4 - ElasticNet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Ames Housing Dataset - ElasticNet\n",
    "\n",
    "> Gianmaria Pizzo - 872966@stud.unive.it\n",
    "\n",
    "These notebooks represent the project submission for the course [Data and Web Mining](https://www.unive.it/data/course/337525) by Professor [Claudio Lucchese](https://www.unive.it/data/people/5590426) at [Ca' Foscari University of Venice](https://www.unive.it).\n",
    "\n",
    "---\n",
    "\n",
    "## Structure of this notebook\n",
    "\n",
    "This notebook covers the following points\n",
    "* The idea\n",
    "* Tuning:\n",
    "    * Automatic: GridSearchCV Hyperparameters tuning for ElasticNet.\n",
    "    * Manual\n",
    "* Model validation\n",
    "* Results\n",
    "* Analysis of worst and best predictions.\n",
    "\n",
    "---\n",
    "\n",
    "### Before running this notebook\n",
    "\n",
    "To avoid issues, before running the following notebook it is best to\n",
    "* Clean previous cell outputs\n",
    "* Restart the kernel\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### Environment, Globals and Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mlxtend in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (0.22.0)\n",
      "Requirement already satisfied: matplotlib>=3.0.0 in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (from mlxtend) (3.5.0)\n",
      "Requirement already satisfied: numpy>=1.16.2 in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (from mlxtend) (1.20.3)\n",
      "Requirement already satisfied: setuptools in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (from mlxtend) (65.6.3)\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (from mlxtend) (1.2.1)\n",
      "Requirement already satisfied: pandas>=0.24.2 in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (from mlxtend) (1.3.5)\n",
      "Requirement already satisfied: joblib>=0.13.2 in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (from mlxtend) (1.1.1)\n",
      "Requirement already satisfied: scipy>=1.2.1 in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (from mlxtend) (1.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (1.4.4)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (3.0.9)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (4.25.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (0.11.0)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (22.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (2.8.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (8.4.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (from pandas>=0.24.2->mlxtend) (2022.7)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib>=3.0.0->mlxtend) (1.16.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (from scikit-learn>=1.0.2->mlxtend) (2.2.0)\n",
      "Requirement already satisfied: xgboost in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (1.6.0)\n",
      "Requirement already satisfied: numpy in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (from xgboost) (1.20.3)\n",
      "Requirement already satisfied: scipy in c:\\users\\paythepizzo\\anaconda3\\lib\\site-packages (from xgboost) (1.10.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install mlxtend\n",
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Interactive\n",
    "%matplotlib notebook\n",
    "# Static\n",
    "# %matplotlib inline\n",
    "\n",
    "# Environment for this notebook\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy\n",
    "import warnings\n",
    "import sklearn \n",
    "import IPython\n",
    "import xgboost\n",
    "from scipy import stats\n",
    "from sklearn.model_selection import RepeatedKFold\n",
    "from sklearn.linear_model import ElasticNet\n",
    "\n",
    "# Set the style for the plots\n",
    "sns.set()\n",
    "plt.style.use('ggplot')\n",
    "sns.set_style(\"darkgrid\")\n",
    "# Ignore warnings\n",
    "warnings.filterwarnings('ignore') \n",
    "\n",
    "# Working folder\n",
    "WORKING_DIR = os.getcwd()\n",
    "# Resources folder\n",
    "RESOURCES_DIR = os.path.join(os.getcwd(), 'resources')\n",
    "# Name of file\n",
    "IN_LABEL = 'ames_housing_out_21.csv'\n",
    "IN_LABEL2 = 'ames_housing_out_22.csv'\n",
    "ORIG_LABEL = 'ames_housing_out_22_orig.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Utils Module\n",
    "\n",
    "def sort_alphabetically(dataset, last_label = None):\n",
    "    \"\"\"\n",
    "    Sorts the dataset alphabetically \n",
    "\n",
    "    :param dataset: a pd.DataFrame\n",
    "    :param last_label: a str containing an existing column label in the dataset\n",
    "    :returns: pd.DataFrame\n",
    "    \"\"\"\n",
    "    # Sort\n",
    "    dataset = dataset.reindex(sorted(dataset.columns), axis=1)\n",
    "    # Move target column to last index\n",
    "    if last_label is not None:\n",
    "        col = dataset.pop(last_label)\n",
    "        dataset.insert(dataset.shape[1], last_label, col)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Module for train test split\n",
    "\n",
    "def get_X_y(dataset, label, ignore=None):\n",
    "    \"\"\"\n",
    "    Returns X and y and ignores labels in ignore\n",
    "    :param dataset: a pd.DataFrame\n",
    "    :param label: a str containing an existing target column label in the dataset\n",
    "    :param ignore: a list of str containing an existing column label in the dataset to ignore\n",
    "    :returns: tuple of pd.DataFrame\n",
    "    \"\"\"\n",
    "    if ignore is not None:\n",
    "        # Drop the labels \n",
    "        all_columns = list(dataset.columns)\n",
    "        # Include only columns that are existing \n",
    "        to_drop = [i for i in all_columns if i in ignore] +[label]\n",
    "        return dataset.drop(columns=to_drop), dataset[[label]]\n",
    "    return dataset.drop(columns=[label]), dataset[[label]]\n",
    "\n",
    "def get_train_test(X, y, size = 0.2, state = 33):\n",
    "    \"\"\"\n",
    "    Returns X_train_[size], X_test, y_train_[size], y_test\n",
    "    :param X: a pd.DataFrame without the target column\n",
    "    :param y: a pd.DataFrame with one column, the target\n",
    "    :param size: a float representing the fraction for the test size\n",
    "    :param state: an integer representing the random state for the test\n",
    "    :returns: 4 pd.DataFrame usually called \"X_train_[size], X_test, y_train_[size], y_test\"\n",
    "    \"\"\"\n",
    "    return train_test_split(X, y, test_size=size, random_state = state)\n",
    "\n",
    "def get_train_val_test(X, y, size_t=0.2, size_v=0.25, state_v = 42):\n",
    "    \"\"\"\n",
    "    Returns X_train, X_valid, X_test, y_train, y_valid, y_test\n",
    "    :param X: a pd.DataFrame without the target column\n",
    "    :param y: a pd.DataFrame with one column, the target\n",
    "    :param size_t: a float representing the fraction for the test size\n",
    "    :param size_v: a float representing the fraction for the validation\n",
    "    :param state_v: an integer representing the random state for the validation\n",
    "    :returns: 6 pd.DataFrame usually called X_train, X_valid, X_test, y_train, y_valid, y_test\n",
    "    \"\"\"\n",
    "    X_train_s, X_test, y_train_s, y_test = get_train_test(X, y, size = size_t)\n",
    "    X_train, X_valid, y_train, y_valid = get_train_test(X_train_s, y_train_s, size = size_v, state = state_v)\n",
    "    return X_train, X_valid, X_test, y_train, y_valid, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import LeaveOneOut, GridSearchCV\n",
    "from sklearn.metrics import mean_squared_error, mean_squared_log_error, mean_absolute_error, r2_score, max_error \n",
    "from mlxtend.evaluate import bias_variance_decomp\n",
    "\n",
    "# Module for traininig and testing\n",
    "def get_regression_metrics(y_test, y_pred):\n",
    "    metrics = {\n",
    "            \"RMSE\": mean_squared_error(y_true=y_test, y_pred=y_pred, squared=False),\n",
    "            \"MSE\": mean_squared_error(y_true=y_test, y_pred=y_pred),\n",
    "            \"MAE\": mean_absolute_error(y_true=y_test, y_pred=y_pred),\n",
    "            \"R2\": r2_score(y_true=y_test, y_pred=y_pred),\n",
    "            \"MAX_Err\": max_error(y_true=y_test, y_pred=y_pred)}\n",
    "    return metrics\n",
    "\n",
    "\n",
    "def get_bias_variance_decomp(dataset, model, label, split_size, ignore, \n",
    "                             num_rounds=50, random_state=230324945):\n",
    "    # Get split\n",
    "    X, y = get_X_y(dataset, label=label, ignore=None)\n",
    "    X_train, X_test, y_train, y_test = get_train_test(X, y, size = split_size, \n",
    "                                                      state = random_state)\n",
    "    # Only accepts np.arrays\n",
    "    mse, bias, var = bias_variance_decomp(estimator=model, \n",
    "                                          X_train=X_train.values, \n",
    "                                          y_train=y_train.values, \n",
    "                                          X_test=X_test.values, \n",
    "                                          y_test=y_test.values, \n",
    "                                          loss='mse', num_rounds=num_rounds, \n",
    "                                          random_seed=random_state)\n",
    "    print('Avg Expected RMSE: %.3f' % np.sqrt(mse))\n",
    "    print('Avg Expected MSE: %.3f' % mse)\n",
    "    print('Avg Bias: %.3f' % bias)\n",
    "    print('Avg Variance: %.3f' % var)\n",
    "    pass\n",
    "\n",
    "\n",
    "def LOO_estimator_eval(dataset, target, estimator, params, ignore=None):\n",
    "    \"\"\"\n",
    "    Function used to evaluate estimators, based on Leave One Out process. It adds a \n",
    "    column 'Predicted' to the given dataset, and returns the metrics used to evaluate the \n",
    "    performances\n",
    "    \n",
    "    :param dataset: a pd.DataFrame with the target column\n",
    "    :param target: a str representing the target\n",
    "    :param estimator: instance of some estimator (i.e. XGBoostRegressor())\n",
    "    :param params: a dictionary containing the parameters for the estimator\n",
    "    :param ignor: a list of strings representing the feature to ignore\n",
    "    :returns: the pd.DataFrame\n",
    "    \"\"\"\n",
    "    # Splitter\n",
    "    splitter = LeaveOneOut()\n",
    "    \n",
    "    # Add predicted\n",
    "    dataset['Predicted'] = 0.0\n",
    "    \n",
    "    # Ignore\n",
    "    if ignore is not None:\n",
    "        ignore = ignore + ['Predicted']\n",
    "    else:\n",
    "        ignore = ['Predicted']\n",
    "    \n",
    "    # Split X, y\n",
    "    X, y = get_X_y(dataset, label=target, ignore=ignore)\n",
    "    \n",
    "    # For each fold and tuple train, test indices\n",
    "    for i, (train_index, test_index) in enumerate(splitter.split(X)):\n",
    "        # Re-Assign\n",
    "        model = estimator\n",
    "        \n",
    "        # Base model initialized with some parameters\n",
    "        if params is not None: \n",
    "            print(params)\n",
    "            model.set_params(params)\n",
    "        \n",
    "        # Get train part\n",
    "        train = dataset.loc[train_index.tolist()]\n",
    "        X_train, y_train = get_X_y(train, label=target, ignore=ignore)\n",
    "       \n",
    "        # Train \n",
    "        model.fit(X_train, y_train)\n",
    "\n",
    "        # Get test part \n",
    "        test = dataset.loc[test_index.tolist()]\n",
    "        X_test, y_test = get_X_y(test, label=target, ignore=ignore)\n",
    "        \n",
    "        # Add predict to dataset\n",
    "        y_pred = model.predict(X_test)\n",
    "        dataset.loc[test_index.tolist()[0]]['Predicted'] = y_pred[0]\n",
    "    return get_regression_metrics(dataset[[target]], dataset[['Predicted']])\n",
    "\n",
    "\n",
    "def GridSearch_CV_Tuning(dataset, target, estimator, params, ignore=None, n_repeats=4, n_splits=4, \n",
    "                random_state=33412):\n",
    "    \"\"\"\n",
    "    Function used to evaluate estimators, based on GridSearchCV process. It evaluates the\n",
    "    performances through a Repeated K Fold, and returns the results\n",
    "    \n",
    "    :param dataset: a pd.DataFrame with the target column\n",
    "    :param target: a str representing the target\n",
    "    :param estimator: instance of some estimator (i.e. XGBoostRegressor())\n",
    "    :param params: a dictionary containing the parameters for the estimator\n",
    "    :param ignore: a list of strings representing the feature to ignore\n",
    "    :param n_repeats: a integer\n",
    "    :param n_splits: a integer\n",
    "    :returns: the pd.DataFrame containing the results\n",
    "    \"\"\"\n",
    "    # Ignore\n",
    "    if ignore is not None:\n",
    "        ignore = ignore + ['Predicted']\n",
    "    else:\n",
    "        ignore = ['Predicted']\n",
    "        \n",
    "    # RepeatedKFold splitter\n",
    "    splitter = RepeatedKFold(n_repeats=n_repeats, n_splits=n_splits, random_state=random_state)\n",
    "    \n",
    "    # GridSearchCV\n",
    "    clf = GridSearchCV(estimator=estimator, cv=splitter,\n",
    "                       param_grid=params, return_train_score = True,\n",
    "                       scoring =['neg_mean_squared_error', 'neg_root_mean_squared_error', 'r2'],\n",
    "                       refit=False, n_jobs=-1, verbose=3)\n",
    "    # X, y\n",
    "    X, y = get_X_y(dataset, label=target, ignore=(ignore + ['Predicted']))\n",
    "    # Train, Test split\n",
    "    X_train, X_test, y_train, y_test = get_train_test(X, y)\n",
    "    # Fit\n",
    "    clf.fit(X_train, y_train)\n",
    "    \n",
    "    return pd.DataFrame(clf.cv_results_)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset Overview\n",
    "\n",
    "The dataset we are going to consider are the following ones:\n",
    "* The modified dataset, in two different subset versions\n",
