -0.00368627, -0.00336837,\n",
       "        -0.03522344, -0.03676983, -0.03684885, -0.03650661, -0.03626558,\n",
       "        -0.00865318, -0.00872116, -0.00828256, -0.00863264, -0.00835027,\n",
       "        -0.00360216, -0.00351542, -0.00374781, -0.00370606, -0.003375  ,\n",
       "        -0.03622472, -0.03599075, -0.03514864, -0.03623724, -0.03575351,\n",
       "        -0.00865782, -0.00833362, -0.0081162 , -0.00815451, -0.00809742,\n",
       "        -0.00353093, -0.00365457, -0.00326294, -0.00363948, -0.00352203]),\n",
       " 'mean_train_score': array([-0.03636058, -0.03657801, -0.03638635, -0.03637498, -0.03656178,\n",
       "        -0.00873576, -0.00862729, -0.00860369, -0.00852966, -0.00840051,\n",
       "        -0.00532381, -0.00511079, -0.00507458, -0.0051494 , -0.00508258,\n",
       "        -0.03609922, -0.03607568, -0.03628987, -0.03615855, -0.03624445,\n",
       "        -0.00861884, -0.00828684, -0.00838123, -0.00841882, -0.00828379,\n",
       "        -0.00524859, -0.00495122, -0.00509416, -0.00496273, -0.00487931,\n",
       "        -0.03658394, -0.03649579, -0.03675546, -0.03623855, -0.03671989,\n",
       "        -0.00864578, -0.00865444, -0.00857194, -0.00845785, -0.00848516,\n",
       "        -0.00379566, -0.00365952, -0.00360016, -0.00369724, -0.00351415,\n",
       "        -0.0365533 , -0.03629256, -0.03606661, -0.03626191, -0.0360706 ,\n",
       "        -0.00846882, -0.00836114, -0.0083484 , -0.00844874, -0.00831577,\n",
       "        -0.00365248, -0.00361685, -0.00360642, -0.00365613, -0.00362944,\n",
       "        -0.03602246, -0.03663072, -0.03672667, -0.03661433, -0.03638179,\n",
       "        -0.00842869, -0.00877353, -0.00855129, -0.00870905, -0.00855834,\n",
       "        -0.00382281, -0.00378892, -0.0037208 , -0.0037069 , -0.00353648,\n",
       "        -0.03659524, -0.03607798, -0.03618446, -0.03650009, -0.03624539,\n",
       "        -0.00866474, -0.00843968, -0.00840605, -0.00847243, -0.00825762,\n",
       "        -0.00367919, -0.00375297, -0.00359504, -0.003719  , -0.00362635]),\n",
       " 'std_train_score': array([1.18632146e-03, 7.95642538e-04, 9.99193198e-04, 8.59157701e-04,\n",
       "        8.19413071e-04, 3.12501801e-04, 2.77999465e-04, 4.51620139e-04,\n",
       "        4.08205456e-04, 2.87863768e-04, 2.35225313e-04, 3.22647736e-04,\n",
       "        3.28276276e-04, 2.13193552e-04, 1.23459738e-04, 7.98000833e-04,\n",
       "        9.52072290e-04, 1.09418233e-03, 1.35751260e-03, 1.41070900e-03,\n",
       "        4.04492452e-04, 4.26454094e-04, 2.22560881e-04, 2.41332905e-04,\n",
       "        2.47399893e-04, 2.87286465e-04, 2.39432383e-04, 2.57788317e-04,\n",
       "        2.32956388e-04, 1.55569980e-04, 9.29787669e-04, 1.49165302e-03,\n",
       "        1.08762892e-03, 1.12930597e-03, 6.27726252e-04, 2.94472783e-04,\n",
       "        3.42655869e-04, 1.94805046e-04, 3.20532975e-04, 2.68463316e-04,\n",
       "        1.57562027e-04, 1.47831018e-04, 2.10554498e-04, 1.14255005e-04,\n",
       "        2.28137254e-04, 7.19243849e-04, 9.54129779e-04, 1.20111818e-03,\n",
       "        1.37588518e-03, 9.66336174e-04, 2.23528330e-04, 2.14568887e-04,\n",
       "        2.38608913e-04, 3.31161624e-04, 3.66714404e-04, 2.13508611e-04,\n",
       "        2.42368473e-04, 2.60470155e-04, 6.40756716e-05, 2.17636640e-04,\n",
       "        7.01385550e-04, 4.06121603e-04, 7.05403745e-04, 6.19967121e-04,\n",
       "        1.20912317e-03, 3.64378467e-04, 3.55222396e-04, 3.90806716e-04,\n",
       "        2.23336164e-04, 2.21418862e-04, 2.68786705e-04, 1.66235222e-04,\n",
       "        1.15191408e-04, 2.56266759e-04, 2.03534933e-04, 1.21435041e-03,\n",
       "        9.06199972e-04, 1.33001541e-03, 1.08852504e-03, 1.28830795e-03,\n",
       "        3.90769729e-04, 3.53141615e-04, 2.86996203e-04, 2.93198048e-04,\n",
       "        3.65793102e-04, 1.82548237e-04, 9.41035353e-05, 1.83731699e-04,\n",
       "        1.86176055e-04, 1.69565171e-04])}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print CV results\n",
    "rf_model.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "d = {'featuresName': X.columns, 'featureImportances': rf_model.best_estimator_.feature_importances_}\n",
    "temp = pd.DataFrame(data=d)\n",
    "temp = temp.sort_values(by=['featureImportances'],ascending=False)\n",
    "\n",
    "\n",
    "newtemp = temp[0:20]\n",
    "\n",
    "\n",
    "sns.barplot(y=newtemp['featuresName'],x=newtemp['featureImportances'])\n",
    "\n",
    "plt.title('Feature Important in Random Forest (first 20 features)')\n",
    "plt.xlabel('Important')\n",
    "plt.ylabel('Feature');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Predict Testing Data and Generate csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make prediction on testing data\n",
    "y_pred = rf_model.predict(test_processed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print predicted sale price distribution\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.hist(y_pred, bins=50)\n",
    "plt.xlabel('Predicted Sale Price')\n",
    "plt.ylabel('Freq')\n",
    "plt.title('Predicted Sale Price Distribution')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# final submission\n",
    "sub = pd.DataFrame()\n",
    "sub['Id'] = test['Id']\n",
    "sub['SalePrice'] = y_pred\n",
    "sub.to_csv('RF-new.csv',index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}