    "* The original dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(os.path.join(RESOURCES_DIR, IN_LABEL))\n",
    "df2 = pd.read_csv(os.path.join(RESOURCES_DIR, IN_LABEL2))\n",
    "df_orig = pd.read_csv(os.path.join(RESOURCES_DIR, ORIG_LABEL))\n",
    "\n",
    "df.drop(columns=['Unnamed: 0', 'Latitude', 'Longitude'], inplace=True)\n",
    "df_orig.drop(columns=['Unnamed: 0', 'Latitude', 'Longitude'], inplace=True)\n",
    "\n",
    "df = sort_alphabetically(df, 'Sale_Price')\n",
    "df2 = sort_alphabetically(df2, 'Sale_Price')\n",
    "df_orig = sort_alphabetically(df_orig, 'Sale_Price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Hyperparameters Tuning\n",
    "\n",
    "First of all, let us try to use a Grid Search CV to find the best parameters.\n",
    "\n",
    "### Automatic Parameters Tuning: Randomized Grid Search\n",
    "\n",
    "By defining the repetitions, the splits and the parameters, we repeatedly train and test the models. From each one of the model, we obtain three scores which we can use to check the best a parameters.\n",
    "\n",
    "But first we can choose a parsimonious range of hyperparameters to test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "en_params = {\n",
    "    'alpha': [0.0, 0.5, 1.0, 1.5],\n",
    "    'l1_ratio': [0.25, 0.5, 0.75],\n",
    "    'max_iter':[25, 50, 100, 250, 500, 1000],\n",
    "    'positive': [True, False],\n",
    "    'selection':['cyclic', 'random'],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 16 folds for each of 288 candidates, totalling 4608 fits\n"
     ]
    }
   ],
   "source": [
    "results = GridSearch_CV_Tuning(dataset=df, target='Sale_Price', estimator=ElasticNet(), params=en_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this dataframe we want to obtain the 10 best models for each metric we used. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
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       "      <th>...</th>\n",
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       "      <td>0.882676</td>\n",
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       "      <td>0.885693</td>\n",
       "      <td>0.877323</td>\n",
       "      <td>0.877389</td>\n",
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       "      <td>0.005187</td>\n",
       "      <td>0.000808</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>25</td>\n",
       "      <td>True</td>\n",
       "      <td>random</td>\n",
       "      <td>{'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.876230</td>\n",
       "      <td>0.876845</td>\n",
       "      <td>0.882673</td>\n",
       "      <td>0.885651</td>\n",
       "      <td>0.880005</td>\n",
       "      <td>0.885675</td>\n",
       "      <td>0.877317</td>\n",
       "      <td>0.877372</td>\n",
       "      <td>0.880214</td>\n",
       "      <td>0.003318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.006188</td>\n",
       "      <td>0.000390</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>25</td>\n",
       "      <td>False</td>\n",
       "      <td>cyclic</td>\n",
       "      <td>{'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.903127</td>\n",
       "      <td>0.902657</td>\n",
       "      <td>0.910010</td>\n",
       "      <td>0.912123</td>\n",
       "      <td>0.908710</td>\n",
       "      <td>0.908789</td>\n",
       "      <td>0.906356</td>\n",
       "      <td>0.903523</td>\n",
       "      <td>0.906916</td>\n",
       "      <td>0.002558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.006375</td>\n",
       "      <td>0.000696</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>25</td>\n",
       "      <td>False</td>\n",
       "      <td>random</td>\n",
       "      <td>{'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.903187</td>\n",
       "      <td>0.902679</td>\n",
       "      <td>0.909796</td>\n",
       "      <td>0.911840</td>\n",
       "      <td>0.907053</td>\n",
       "      <td>0.908646</td>\n",
       "      <td>0.904644</td>\n",
       "      <td>0.903023</td>\n",
       "      <td>0.906483</td>\n",
       "      <td>0.002491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.006750</td>\n",
       "      <td>0.000829</td>\n",
       "      <td>0.005375</td>\n",
       "      <td>0.000696</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>50</td>\n",
       "      <td>True</td>\n",
       "      <td>cyclic</td>\n",
       "      <td>{'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 5...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.876239</td>\n",
       "      <td>0.876845</td>\n",
       "      <td>0.882676</td>\n",
       "      <td>0.885675</td>\n",
       "      <td>0.880019</td>\n",
       "      <td>0.885693</td>\n",
       "      <td>0.877324</td>\n",
       "      <td>0.877389</td>\n",
       "      <td>0.880235</td>\n",
       "      <td>0.003324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>0.010375</td>\n",
       "      <td>0.001053</td>\n",
       "      <td>0.004812</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>500</td>\n",
       "      <td>False</td>\n",
       "      <td>random</td>\n",
       "      <td>{'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 5...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.881881</td>\n",
       "      <td>0.881578</td>\n",
       "      <td>0.888123</td>\n",
       "      <td>0.890923</td>\n",
       "      <td>0.887047</td>\n",
       "      <td>0.889913</td>\n",
       "      <td>0.883371</td>\n",
       "      <td>0.881590</td>\n",
       "      <td>0.885597</td>\n",
       "      <td>0.003042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>0.005750</td>\n",
       "      <td>0.000559</td>\n",
       "      <td>0.004563</td>\n",
       "      <td>0.000609</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1000</td>\n",
       "      <td>True</td>\n",
       "      <td>cyclic</td>\n",
       "      <td>{'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.865740</td>\n",
       "      <td>0.865620</td>\n",
       "      <td>0.871379</td>\n",
       "      <td>0.875543</td>\n",
       "      <td>0.869820</td>\n",
       "      <td>0.874643</td>\n",
       "      <td>0.866871</td>\n",
       "      <td>0.866244</td>\n",
       "      <td>0.869490</td>\n",
       "      <td>0.003395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>0.006500</td>\n",
       "      <td>0.001173</td>\n",
       "      <td>0.004125</td>\n",
       "      <td>0.000696</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1000</td>\n",
       "      <td>True</td>\n",
       "      <td>random</td>\n",
       "      <td>{'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.865740</td>\n",
       "      <td>0.865620</td>\n",
       "      <td>0.871379</td>\n",
       "      <td>0.875543</td>\n",
       "      <td>0.869820</td>\n",
       "      <td>0.874643</td>\n",
       "      <td>0.866871</td>\n",
       "      <td>0.866244</td>\n",
       "      <td>0.869490</td>\n",
       "      <td>0.003395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>0.006312</td>\n",
       "      <td>0.000768</td>\n",
       "      <td>0.004688</td>\n",
       "      <td>0.000463</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1000</td>\n",
       "      <td>False</td>\n",
       "      <td>cyclic</td>\n",
       "      <td>{'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.881881</td>\n",
       "      <td>0.881578</td>\n",
       "      <td>0.888123</td>\n",
       "      <td>0.890923</td>\n",
       "      <td>0.887047</td>\n",
       "      <td>0.889913</td>\n",
       "      <td>0.883371</td>\n",
       "      <td>0.881590</td>\n",
       "      <td>0.885597</td>\n",
       "      <td>0.003042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>0.009562</td>\n",
       "      <td>0.001619</td>\n",
       "      <td>0.003813</td>\n",
       "      <td>0.000808</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1000</td>\n",
       "      <td>False</td>\n",
       "      <td>random</td>\n",
       "      <td>{'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.881881</td>\n",
       "      <td>0.881578</td>\n",
       "      <td>0.888123</td>\n",
       "      <td>0.890923</td>\n",
       "      <td>0.887047</td>\n",
       "      <td>0.889913</td>\n",
       "      <td>0.883371</td>\n",
       "      <td>0.881590</td>\n",
       "      <td>0.885597</td>\n",
       "      <td>0.003042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>288 rows × 121 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean_fit_time  std_fit_time  mean_score_time  std_score_time param_alpha  \\\n",
       "0         0.010500      0.002693         0.006187        0.000950         0.0   \n",
       "1         0.006938      0.001519         0.005187        0.000808         0.0   \n",
       "2         0.006188      0.000390         0.005000        0.000353         0.0   \n",
       "3         0.006375      0.000696         0.005000        0.000500         0.0   \n",
       "4         0.006750      0.000829         0.005375        0.000696         0.0   \n",
       "..             ...           ...              ...             ...         ...   \n",
       "283       0.010375      0.001053         0.004812        0.000527         1.5   \n",
       "284       0.005750      0.000559         0.004563        0.000609         1.5   \n",
       "285       0.006500      0.001173         0.004125        0.000696         1.5   \n",
       "286       0.006312      0.000768         0.004688        0.000463         1.5   \n",
       "287       0.009562      0.001619         0.003813        0.000808         1.5   \n",
       "\n",
       "    param_l1_ratio param_max_iter param_positive param_selection  \\\n",
       "0             0.25             25           True          cyclic   \n",
       "1             0.25             25           True          random   \n",
       "2             0.25             25          False          cyclic   \n",
       "3             0.25             25          False          random   \n",
       "4             0.25             50           True          cyclic   \n",
       "..             ...            ...            ...             ...   \n",
       "283           0.75            500          False          random   \n",
       "284           0.75           1000           True          cyclic   \n",
       "285           0.75           1000           True          random   \n",
       "286           0.75           1000          False          cyclic   \n",
       "287           0.75           1000          False          random   \n",
       "\n",
       "                                                params  ...  split8_train_r2  \\\n",
       "0    {'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...  ...         0.876239   \n",
       "1    {'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...  ...         0.876230   \n",
       "2    {'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...  ...         0.903127   \n",
       "3    {'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...  ...         0.903187   \n",
       "4    {'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 5...  ...         0.876239   \n",
       "..                                                 ...  ...              ...   \n",
       "283  {'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 5...  ...         0.881881   \n",
       "284  {'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...  ...         0.865740   \n",
       "285  {'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...  ...         0.865740   \n",
       "286  {'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...  ...         0.881881   \n",
       "287  {'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...  ...         0.881881   \n",
       "\n",
       "     split9_train_r2  split10_train_r2  split11_train_r2  split12_train_r2  \\\n",
       "0           0.876845          0.882676          0.885675          0.880019   \n",
       "1           0.876845          0.882673          0.885651          0.880005   \n",
       "2           0.902657          0.910010          0.912123          0.908710   \n",
       "3           0.902679          0.909796          0.911840          0.907053   \n",
       "4           0.876845          0.882676          0.885675          0.880019   \n",
       "..               ...               ...               ...               ...   \n",
       "283         0.881578          0.888123          0.890923          0.887047   \n",
       "284         0.865620          0.871379          0.875543          0.869820   \n",
       "285         0.865620          0.871379          0.875543          0.869820   \n",
       "286         0.881578          0.888123          0.890923          0.887047   \n",
       "287         0.881578          0.888123          0.890923          0.887047   \n",
       "\n",
       "     split13_train_r2  split14_train_r2  split15_train_r2  mean_train_r2  \\\n",
       "0            0.885693          0.877323          0.877389       0.880235   \n",
       "1            0.885675          0.877317          0.877372       0.880214   \n",
       "2            0.908789          0.906356          0.903523       0.906916   \n",
       "3            0.908646          0.904644          0.903023       0.906483   \n",
       "4            0.885693          0.877324          0.877389       0.880235   \n",
       "..                ...               ...               ...            ...   \n",
       "283          0.889913          0.883371          0.881590       0.885597   \n",
       "284          0.874643          0.866871          0.866244       0.869490   \n",
       "285          0.874643          0.866871          0.866244       0.869490   \n",
       "286          0.889913          0.883371          0.881590       0.885597   \n",
       "287          0.889913          0.883371          0.881590       0.885597   \n",
       "\n",
       "     std_train_r2  \n",
       "0        0.003324  \n",
       "1        0.003318  \n",
       "2        0.002558  \n",
       "3        0.002491  \n",
       "4        0.003324  \n",
       "..            ...  \n",
       "283      0.003042  \n",
       "284      0.003395  \n",
       "285      0.003395  \n",
       "286      0.003042  \n",
       "287      0.003042  \n",
       "\n",
       "[288 rows x 121 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "best_r2 = list(results[['rank_test_r2','mean_train_r2', 'mean_test_r2']][results['rank_test_r2']==1].index)\n",
    "best_mse = list(results[['rank_test_neg_mean_squared_error','mean_train_neg_mean_squared_error', 'mean_test_neg_mean_squared_error']][results['rank_test_neg_mean_squared_error']==1].index)\n",
    "best_rmse = list(results[['rank_test_neg_root_mean_squared_error','mean_train_neg_root_mean_squared_error', 'mean_test_neg_root_mean_squared_error']][results['rank_test_neg_root_mean_squared_error']==1].index)\n",
    "\n",
    "best = list(set(best_r2) | set(best_mse) | set(best_rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "best_df = results[['mean_fit_time', 'mean_test_neg_mean_squared_error', 'mean_test_neg_root_mean_squared_error', 'mean_test_r2', 'params',]].loc[best].sort_values(by=['mean_fit_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_test_neg_mean_squared_error</th>\n",
       "      <th>mean_test_neg_root_mean_squared_error</th>\n",
       "      <th>mean_test_r2</th>\n",
       "      <th>params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.007187</td>\n",
       "      <td>-5.523102e+08</td>\n",
       "      <td>-23468.658913</td>\n",
       "      <td>0.900996</td>\n",
       "      <td>{'alpha': 0.0, 'l1_ratio': 0.5, 'max_iter': 50...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  mean_test_neg_mean_squared_error  \\\n",
       "31       0.007187                     -5.523102e+08   \n",
       "\n",
       "    mean_test_neg_root_mean_squared_error  mean_test_r2  \\\n",
       "31                          -23468.658913      0.900996   \n",
       "\n",
       "                                               params  \n",
       "31  {'alpha': 0.0, 'l1_ratio': 0.5, 'max_iter': 50...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>alpha</th>\n",
       "      <th>l1_ratio</th>\n",
       "      <th>max_iter</th>\n",
       "      <th>positive</th>\n",
       "      <th>selection</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>50</td>\n",
       "      <td>False</td>\n",
       "      <td>random</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alpha  l1_ratio  max_iter  positive selection\n",
       "0    0.0       0.5        50     False    random"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(list(best_df.params))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just to make sure this is the right way I want to re iter this on the original dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 16 folds for each of 288 candidates, totalling 4608 fits\n"
     ]
    }
   ],
   "source": [
    "results_orig = GridSearch_CV_Tuning(dataset=df_orig, target='Sale_Price', estimator=ElasticNet(), params=en_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_alpha</th>\n",
       "      <th>param_l1_ratio</th>\n",
       "      <th>param_max_iter</th>\n",
       "      <th>param_positive</th>\n",
       "      <th>param_selection</th>\n",
       "      <th>params</th>\n",
       "      <th>...</th>\n",
       "      <th>split8_train_r2</th>\n",
       "      <th>split9_train_r2</th>\n",
       "      <th>split10_train_r2</th>\n",
       "      <th>split11_train_r2</th>\n",
       "      <th>split12_train_r2</th>\n",
       "      <th>split13_train_r2</th>\n",
       "      <th>split14_train_r2</th>\n",
       "      <th>split15_train_r2</th>\n",
       "      <th>mean_train_r2</th>\n",
       "      <th>std_train_r2</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.004936</td>\n",
       "      <td>0.000658</td>\n",
       "      <td>0.004250</td>\n",
       "      <td>0.000559</td>\n",
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       "      <td>25</td>\n",
       "      <td>True</td>\n",
       "      <td>cyclic</td>\n",
       "      <td>{'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...</td>\n",
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       "      <td>0.827896</td>\n",
       "      <td>0.844351</td>\n",
       "      <td>0.843103</td>\n",
       "      <td>0.849535</td>\n",
       "      <td>0.831813</td>\n",
       "      <td>0.830523</td>\n",
       "      <td>0.855216</td>\n",
       "      <td>0.846161</td>\n",
       "      <td>0.841077</td>\n",
       "      <td>0.011032</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.004938</td>\n",
       "      <td>0.000747</td>\n",
       "      <td>0.003875</td>\n",
       "      <td>0.000697</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>25</td>\n",
       "      <td>True</td>\n",
       "      <td>random</td>\n",
       "      <td>{'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.827897</td>\n",
       "      <td>0.844346</td>\n",
       "      <td>0.839901</td>\n",
       "      <td>0.849550</td>\n",
       "      <td>0.831812</td>\n",
       "      <td>0.830540</td>\n",
       "      <td>0.855222</td>\n",
       "      <td>0.846167</td>\n",
       "      <td>0.840784</td>\n",
       "      <td>0.011156</td>\n",
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       "      <td>0.005438</td>\n",
       "      <td>0.000609</td>\n",
       "      <td>0.004375</td>\n",
       "      <td>0.000484</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>25</td>\n",
       "      <td>False</td>\n",
       "      <td>cyclic</td>\n",
       "      <td>{'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.848379</td>\n",
       "      <td>0.864293</td>\n",
       "      <td>0.863846</td>\n",
       "      <td>0.869652</td>\n",
       "      <td>0.852786</td>\n",
       "      <td>0.852328</td>\n",
       "      <td>0.875099</td>\n",
       "      <td>0.865511</td>\n",
       "      <td>0.861468</td>\n",
       "      <td>0.009809</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.005749</td>\n",
       "      <td>0.000901</td>\n",
       "      <td>0.004250</td>\n",
       "      <td>0.000829</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>25</td>\n",
       "      <td>False</td>\n",
       "      <td>random</td>\n",
       "      <td>{'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.846699</td>\n",
       "      <td>0.863824</td>\n",
       "      <td>0.861176</td>\n",
       "      <td>0.868976</td>\n",
       "      <td>0.850579</td>\n",
       "      <td>0.852062</td>\n",
       "      <td>0.873262</td>\n",
       "      <td>0.864856</td>\n",
       "      <td>0.859639</td>\n",
       "      <td>0.010174</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.006312</td>\n",
       "      <td>0.001648</td>\n",
       "      <td>0.004688</td>\n",
       "      <td>0.000583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>50</td>\n",
       "      <td>True</td>\n",
       "      <td>cyclic</td>\n",
       "      <td>{'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 5...</td>\n",
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       "      <td>0.011031</td>\n",
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       "      <td>0.024249</td>\n",
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       "      <td>1.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>500</td>\n",
       "      <td>False</td>\n",
       "      <td>random</td>\n",
       "      <td>{'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 5...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.821309</td>\n",
       "      <td>0.838780</td>\n",
       "      <td>0.836617</td>\n",
       "      <td>0.847087</td>\n",
       "      <td>0.826041</td>\n",
       "      <td>0.824756</td>\n",
       "      <td>0.851406</td>\n",
       "      <td>0.840635</td>\n",
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       "      <th>284</th>\n",
       "      <td>0.005188</td>\n",
       "      <td>0.000807</td>\n",
       "      <td>0.004687</td>\n",
       "      <td>0.000463</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1000</td>\n",
       "      <td>True</td>\n",
       "      <td>cyclic</td>\n",
       "      <td>{'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...</td>\n",
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       "      <td>0.841070</td>\n",
       "      <td>0.834200</td>\n",
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       "      <td>0.000609</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1000</td>\n",
       "      <td>True</td>\n",
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       "      <td>0.831372</td>\n",
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       "      <td>0.818642</td>\n",
       "      <td>0.841072</td>\n",
       "      <td>0.834202</td>\n",
       "      <td>0.828455</td>\n",
       "      <td>0.011447</td>\n",
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       "      <th>286</th>\n",
       "      <td>0.017437</td>\n",
       "      <td>0.001540</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1000</td>\n",
       "      <td>False</td>\n",
       "      <td>cyclic</td>\n",
       "      <td>{'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.821309</td>\n",
       "      <td>0.838780</td>\n",
       "      <td>0.836617</td>\n",
       "      <td>0.847087</td>\n",
       "      <td>0.826041</td>\n",
       "      <td>0.824756</td>\n",
       "      <td>0.851406</td>\n",
       "      <td>0.840635</td>\n",
       "      <td>0.835813</td>\n",
       "      <td>0.012011</td>\n",
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       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>0.028874</td>\n",
       "      <td>0.004648</td>\n",
       "      <td>0.004125</td>\n",
       "      <td>0.000992</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1000</td>\n",
       "      <td>False</td>\n",
       "      <td>random</td>\n",
       "      <td>{'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.821309</td>\n",
       "      <td>0.838780</td>\n",
       "      <td>0.836617</td>\n",
       "      <td>0.847087</td>\n",
       "      <td>0.826041</td>\n",
       "      <td>0.824756</td>\n",
       "      <td>0.851406</td>\n",
       "      <td>0.840635</td>\n",
       "      <td>0.835813</td>\n",
       "      <td>0.012011</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>288 rows × 121 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean_fit_time  std_fit_time  mean_score_time  std_score_time param_alpha  \\\n",
       "0         0.004936      0.000658         0.004250        0.000559         0.0   \n",
       "1         0.004938      0.000747         0.003875        0.000697         0.0   \n",
       "2         0.005438      0.000609         0.004375        0.000484         0.0   \n",
       "3         0.005749      0.000901         0.004250        0.000829         0.0   \n",
       "4         0.006312      0.001648         0.004688        0.000583         0.0   \n",
       "..             ...           ...              ...             ...         ...   \n",
       "283       0.024249      0.005437         0.004001        0.001118         1.5   \n",
       "284       0.005188      0.000807         0.004687        0.000463         1.5   \n",
       "285       0.006125      0.000781         0.004562        0.000609         1.5   \n",
       "286       0.017437      0.001540         0.005000        0.000353         1.5   \n",
       "287       0.028874      0.004648         0.004125        0.000992         1.5   \n",
       "\n",
       "    param_l1_ratio param_max_iter param_positive param_selection  \\\n",
       "0             0.25             25           True          cyclic   \n",
       "1             0.25             25           True          random   \n",
       "2             0.25             25          False          cyclic   \n",
       "3             0.25             25          False          random   \n",
       "4             0.25             50           True          cyclic   \n",
       "..             ...            ...            ...             ...   \n",
       "283           0.75            500          False          random   \n",
       "284           0.75           1000           True          cyclic   \n",
       "285           0.75           1000           True          random   \n",
       "286           0.75           1000          False          cyclic   \n",
       "287           0.75           1000          False          random   \n",
       "\n",
       "                                                params  ...  split8_train_r2  \\\n",
       "0    {'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...  ...         0.827896   \n",
       "1    {'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...  ...         0.827897   \n",
       "2    {'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...  ...         0.848379   \n",
       "3    {'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...  ...         0.846699   \n",
       "4    {'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 5...  ...         0.827909   \n",
       "..                                                 ...  ...              ...   \n",
       "283  {'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 5...  ...         0.821309   \n",
       "284  {'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...  ...         0.814849   \n",
       "285  {'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...  ...         0.814849   \n",
       "286  {'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...  ...         0.821309   \n",
       "287  {'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...  ...         0.821309   \n",
       "\n",
       "     split9_train_r2  split10_train_r2  split11_train_r2  split12_train_r2  \\\n",
       "0           0.844351          0.843103          0.849535          0.831813   \n",
       "1           0.844346          0.839901          0.849550          0.831812   \n",
       "2           0.864293          0.863846          0.869652          0.852786   \n",
       "3           0.863824          0.861176          0.868976          0.850579   \n",
       "4           0.844357          0.843110          0.849550          0.831824   \n",
       "..               ...               ...               ...               ...   \n",
       "283         0.838780          0.836617          0.847087          0.826041   \n",
       "284         0.831369          0.830317          0.837860          0.819186   \n",
       "285         0.831372          0.830319          0.837860          0.819186   \n",
       "286         0.838780          0.836617          0.847087          0.826041   \n",
       "287         0.838780          0.836617          0.847087          0.826041   \n",
       "\n",
       "     split13_train_r2  split14_train_r2  split15_train_r2  mean_train_r2  \\\n",
       "0            0.830523          0.855216          0.846161       0.841077   \n",
       "1            0.830540          0.855222          0.846167       0.840784   \n",
       "2            0.852328          0.875099          0.865511       0.861468   \n",
       "3            0.852062          0.873262          0.864856       0.859639   \n",
       "4            0.830541          0.855222          0.846170       0.841088   \n",
       "..                ...               ...               ...            ...   \n",
       "283          0.824756          0.851406          0.840635       0.835813   \n",
       "284          0.818640          0.841070          0.834200       0.828453   \n",
       "285          0.818642          0.841072          0.834202       0.828455   \n",
       "286          0.824756          0.851406          0.840635       0.835813   \n",
       "287          0.824756          0.851406          0.840635       0.835813   \n",
       "\n",
       "     std_train_r2  \n",
       "0        0.011032  \n",
       "1        0.011156  \n",
       "2        0.009809  \n",
       "3        0.010174  \n",
       "4        0.011031  \n",
       "..            ...  \n",
       "283      0.012011  \n",
       "284      0.011447  \n",
       "285      0.011447  \n",
       "286      0.012011  \n",
       "287      0.012011  \n",
       "\n",
       "[288 rows x 121 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_orig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_r22 = list(results_orig[['rank_test_r2','mean_train_r2', 'mean_test_r2']][results_orig['rank_test_r2']==1].index)\n",
    "best_mse2 = list(results_orig[['rank_test_neg_mean_squared_error','mean_train_neg_mean_squared_error', 'mean_test_neg_mean_squared_error']][results_orig['rank_test_neg_mean_squared_error']==1].index)\n",
    "best_rmse2 = list(results_orig[['rank_test_neg_root_mean_squared_error','mean_train_neg_root_mean_squared_error', 'mean_test_neg_root_mean_squared_error']][results_orig['rank_test_neg_root_mean_squared_error']==1].index)\n",
    "\n",
    "best2 = list(set(best_r22) | set(best_mse2) | set(best_rmse2))\n",
    "\n",
    "best_df2 = results_orig[['mean_fit_time', 'mean_test_neg_mean_squared_error', 'mean_test_neg_root_mean_squared_error', 'mean_test_r2', 'params',]].loc[best2].sort_values(by=['mean_fit_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_test_neg_mean_squared_error</th>\n",
       "      <th>mean_test_neg_root_mean_squared_error</th>\n",
       "      <th>mean_test_r2</th>\n",
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       "      <th>54</th>\n",
       "      <td>0.006625</td>\n",
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       "      <td>-30523.288386</td>\n",
       "      <td>0.845182</td>\n",
       "      <td>{'alpha': 0.0, 'l1_ratio': 0.75, 'max_iter': 5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.007313</td>\n",
       "      <td>-9.441079e+08</td>\n",
       "      <td>-30523.288386</td>\n",
       "      <td>0.845182</td>\n",
       "      <td>{'alpha': 0.0, 'l1_ratio': 0.5, 'max_iter': 50...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.007375</td>\n",
       "      <td>-9.441079e+08</td>\n",
       "      <td>-30523.288386</td>\n",
       "      <td>0.845182</td>\n",
       "      <td>{'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 5...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  mean_test_neg_mean_squared_error  \\\n",
       "54       0.006625                     -9.441079e+08   \n",
       "30       0.007313                     -9.441079e+08   \n",
       "6        0.007375                     -9.441079e+08   \n",
       "\n",
       "    mean_test_neg_root_mean_squared_error  mean_test_r2  \\\n",
       "54                          -30523.288386      0.845182   \n",
       "30                          -30523.288386      0.845182   \n",
       "6                           -30523.288386      0.845182   \n",
       "\n",
       "                                               params  \n",
       "54  {'alpha': 0.0, 'l1_ratio': 0.75, 'max_iter': 5...  \n",
       "30  {'alpha': 0.0, 'l1_ratio': 0.5, 'max_iter': 50...  \n",
       "6   {'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 5...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_df2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>alpha</th>\n",
       "      <th>l1_ratio</th>\n",
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       "      <th>positive</th>\n",
       "      <th>selection</th>\n",
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       "      <td>cyclic</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
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       "      <th>2</th>\n",
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      ],
      "text/plain": [
       "   alpha  l1_ratio  max_iter  positive selection\n",
       "0    0.0      0.75        50     False    cyclic\n",
       "1    0.0      0.50        50     False    cyclic\n",
       "2    0.0      0.25        50     False    cyclic"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(list(best_df2.params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 16 folds for each of 288 candidates, totalling 4608 fits\n"
     ]
    }
   ],
   "source": [
    "results2 = GridSearch_CV_Tuning(dataset=df2, target='Sale_Price', estimator=ElasticNet(), params=en_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>split11_train_r2</th>\n",
       "      <th>split12_train_r2</th>\n",
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       "      <th>split14_train_r2</th>\n",
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       "      <td>0.004688</td>\n",
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       "      <td>0.889512</td>\n",
       "      <td>0.888775</td>\n",
       "      <td>0.894683</td>\n",
       "      <td>0.898249</td>\n",
       "      <td>0.891763</td>\n",
       "      <td>0.895032</td>\n",
       "      <td>0.892279</td>\n",
       "      <td>0.891584</td>\n",
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       "      <td>0.004625</td>\n",
       "      <td>0.000696</td>\n",
       "      <td>0.003875</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>25</td>\n",
       "      <td>True</td>\n",
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       "      <td>{'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.889497</td>\n",
       "      <td>0.888669</td>\n",
       "      <td>0.894605</td>\n",
       "      <td>0.897407</td>\n",
       "      <td>0.891719</td>\n",
       "      <td>0.895026</td>\n",
       "      <td>0.892272</td>\n",
       "      <td>0.891548</td>\n",
       "      <td>0.892166</td>\n",
       "      <td>0.003117</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.006000</td>\n",
       "      <td>0.001732</td>\n",
       "      <td>0.004750</td>\n",
       "      <td>0.000559</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>25</td>\n",
       "      <td>False</td>\n",
       "      <td>cyclic</td>\n",
       "      <td>{'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.909006</td>\n",
       "      <td>0.909584</td>\n",
       "      <td>0.915462</td>\n",
       "      <td>0.917581</td>\n",
       "      <td>0.912927</td>\n",
       "      <td>0.912964</td>\n",
       "      <td>0.913615</td>\n",
       "      <td>0.911707</td>\n",
       "      <td>0.912856</td>\n",
       "      <td>0.002400</td>\n",
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       "      <td>0.001323</td>\n",
       "      <td>0.004687</td>\n",
       "      <td>0.000682</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>25</td>\n",
       "      <td>False</td>\n",
       "      <td>random</td>\n",
       "      <td>{'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.909095</td>\n",
       "      <td>0.909079</td>\n",
       "      <td>0.915712</td>\n",
       "      <td>0.917574</td>\n",
       "      <td>0.911769</td>\n",
       "      <td>0.913202</td>\n",
       "      <td>0.906572</td>\n",
       "      <td>0.911242</td>\n",
       "      <td>0.912124</td>\n",
       "      <td>0.002755</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.006125</td>\n",
       "      <td>0.001053</td>\n",
       "      <td>0.004625</td>\n",
       "      <td>0.000485</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>50</td>\n",
       "      <td>True</td>\n",
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       "      <td>0.889540</td>\n",
       "      <td>0.888804</td>\n",
       "      <td>0.894752</td>\n",
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       "      <td>0.895070</td>\n",
       "      <td>0.892299</td>\n",
       "      <td>0.891609</td>\n",
       "      <td>0.892795</td>\n",
       "      <td>0.002882</td>\n",
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       "      <td>0.004312</td>\n",
       "      <td>0.001102</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>500</td>\n",
       "      <td>False</td>\n",
       "      <td>random</td>\n",
       "      <td>{'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 5...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.888938</td>\n",
       "      <td>0.887985</td>\n",
       "      <td>0.894336</td>\n",
       "      <td>0.896833</td>\n",
       "      <td>0.892984</td>\n",
       "      <td>0.893849</td>\n",
       "      <td>0.890956</td>\n",
       "      <td>0.890194</td>\n",
       "      <td>0.892056</td>\n",
       "      <td>0.002661</td>\n",
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       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>0.009188</td>\n",
       "      <td>0.002855</td>\n",
       "      <td>0.005062</td>\n",
       "      <td>0.000428</td>\n",
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       "      <td>0.75</td>\n",
       "      <td>1000</td>\n",
       "      <td>True</td>\n",
       "      <td>cyclic</td>\n",
       "      <td>{'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...</td>\n",
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       "      <td>0.878881</td>\n",
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       "      <td>0.882955</td>\n",
       "      <td>0.880481</td>\n",
       "      <td>0.879361</td>\n",
       "      <td>0.880849</td>\n",
       "      <td>0.002787</td>\n",
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       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>0.010812</td>\n",
       "      <td>0.004202</td>\n",
       "      <td>0.004813</td>\n",
       "      <td>0.000634</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1000</td>\n",
       "      <td>True</td>\n",
       "      <td>random</td>\n",
       "      <td>{'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.878881</td>\n",
       "      <td>0.876649</td>\n",
       "      <td>0.881910</td>\n",
       "      <td>0.885638</td>\n",
       "      <td>0.880535</td>\n",
       "      <td>0.882955</td>\n",
       "      <td>0.880481</td>\n",
       "      <td>0.879361</td>\n",
       "      <td>0.880849</td>\n",
       "      <td>0.002786</td>\n",
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       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>0.016750</td>\n",
       "      <td>0.001854</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1000</td>\n",
       "      <td>False</td>\n",
       "      <td>cyclic</td>\n",
       "      <td>{'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.888938</td>\n",
       "      <td>0.887985</td>\n",
       "      <td>0.894336</td>\n",
       "      <td>0.896833</td>\n",
       "      <td>0.892984</td>\n",
       "      <td>0.893849</td>\n",
       "      <td>0.890956</td>\n",
       "      <td>0.890194</td>\n",
       "      <td>0.892056</td>\n",
       "      <td>0.002661</td>\n",
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       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>0.027124</td>\n",
       "      <td>0.005612</td>\n",
       "      <td>0.004188</td>\n",
       "      <td>0.000950</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1000</td>\n",
       "      <td>False</td>\n",
       "      <td>random</td>\n",
       "      <td>{'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.888938</td>\n",
       "      <td>0.887985</td>\n",
       "      <td>0.894336</td>\n",
       "      <td>0.896833</td>\n",
       "      <td>0.892984</td>\n",
       "      <td>0.893849</td>\n",
       "      <td>0.890956</td>\n",
       "      <td>0.890194</td>\n",
       "      <td>0.892056</td>\n",
       "      <td>0.002661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>288 rows × 121 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean_fit_time  std_fit_time  mean_score_time  std_score_time param_alpha  \\\n",
       "0         0.004688      0.000845         0.003626        0.000599         0.0   \n",
       "1         0.004625      0.000696         0.003875        0.000857         0.0   \n",
       "2         0.006000      0.001732         0.004750        0.000559         0.0   \n",
       "3         0.005999      0.001323         0.004687        0.000682         0.0   \n",
       "4         0.006125      0.001053         0.004625        0.000485         0.0   \n",
       "..             ...           ...              ...             ...         ...   \n",
       "283       0.024499      0.006374         0.004312        0.001102         1.5   \n",
       "284       0.009188      0.002855         0.005062        0.000428         1.5   \n",
       "285       0.010812      0.004202         0.004813        0.000634         1.5   \n",
       "286       0.016750      0.001854         0.005000        0.000353         1.5   \n",
       "287       0.027124      0.005612         0.004188        0.000950         1.5   \n",
       "\n",
       "    param_l1_ratio param_max_iter param_positive param_selection  \\\n",
       "0             0.25             25           True          cyclic   \n",
       "1             0.25             25           True          random   \n",
       "2             0.25             25          False          cyclic   \n",
       "3             0.25             25          False          random   \n",
       "4             0.25             50           True          cyclic   \n",
       "..             ...            ...            ...             ...   \n",
       "283           0.75            500          False          random   \n",
       "284           0.75           1000           True          cyclic   \n",
       "285           0.75           1000           True          random   \n",
       "286           0.75           1000          False          cyclic   \n",
       "287           0.75           1000          False          random   \n",
       "\n",
       "                                                params  ...  split8_train_r2  \\\n",
       "0    {'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...  ...         0.889512   \n",
       "1    {'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...  ...         0.889497   \n",
       "2    {'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...  ...         0.909006   \n",
       "3    {'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 2...  ...         0.909095   \n",
       "4    {'alpha': 0.0, 'l1_ratio': 0.25, 'max_iter': 5...  ...         0.889540   \n",
       "..                                                 ...  ...              ...   \n",
       "283  {'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 5...  ...         0.888938   \n",
       "284  {'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...  ...         0.878881   \n",
       "285  {'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...  ...         0.878881   \n",
       "286  {'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...  ...         0.888938   \n",
       "287  {'alpha': 1.5, 'l1_ratio': 0.75, 'max_iter': 1...  ...         0.888938   \n",
       "\n",
       "     split9_train_r2  split10_train_r2  split11_train_r2  split12_train_r2  \\\n",
       "0           0.888775          0.894683          0.898249          0.891763   \n",
       "1           0.888669          0.894605          0.897407          0.891719   \n",
       "2           0.909584          0.915462          0.917581          0.912927   \n",
       "3           0.909079          0.915712          0.917574          0.911769   \n",
       "4           0.888804          0.894752          0.898261          0.891800   \n",
       "..               ...               ...               ...               ...   \n",
       "283         0.887985          0.894336          0.896833          0.892984   \n",
       "284         0.876649          0.881910          0.885645          0.880536   \n",
       "285         0.876649          0.881910          0.885638          0.880535   \n",
       "286         0.887985          0.894336          0.896833          0.892984   \n",
       "287         0.887985          0.894336          0.896833          0.892984   \n",
       "\n",
       "     split13_train_r2  split14_train_r2  split15_train_r2  mean_train_r2  \\\n",
       "0            0.895032          0.892279          0.891584       0.892761   \n",
       "1            0.895026          0.892272          0.891548       0.892166   \n",
       "2            0.912964          0.913615          0.911707       0.912856   \n",
       "3            0.913202          0.906572          0.911242       0.912124   \n",
       "4            0.895070          0.892299          0.891609       0.892795   \n",
       "..                ...               ...               ...            ...   \n",
       "283          0.893849          0.890956          0.890194       0.892056   \n",
       "284          0.882955          0.880481          0.879361       0.880849   \n",
       "285          0.882955          0.880481          0.879361       0.880849   \n",
       "286          0.893849          0.890956          0.890194       0.892056   \n",
       "287          0.893849          0.890956          0.890194       0.892056   \n",
       "\n",
       "     std_train_r2  \n",
       "0        0.002881  \n",
       "1        0.003117  \n",
       "2        0.002400  \n",
       "3        0.002755  \n",
       "4        0.002882  \n",
       "..            ...  \n",
       "283      0.002661  \n",
       "284      0.002787  \n",
       "285      0.002786  \n",
       "286      0.002661  \n",
       "287      0.002661  \n",
       "\n",
       "[288 rows x 121 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_r3 = list(results2[['rank_test_r2','mean_train_r2', 'mean_test_r2']][results2['rank_test_r2']==1].index)\n",
    "best_mse3 = list(results2[['rank_test_neg_mean_squared_error','mean_train_neg_mean_squared_error', 'mean_test_neg_mean_squared_error']][results2['rank_test_neg_mean_squared_error']==1].index)\n",
    "best_rmse3 = list(results2[['rank_test_neg_root_mean_squared_error','mean_train_neg_root_mean_squared_error', 'mean_test_neg_root_mean_squared_error']][results2['rank_test_neg_root_mean_squared_error']==1].index)\n",
    "\n",
    "best3 = list(set(best_r3) | set(best_mse3) | set(best_rmse3))\n",
    "\n",
    "best_df3 = results_orig[['mean_fit_time', 'mean_test_neg_mean_squared_error', 'mean_test_neg_root_mean_squared_error', 'mean_test_r2', 'params',]].loc[best3].sort_values(by=['mean_fit_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_test_neg_mean_squared_error</th>\n",
       "      <th>mean_test_neg_root_mean_squared_error</th>\n",
       "      <th>mean_test_r2</th>\n",
       "      <th>params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.032</td>\n",
       "      <td>-9.449134e+08</td>\n",
       "      <td>-30534.527905</td>\n",
       "      <td>0.845051</td>\n",
       "      <td>{'alpha': 0.0, 'l1_ratio': 0.5, 'max_iter': 50...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  mean_test_neg_mean_squared_error  \\\n",
       "43          0.032                     -9.449134e+08   \n",
       "\n",
       "    mean_test_neg_root_mean_squared_error  mean_test_r2  \\\n",
       "43                          -30534.527905      0.845051   \n",
       "\n",
       "                                               params  \n",
       "43  {'alpha': 0.0, 'l1_ratio': 0.5, 'max_iter': 50...  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alpha</th>\n",
       "      <th>l1_ratio</th>\n",
       "      <th>max_iter</th>\n",
       "      <th>positive</th>\n",
       "      <th>selection</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>500</td>\n",
       "      <td>False</td>\n",
       "      <td>random</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alpha  l1_ratio  max_iter  positive selection\n",
       "0    0.0       0.5       500     False    random"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(list(best_df3.params))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Elastic Net Evaluation - Leave One Out Metrics Computing\n",
    "\n",
    "Now that we have some good knowledge about hyperparameters for the estimator, we can closely analyze how accurate the model is.\n",
    "\n",
    "To get the most accurate results on the test, we are going to use the Leave One Out Approach for the instances. The model is going to be evaluated multiple times and the dataset will be changed in place. \n",
    "This is going to allow us to find the best and worst predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "Enet1 = ElasticNet(alpha=0.0, l1_ratio = 0.5, max_iter=50, positive=True, selection = 'cyclic')\n",
    "Enet2 = ElasticNet(alpha=0.0, l1_ratio = 0.75, max_iter=50, positive=True, selection = 'random')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RMSE': 26700.578915042264,\n",
       " 'MSE': 712920914.3983995,\n",
       " 'MAE': 19189.81572759709,\n",
       " 'R2': 0.8778111867181878,\n",
       " 'MAX_Err': 227541.11343941686}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df1 = df.copy()\n",
    "LOO_estimator_eval(dataset=test_df1, target='Sale_Price', \n",
    "                   estimator=Enet1, \n",
    "                   params=None, ignore=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RMSE': 26701.282899444563,\n",
       " 'MSE': 712958508.4761707,\n",
       " 'MAE': 19190.05150862439,\n",
       " 'R2': 0.8778047434007643,\n",
       " 'MAX_Err': 227677.74990962807}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df2 = df.copy()\n",
    "LOO_estimator_eval(dataset=test_df2, target='Sale_Price', \n",
    "                   estimator=Enet2, \n",
    "                   params=None, ignore=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that for the first dataset, the most parsimonious model is also the best one!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RMSE': 198156.87459070655,\n",
       " 'MSE': 39266146947.557,\n",
       " 'MAE': 182843.00038124286,\n",
       " 'R2': -5.7298964033338144,\n",
       " 'MAX_Err': 615000.0}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df3 = df2.copy()\n",
    "LOO_estimator_eval(dataset=test_df3, target='Sale_Price', \n",
    "                   estimator=Enet1, \n",
    "                   params=None, ignore=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RMSE': 198156.87459070655,\n",
       " 'MSE': 39266146947.557,\n",
       " 'MAE': 182843.00038124286,\n",
       " 'R2': -5.7298964033338144,\n",
       " 'MAX_Err': 615000.0}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df4 = df2.copy()\n",
    "LOO_estimator_eval(dataset=test_df4, target='Sale_Price', \n",
    "                   estimator=Enet2, \n",
    "                   params=None, ignore=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Curiosly enough, the RMSE is much smaller here, but the RSquared Value is looking very bad."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RMSE': 32941.440984012486,\n",
       " 'MSE': 1085138534.1031773,\n",
       " 'MAE': 20505.819714176752,\n",
       " 'R2': 0.8299077387862126,\n",
       " 'MAX_Err': 564428.9879390353}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df5 = df_orig.copy()\n",
    "LOO_estimator_eval(dataset=test_df5, target='Sale_Price', \n",
    "                   estimator=Enet1, \n",
    "                   params=None, ignore=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RMSE': 32946.278884246545,\n",
       " 'MSE': 1085457292.3185496,\n",
       " 'MAE': 20507.795693128115,\n",
       " 'R2': 0.8298577743771264,\n",
       " 'MAX_Err': 564379.406314693}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df6 = df_orig.copy()\n",
    "LOO_estimator_eval(dataset=test_df6, target='Sale_Price', \n",
    "                   estimator=Enet2, \n",
    "                   params=None, ignore=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Worst Predictions and Best Predictions\n",
    "\n",
    "To consider the best and worst predictions, I decided to consider the models with the lowest error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modified First Subset\n",
    "test_df1['Prediction_Error'] = np.abs(test_df1['Sale_Price']-test_df1['Predicted'])\n",
    "# Original Second Subset\n",
    "test_df6['Prediction_Error'] = np.abs(test_df6['Sale_Price']-test_df6['Predicted'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most wrong on df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BC_Bsmt_Unf_SF</th>\n",
       "      <th>Bedroom_AbvGr</th>\n",
       "      <th>Bsmt_Full_Bath</th>\n",
       "      <th>Bsmt_Unf_SF</th>\n",
       "      <th>Central_Air</th>\n",
       "      <th>External_Eval</th>\n",
       "      <th>Fireplace_Gr_Area_Ratio</th>\n",
       "      <th>Fireplace_Qu</th>\n",
       "      <th>Garage_Area</th>\n",
       "      <th>Garage_Cars</th>\n",
       "      <th>...</th>\n",
       "      <th>garage_type_1</th>\n",
       "      <th>hs_style_1</th>\n",
       "      <th>neighborhoods_1</th>\n",
       "      <th>neighborhoods_2</th>\n",
       "      <th>neighborhoods_3</th>\n",
       "      <th>neighborhoods_4</th>\n",
       "      <th>sale_cond_1</th>\n",
       "      <th>Sale_Price</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Prediction_Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>3.016554</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.90000</td>\n",
       "      <td>1182.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>820.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>611657.0</td>\n",
       "      <td>384115.886561</td>\n",
       "      <td>227541.113439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>4.213478</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1734.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1117.79990</td>\n",
       "      <td>2822.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>582933.0</td>\n",
       "      <td>389741.360722</td>\n",
       "      <td>193191.639278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2106</th>\n",
       "      <td>4.218352</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1752.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>644.00000</td>\n",
       "      <td>2868.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>716.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>556581.0</td>\n",
       "      <td>375847.410645</td>\n",
       "      <td>180733.589355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>3.341847</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1563.29990</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>789.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>615000.0</td>\n",
       "      <td>439400.644016</td>\n",
       "      <td>175599.355984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>3.441461</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1106.99990</td>\n",
       "      <td>1337.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>762.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>610000.0</td>\n",
       "      <td>437285.507367</td>\n",
       "      <td>172714.492633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>3.840031</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>788.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>653.39996</td>\n",
       "      <td>1201.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>672.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>555000.0</td>\n",
       "      <td>406254.448952</td>\n",
       "      <td>148745.551048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>3.597804</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>280.00000</td>\n",
       "      <td>1419.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>392000.0</td>\n",
       "      <td>257044.504160</td>\n",
       "      <td>134955.495840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1601</th>\n",
       "      <td>4.229794</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1795.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.00000</td>\n",
       "      <td>1795.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>895.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>147000.0</td>\n",
       "      <td>280521.228665</td>\n",
       "      <td>133521.228665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2104</th>\n",
       "      <td>4.153759</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1528.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>642.60000</td>\n",
       "      <td>3390.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>758.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>545224.0</td>\n",
       "      <td>413432.911475</td>\n",
       "      <td>131791.088525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2218</th>\n",
       "      <td>2.482185</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2681.80000</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>959.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>584500.0</td>\n",
       "      <td>456239.795410</td>\n",
       "      <td>128260.204590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      BC_Bsmt_Unf_SF  Bedroom_AbvGr  Bsmt_Full_Bath  Bsmt_Unf_SF  Central_Air  \\\n",
       "40          3.016554            2.0             1.0        142.0          0.0   \n",
       "389         4.213478            4.0             0.0       1734.0          0.0   \n",
       "2106        4.218352            4.0             0.0       1752.0          0.0   \n",
       "957         3.341847            1.0             2.0        278.0          0.0   \n",
       "388         3.441461            2.0             2.0        342.0          0.0   \n",
       "380         3.840031            2.0             1.0        788.0          0.0   \n",
       "1469        3.597804            2.0             1.0        474.0          0.0   \n",
       "1601        4.229794            2.0             0.0       1795.0          0.0   \n",
       "2104        4.153759            5.0             0.0       1528.0          0.0   \n",
       "2218        2.482185            4.0             1.0         48.0          0.0   \n",
       "\n",
       "      External_Eval  Fireplace_Gr_Area_Ratio  Fireplace_Qu  Garage_Area  \\\n",
       "40        180.90000                   1182.0           4.0        820.0   \n",
       "389      1117.79990                   2822.0           4.0       1020.0   \n",
       "2106      644.00000                   2868.0           4.0        716.0   \n",
       "957      1563.29990                   1235.0           4.0        789.0   \n",
       "388      1106.99990                   1337.0           4.0        762.0   \n",
       "380       653.39996                   1201.0           4.0        672.0   \n",
       "1469      280.00000                   1419.0           4.0        567.0   \n",
       "1601       98.00000                   1795.0           4.0        895.0   \n",
       "2104      642.60000                   3390.0           5.0        758.0   \n",
       "2218     2681.80000                   3500.0           3.0        959.0   \n",
       "\n",
       "      Garage_Cars  ...  garage_type_1  hs_style_1  neighborhoods_1  \\\n",
       "40            3.0  ...            1.0         1.0              0.0   \n",
       "389           3.0  ...            1.0         1.0              0.0   \n",
       "2106          3.0  ...            1.0         1.0              0.0   \n",
       "957           3.0  ...            1.0         1.0              0.0   \n",
       "388           3.0  ...            1.0         1.0              0.0   \n",
       "380           3.0  ...            1.0         1.0              0.0   \n",
       "1469          2.0  ...            1.0         1.0              0.0   \n",
       "1601          3.0  ...            1.0         1.0              0.0   \n",
       "2104          3.0  ...            1.0         1.0              0.0   \n",
       "2218          3.0  ...            1.0         1.0              0.0   \n",
       "\n",
       "      neighborhoods_2  neighborhoods_3  neighborhoods_4  sale_cond_1  \\\n",
       "40                0.0              0.0              0.0          0.0   \n",
       "389               0.0              0.0              0.0          0.0   \n",
       "2106              0.0              0.0              0.0          0.0   \n",
       "957               0.0              0.0              0.0          1.0   \n",
       "388               0.0              0.0              0.0          1.0   \n",
       "380               0.0              0.0              0.0          1.0   \n",
       "1469              0.0              0.0              0.0          0.0   \n",
       "1601              0.0              1.0              0.0          0.0   \n",
       "2104              0.0              0.0              0.0          0.0   \n",
       "2218              0.0              0.0              0.0          1.0   \n",
       "\n",
       "      Sale_Price      Predicted  Prediction_Error  \n",
       "40      611657.0  384115.886561     227541.113439  \n",
       "389     582933.0  389741.360722     193191.639278  \n",
       "2106    556581.0  375847.410645     180733.589355  \n",
       "957     615000.0  439400.644016     175599.355984  \n",
       "388     610000.0  437285.507367     172714.492633  \n",
       "380     555000.0  406254.448952     148745.551048  \n",
       "1469    392000.0  257044.504160     134955.495840  \n",
       "1601    147000.0  280521.228665     133521.228665  \n",
       "2104    545224.0  413432.911475     131791.088525  \n",
       "2218    584500.0  456239.795410     128260.204590  \n",
       "\n",
       "[10 rows x 39 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df1.sort_values(by=['Prediction_Error', 'Sale_Price'], ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>BC_Bsmt_Unf_SF</th>\n",
       "      <th>Bedroom_AbvGr</th>\n",
       "      <th>Bsmt_Full_Bath</th>\n",
       "      <th>Bsmt_Unf_SF</th>\n",
       "      <th>Central_Air</th>\n",
       "      <th>External_Eval</th>\n",
       "      <th>Fireplace_Gr_Area_Ratio</th>\n",
       "      <th>Fireplace_Qu</th>\n",
       "      <th>Garage_Area</th>\n",
       "      <th>Garage_Cars</th>\n",
       "      <th>...</th>\n",
       "      <th>garage_type_1</th>\n",
       "      <th>hs_style_1</th>\n",
       "      <th>neighborhoods_1</th>\n",
       "      <th>neighborhoods_2</th>\n",
       "      <th>neighborhoods_3</th>\n",
       "      <th>neighborhoods_4</th>\n",
       "      <th>sale_cond_1</th>\n",
       "      <th>Sale_Price</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Prediction_Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.334758</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.860000</td>\n",
       "      <td>663.080000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>692.375983</td>\n",
       "      <td>1718.480000</td>\n",
       "      <td>3.620000</td>\n",
       "      <td>698.420000</td>\n",
       "      <td>2.440000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.14000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>391140.46000</td>\n",
       "      <td>320955.147539</td>\n",
       "      <td>102640.185419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.076880</td>\n",
       "      <td>1.216385</td>\n",
       "      <td>0.606428</td>\n",
       "      <td>610.674396</td>\n",
       "      <td>0.197949</td>\n",
       "      <td>561.196550</td>\n",
       "      <td>887.243432</td>\n",
       "      <td>1.193349</td>\n",
       "      <td>276.691982</td>\n",
       "      <td>0.860944</td>\n",
       "      <td>...</td>\n",
       "      <td>0.274048</td>\n",
       "      <td>0.274048</td>\n",
       "      <td>0.35051</td>\n",
       "      <td>0.141421</td>\n",
       "      <td>0.274048</td>\n",
       "      <td>0.404061</td>\n",
       "      <td>0.504672</td>\n",
       "      <td>147980.18752</td>\n",
       "      <td>93300.710734</td>\n",
       "      <td>36658.517011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39300.00000</td>\n",
       "      <td>-33125.718991</td>\n",
       "      <td>67138.312448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.293201</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>251.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>318.000000</td>\n",
       "      <td>1129.125000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>553.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>312375.00000</td>\n",
       "      <td>274177.924017</td>\n",
       "      <td>74674.613881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.607678</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>484.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>609.049980</td>\n",
       "      <td>1715.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>734.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>423935.00000</td>\n",
       "      <td>348347.262778</td>\n",
       "      <td>92186.270918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.832562</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>934.500000</td>\n",
       "      <td>2463.250000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>890.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>483517.25000</td>\n",
       "      <td>386535.602840</td>\n",
       "      <td>119297.106999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.312694</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2140.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2681.800000</td>\n",
       "      <td>3500.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1174.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>615000.00000</td>\n",
       "      <td>456239.795410</td>\n",
       "      <td>227541.113439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       BC_Bsmt_Unf_SF  Bedroom_AbvGr  Bsmt_Full_Bath  Bsmt_Unf_SF  \\\n",
       "count       50.000000      50.000000       50.000000    50.000000   \n",
       "mean         3.334758       2.700000        0.860000   663.080000   \n",
       "std          1.076880       1.216385        0.606428   610.674396   \n",
       "min          0.000000       1.000000        0.000000     0.000000   \n",
       "25%          3.293201       2.000000        0.250000   251.750000   \n",
       "50%          3.607678       3.000000        1.000000   484.000000   \n",
       "75%          3.832562       3.000000        1.000000   776.000000   \n",
       "max          4.312694       6.000000        2.000000  2140.000000   \n",
       "\n",
       "       Central_Air  External_Eval  Fireplace_Gr_Area_Ratio  Fireplace_Qu  \\\n",
       "count    50.000000      50.000000                50.000000     50.000000   \n",
       "mean      0.040000     692.375983              1718.480000      3.620000   \n",
       "std       0.197949     561.196550               887.243432      1.193349   \n",
       "min       0.000000       0.000000                 0.000000      0.000000   \n",
       "25%       0.000000     318.000000              1129.125000      4.000000   \n",
       "50%       0.000000     609.049980              1715.000000      4.000000   \n",
       "75%       0.000000     934.500000              2463.250000      4.000000   \n",
       "max       1.000000    2681.800000              3500.000000      5.000000   \n",
       "\n",
       "       Garage_Area  Garage_Cars  ...  garage_type_1  hs_style_1  \\\n",
       "count    50.000000    50.000000  ...      50.000000   50.000000   \n",
       "mean    698.420000     2.440000  ...       0.920000    0.920000   \n",
       "std     276.691982     0.860944  ...       0.274048    0.274048   \n",
       "min       0.000000     0.000000  ...       0.000000    0.000000   \n",
       "25%     553.500000     2.000000  ...       1.000000    1.000000   \n",
       "50%     734.000000     3.000000  ...       1.000000    1.000000   \n",
       "75%     890.000000     3.000000  ...       1.000000    1.000000   \n",
       "max    1174.000000     3.000000  ...       1.000000    1.000000   \n",
       "\n",
       "       neighborhoods_1  neighborhoods_2  neighborhoods_3  neighborhoods_4  \\\n",
       "count         50.00000        50.000000        50.000000        50.000000   \n",
       "mean           0.14000         0.020000         0.080000         0.200000   \n",
       "std            0.35051         0.141421         0.274048         0.404061   \n",
       "min            0.00000         0.000000         0.000000         0.000000   \n",
       "25%            0.00000         0.000000         0.000000         0.000000   \n",
       "50%            0.00000         0.000000         0.000000         0.000000   \n",
       "75%            0.00000         0.000000         0.000000         0.000000   \n",
       "max            1.00000         1.000000         1.000000         1.000000   \n",
       "\n",
       "       sale_cond_1    Sale_Price      Predicted  Prediction_Error  \n",
       "count    50.000000      50.00000      50.000000         50.000000  \n",
       "mean      0.480000  391140.46000  320955.147539     102640.185419  \n",
       "std       0.504672  147980.18752   93300.710734      36658.517011  \n",
       "min       0.000000   39300.00000  -33125.718991      67138.312448  \n",
       "25%       0.000000  312375.00000  274177.924017      74674.613881  \n",
       "50%       0.000000  423935.00000  348347.262778      92186.270918  \n",
       "75%       1.000000  483517.25000  386535.602840     119297.106999  \n",
       "max       1.000000  615000.00000  456239.795410     227541.113439  \n",
       "\n",
       "[8 rows x 39 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df1.sort_values(by=['Prediction_Error', 'Sale_Price'], ascending=False).head(50).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best on df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BC_Bsmt_Unf_SF</th>\n",
       "      <th>Bedroom_AbvGr</th>\n",
       "      <th>Bsmt_Full_Bath</th>\n",
       "      <th>Bsmt_Unf_SF</th>\n",
       "      <th>Central_Air</th>\n",
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       "      <th>Fireplace_Gr_Area_Ratio</th>\n",
       "      <th>Fireplace_Qu</th>\n",
       "      <th>Garage_Area</th>\n",
       "      <th>Garage_Cars</th>\n",
       "      <th>...</th>\n",
       "      <th>garage_type_1</th>\n",
       "      <th>hs_style_1</th>\n",
       "      <th>neighborhoods_1</th>\n",
       "      <th>neighborhoods_2</th>\n",
       "      <th>neighborhoods_3</th>\n",
       "      <th>neighborhoods_4</th>\n",
       "      <th>sale_cond_1</th>\n",
       "      <th>Sale_Price</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Prediction_Error</th>\n",
       "    </tr>\n",
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       "      <th>570</th>\n",
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       "      <td>314.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.300000</td>\n",
       "      <td>662.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>205000.0</td>\n",
       "      <td>204999.538899</td>\n",
       "      <td>0.461101</td>\n",
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       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>3.882115</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>1477.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>145000.0</td>\n",
       "      <td>145004.095411</td>\n",
       "      <td>4.095411</td>\n",
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       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>2.246507</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>167000.0</td>\n",
       "      <td>167006.353973</td>\n",
       "      <td>6.353973</td>\n",
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       "    <tr>\n",
       "      <th>1849</th>\n",
       "      <td>3.150307</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>114.399994</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>108500.0</td>\n",
       "      <td>108474.441265</td>\n",
       "      <td>25.558735</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.444260</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1078.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>149900.0</td>\n",
       "      <td>149929.120875</td>\n",
       "      <td>29.120875</td>\n",
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       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>3.705522</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.800000</td>\n",
       "      <td>1404.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>504.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>160000.0</td>\n",
       "      <td>160080.093319</td>\n",
       "      <td>80.093319</td>\n",
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       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>4.222642</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1768.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>136500.0</td>\n",
       "      <td>136418.823942</td>\n",
       "      <td>81.176058</td>\n",
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       "    <tr>\n",
       "      <th>1756</th>\n",
       "      <td>3.062317</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>770.899960</td>\n",
       "      <td>1647.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>153000.0</td>\n",
       "      <td>152898.103199</td>\n",
       "      <td>101.896801</td>\n",
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       "    <tr>\n",
       "      <th>2471</th>\n",
       "      <td>4.080272</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1308.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>848.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>229800.0</td>\n",
       "      <td>229912.844419</td>\n",
       "      <td>112.844419</td>\n",
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       "    <tr>\n",
       "      <th>1856</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>327.599980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>129000.0</td>\n",
       "      <td>129168.278584</td>\n",
       "      <td>168.278584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      BC_Bsmt_Unf_SF  Bedroom_AbvGr  Bsmt_Full_Bath  Bsmt_Unf_SF  Central_Air  \\\n",
       "570         3.400430            3.0             0.0        314.0          0.0   \n",
       "832         3.882115            3.0             0.0        861.0          0.0   \n",
       "439         2.246507            3.0             0.0         30.0          0.0   \n",
       "1849        3.150307            2.0             1.0        187.0          1.0   \n",
       "21          3.444260            3.0             1.0        344.0          0.0   \n",
       "881         3.705522            3.0             0.0        594.0          0.0   \n",
       "196         4.222642            4.0             0.0       1768.0          1.0   \n",
       "1756        3.062317            3.0             1.0        156.0          0.0   \n",
       "2471        4.080272            3.0             0.0       1308.0          0.0   \n",
       "1856        0.000000            3.0             1.0          0.0          0.0   \n",
       "\n",
       "      External_Eval  Fireplace_Gr_Area_Ratio  Fireplace_Qu  Garage_Area  \\\n",
       "570      300.300000                    662.0           2.0        486.0   \n",
       "832       65.000000                   1477.0           4.0        216.0   \n",
       "439        0.000000                      0.0           0.0        400.0   \n",
       "1849     114.399994                      0.0           0.0        240.0   \n",
       "21         0.000000                   1078.0           2.0        500.0   \n",
       "881       20.800000                   1404.0           1.0        504.0   \n",
       "196        0.000000                      0.0           0.0          0.0   \n",
       "1756     770.899960                   1647.0           2.0        280.0   \n",
       "2471      80.000000                      0.0           0.0        848.0   \n",
       "1856     327.599980                      0.0           0.0        240.0   \n",
       "\n",
       "      Garage_Cars  ...  garage_type_1  hs_style_1  neighborhoods_1  \\\n",
       "570           2.0  ...            1.0         1.0              1.0   \n",
       "832           1.0  ...            0.0         0.0              1.0   \n",
       "439           2.0  ...            1.0         0.0              0.0   \n",
       "1849          1.0  ...            0.0         0.0              1.0   \n",
       "21            2.0  ...            1.0         1.0              1.0   \n",
       "881           2.0  ...            1.0         1.0              0.0   \n",
       "196           0.0  ...            0.0         1.0              1.0   \n",
       "1756          1.0  ...            1.0         1.0              1.0   \n",
       "2471          3.0  ...            1.0         1.0              0.0   \n",
       "1856          1.0  ...            0.0         0.0              1.0   \n",
       "\n",
       "      neighborhoods_2  neighborhoods_3  neighborhoods_4  sale_cond_1  \\\n",
       "570               0.0              0.0              0.0          1.0   \n",
       "832               0.0              0.0              0.0          1.0   \n",
       "439               0.0              1.0              0.0          1.0   \n",
       "1849              0.0              0.0              0.0          1.0   \n",
       "21                0.0              0.0              0.0          1.0   \n",
       "881               0.0              0.0              1.0          1.0   \n",
       "196               0.0              0.0              0.0          1.0   \n",
       "1756              0.0              0.0              0.0          1.0   \n",
       "2471              0.0              0.0              1.0          0.0   \n",
       "1856              0.0              0.0              0.0          1.0   \n",
       "\n",
       "      Sale_Price      Predicted  Prediction_Error  \n",
       "570     205000.0  204999.538899          0.461101  \n",
       "832     145000.0  145004.095411          4.095411  \n",
       "439     167000.0  167006.353973          6.353973  \n",
       "1849    108500.0  108474.441265         25.558735  \n",
       "21      149900.0  149929.120875         29.120875  \n",
       "881     160000.0  160080.093319         80.093319  \n",
       "196     136500.0  136418.823942         81.176058  \n",
       "1756    153000.0  152898.103199        101.896801  \n",
       "2471    229800.0  229912.844419        112.844419  \n",
       "1856    129000.0  129168.278584        168.278584  \n",
       "\n",
       "[10 rows x 39 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df1.sort_values(by=['Prediction_Error', 'Sale_Price']).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BC_Bsmt_Unf_SF</th>\n",
       "      <th>Bedroom_AbvGr</th>\n",
       "      <th>Bsmt_Full_Bath</th>\n",
       "      <th>Bsmt_Unf_SF</th>\n",
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       "      <th>External_Eval</th>\n",
       "      <th>Fireplace_Gr_Area_Ratio</th>\n",
       "      <th>Fireplace_Qu</th>\n",
       "      <th>Garage_Area</th>\n",
       "      <th>Garage_Cars</th>\n",
       "      <th>...</th>\n",
       "      <th>garage_type_1</th>\n",
       "      <th>hs_style_1</th>\n",
       "      <th>neighborhoods_1</th>\n",
       "      <th>neighborhoods_2</th>\n",
       "      <th>neighborhoods_3</th>\n",
       "      <th>neighborhoods_4</th>\n",
       "      <th>sale_cond_1</th>\n",
       "      <th>Sale_Price</th>\n",
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       "      <td>50.000000</td>\n",
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       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
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       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.144780</td>\n",
       "      <td>2.960000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>457.020000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>261.147995</td>\n",
       "      <td>795.050000</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>469.520000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>175619.980000</td>\n",
       "      <td>175734.411029</td>\n",
       "      <td>324.391859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.131707</td>\n",
       "      <td>0.604743</td>\n",
       "      <td>0.484873</td>\n",
       "      <td>363.820963</td>\n",
       "      <td>0.274048</td>\n",
       "      <td>229.660805</td>\n",
       "      <td>830.023581</td>\n",
       "      <td>1.618263</td>\n",
       "      <td>182.545185</td>\n",
       "      <td>0.699854</td>\n",
       "      <td>...</td>\n",
       "      <td>0.418452</td>\n",
       "      <td>0.443087</td>\n",
       "      <td>0.471212</td>\n",
       "      <td>0.141421</td>\n",
       "      <td>0.443087</td>\n",
       "      <td>0.484873</td>\n",
       "      <td>0.274048</td>\n",
       "      <td>45615.249433</td>\n",
       "      <td>45588.104602</td>\n",
       "      <td>165.737260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>108500.000000</td>\n",
       "      <td>108474.441265</td>\n",
       "      <td>0.461101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.155381</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>189.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>86.849996</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>141250.000000</td>\n",
       "      <td>141385.718449</td>\n",
       "      <td>212.120088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.488767</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>377.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>213.199990</td>\n",
       "      <td>653.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>461.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166000.000000</td>\n",
       "      <td>166175.283053</td>\n",
       "      <td>355.193865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.764724</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>673.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>354.050000</td>\n",
       "      <td>1467.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>588.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>204937.500000</td>\n",
       "      <td>205102.493491</td>\n",
       "      <td>463.079822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.222642</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1768.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1012.699950</td>\n",
       "      <td>2614.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>869.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>336000.000000</td>\n",
       "      <td>336439.198900</td>\n",
       "      <td>552.738495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       BC_Bsmt_Unf_SF  Bedroom_AbvGr  Bsmt_Full_Bath  Bsmt_Unf_SF  \\\n",
       "count       50.000000      50.000000       50.000000    50.000000   \n",
       "mean         3.144780       2.960000        0.360000   457.020000   \n",
       "std          1.131707       0.604743        0.484873   363.820963   \n",
       "min          0.000000       1.000000        0.000000     0.000000   \n",
       "25%          3.155381       3.000000        0.000000   189.000000   \n",
       "50%          3.488767       3.000000        0.000000   377.500000   \n",
       "75%          3.764724       3.000000        1.000000   673.000000   \n",
       "max          4.222642       4.000000        1.000000  1768.000000   \n",
       "\n",
       "       Central_Air  External_Eval  Fireplace_Gr_Area_Ratio  Fireplace_Qu  \\\n",
       "count    50.000000      50.000000                50.000000     50.000000   \n",
       "mean      0.080000     261.147995               795.050000      1.560000   \n",
       "std       0.274048     229.660805               830.023581      1.618263   \n",
       "min       0.000000       0.000000                 0.000000      0.000000   \n",
       "25%       0.000000      86.849996                 0.000000      0.000000   \n",
       "50%       0.000000     213.199990               653.000000      1.000000   \n",
       "75%       0.000000     354.050000              1467.750000      3.000000   \n",
       "max       1.000000    1012.699950              2614.000000      4.000000   \n",
       "\n",
       "       Garage_Area  Garage_Cars  ...  garage_type_1  hs_style_1  \\\n",
       "count    50.000000    50.000000  ...      50.000000   50.000000   \n",
       "mean    469.520000     1.800000  ...       0.780000    0.740000   \n",
       "std     182.545185     0.699854  ...       0.418452    0.443087   \n",
       "min       0.000000     0.000000  ...       0.000000    0.000000   \n",
       "25%     309.000000     1.000000  ...       1.000000    0.250000   \n",
       "50%     461.500000     2.000000  ...       1.000000    1.000000   \n",
       "75%     588.750000     2.000000  ...       1.000000    1.000000   \n",
       "max     869.000000     3.000000  ...       1.000000    1.000000   \n",
       "\n",
       "       neighborhoods_1  neighborhoods_2  neighborhoods_3  neighborhoods_4  \\\n",
       "count        50.000000        50.000000        50.000000        50.000000   \n",
       "mean          0.320000         0.020000         0.260000         0.360000   \n",
       "std           0.471212         0.141421         0.443087         0.484873   \n",
       "min           0.000000         0.000000         0.000000         0.000000   \n",
       "25%           0.000000         0.000000         0.000000         0.000000   \n",
       "50%           0.000000         0.000000         0.000000         0.000000   \n",
       "75%           1.000000         0.000000         0.750000         1.000000   \n",
       "max           1.000000         1.000000         1.000000         1.000000   \n",
       "\n",
       "       sale_cond_1     Sale_Price      Predicted  Prediction_Error  \n",
       "count    50.000000      50.000000      50.000000         50.000000  \n",
       "mean      0.920000  175619.980000  175734.411029        324.391859  \n",
       "std       0.274048   45615.249433   45588.104602        165.737260  \n",
       "min       0.000000  108500.000000  108474.441265          0.461101  \n",
       "25%       1.000000  141250.000000  141385.718449        212.120088  \n",
       "50%       1.000000  166000.000000  166175.283053        355.193865  \n",
       "75%       1.000000  204937.500000  205102.493491        463.079822  \n",
       "max       1.000000  336000.000000  336439.198900        552.738495  \n",
       "\n",
       "[8 rows x 39 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df1.sort_values(by=['Prediction_Error', 'Sale_Price']).head(50).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most wrong on df_orig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>Age</th>\n",
       "      <th>BC_Bsmt_Unf_SF</th>\n",
       "      <th>BC_External_SF</th>\n",
       "      <th>Baths</th>\n",
       "      <th>Bedroom_Liv_Area_Ratio</th>\n",
       "      <th>Bsmt_Eval</th>\n",
       "      <th>Bsmt_Unf_SF</th>\n",
       "      <th>Central_Air</th>\n",
       "      <th>Exter_Qual</th>\n",
       "      <th>Fireplace_Eval</th>\n",
       "      <th>...</th>\n",
       "      <th>bldg_type_3</th>\n",
       "      <th>garage_type_1</th>\n",
       "      <th>hs_style_1</th>\n",
       "      <th>neighborhoods_1</th>\n",
       "      <th>neighborhoods_3</th>\n",
       "      <th>neighborhoods_4</th>\n",
       "      <th>sale_cond_1</th>\n",
       "      <th>Sale_Price</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Prediction_Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.599931</td>\n",
       "      <td>38.787690</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1880.6666</td>\n",
       "      <td>25356.5000</td>\n",
       "      <td>466.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3761.3333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160000.0</td>\n",
       "      <td>724379.406315</td>\n",
       "      <td>564379.406315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2180</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>4.003929</td>\n",
       "      <td>39.531784</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2547.5000</td>\n",
       "      <td>21144.2500</td>\n",
       "      <td>1085.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5095.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>183850.0</td>\n",
       "      <td>676949.090637</td>\n",
       "      <td>493099.090637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2181</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.903065</td>\n",
       "      <td>31.517199</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1558.6666</td>\n",
       "      <td>13022.7000</td>\n",
       "      <td>878.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9352.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>184750.0</td>\n",
       "      <td>547177.406652</td>\n",
       "      <td>362427.406652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1760</th>\n",
       "      <td>11.0</td>\n",
       "      <td>3.387806</td>\n",
       "      <td>35.481370</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1119.0000</td>\n",
       "      <td>9464.2000</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3876.3296</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>745000.0</td>\n",
       "      <td>421828.835095</td>\n",
       "      <td>323171.164905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1767</th>\n",
       "      <td>13.0</td>\n",
       "      <td>3.959812</td>\n",
       "      <td>26.965897</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1079.0000</td>\n",
       "      <td>10142.6010</td>\n",
       "      <td>989.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4825.4346</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>755000.0</td>\n",
       "      <td>474372.771317</td>\n",
       "      <td>280627.228683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.024274</td>\n",
       "      <td>11.323102</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1182.0000</td>\n",
       "      <td>9669.5000</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2364.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>611657.0</td>\n",
       "      <td>404642.442164</td>\n",
       "      <td>207014.557836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1182</th>\n",
       "      <td>31.0</td>\n",
       "      <td>3.708188</td>\n",
       "      <td>20.123798</td>\n",
       "      <td>3.0</td>\n",
       "      <td>981.3333</td>\n",
       "      <td>3056.5498</td>\n",
       "      <td>584.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5888.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>348895.438547</td>\n",
       "      <td>198895.438547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.226680</td>\n",
       "      <td>26.458273</td>\n",
       "      <td>3.5</td>\n",
       "      <td>705.5000</td>\n",
       "      <td>7196.1000</td>\n",
       "      <td>1734.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5644.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>582933.0</td>\n",
       "      <td>392703.572447</td>\n",
       "      <td>190229.427553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2332</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.231577</td>\n",
       "      <td>23.635477</td>\n",
       "      <td>3.5</td>\n",
       "      <td>717.0000</td>\n",
       "      <td>7868.4000</td>\n",
       "      <td>1752.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5736.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>556581.0</td>\n",
       "      <td>368289.449488</td>\n",
       "      <td>188291.550512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2445</th>\n",
       "      <td>11.0</td>\n",
       "      <td>3.673307</td>\n",
       "      <td>27.104687</td>\n",
       "      <td>4.5</td>\n",
       "      <td>906.7500</td>\n",
       "      <td>8009.5000</td>\n",
       "      <td>543.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6282.1480</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>625000.0</td>\n",
       "      <td>437567.086794</td>\n",
       "      <td>187432.913206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age  BC_Bsmt_Unf_SF  BC_External_SF  Baths  Bedroom_Liv_Area_Ratio  \\\n",
       "1498   0.0        3.599931       38.787690    4.5               1880.6666   \n",
       "2180  -1.0        4.003929       39.531784    4.0               2547.5000   \n",
       "2181   0.0        3.903065       31.517199    4.5               1558.6666   \n",
       "1760  11.0        3.387806       35.481370    4.5               1119.0000   \n",
       "1767  13.0        3.959812       26.965897    4.0               1079.0000   \n",
       "44     1.0        3.024274       11.323102    3.5               1182.0000   \n",
       "1182  31.0        3.708188       20.123798    3.0                981.3333   \n",
       "433    1.0        4.226680       26.458273    3.5                705.5000   \n",
       "2332   1.0        4.231577       23.635477    3.5                717.0000   \n",
       "2445  11.0        3.673307       27.104687    4.5                906.7500   \n",
       "\n",
       "       Bsmt_Eval  Bsmt_Unf_SF  Central_Air  Exter_Qual  Fireplace_Eval  ...  \\\n",
       "1498  25356.5000        466.0          0.0         3.0       3761.3333  ...   \n",
       "2180  21144.2500       1085.0          0.0         3.0       5095.0000  ...   \n",
       "2181  13022.7000        878.0          0.0         3.0       9352.0000  ...   \n",
       "1760   9464.2000        300.0          0.0         2.0       3876.3296  ...   \n",
       "1767  10142.6010        989.0          0.0         3.0       4825.4346  ...   \n",
       "44     9669.5000        142.0          0.0         3.0       2364.0000  ...   \n",
       "1182   3056.5498        584.0          0.0         2.0       5888.0000  ...   \n",
       "433    7196.1000       1734.0          0.0         3.0       5644.0000  ...   \n",
       "2332   7868.4000       1752.0          0.0         2.0       5736.0000  ...   \n",
       "2445   8009.5000        543.0          0.0         2.0       6282.1480  ...   \n",
       "\n",
       "      bldg_type_3  garage_type_1  hs_style_1  neighborhoods_1  \\\n",
       "1498          0.0            1.0         1.0              1.0   \n",
       "2180          0.0            1.0         1.0              1.0   \n",
       "2181          0.0            1.0         1.0              1.0   \n",
       "1760          0.0            1.0         1.0              0.0   \n",
       "1767          0.0            1.0         1.0              0.0   \n",
       "44            0.0            1.0         1.0              0.0   \n",
       "1182          0.0            1.0         1.0              0.0   \n",
       "433           0.0            1.0         1.0              0.0   \n",
       "2332          0.0            1.0         1.0              0.0   \n",
       "2445          0.0            1.0         1.0              0.0   \n",
       "\n",
       "      neighborhoods_3  neighborhoods_4  sale_cond_1  Sale_Price  \\\n",
       "1498              0.0              0.0          0.0    160000.0   \n",
       "2180              0.0              0.0          0.0    183850.0   \n",
       "2181              0.0              0.0          0.0    184750.0   \n",
       "1760              0.0              0.0          0.0    745000.0   \n",
       "1767              0.0              0.0          1.0    755000.0   \n",
       "44                0.0              0.0          0.0    611657.0   \n",
       "1182              0.0              1.0          0.0    150000.0   \n",
       "433               0.0              0.0          0.0    582933.0   \n",
       "2332              0.0              0.0          0.0    556581.0   \n",
       "2445              0.0              0.0          1.0    625000.0   \n",
       "\n",
       "          Predicted  Prediction_Error  \n",
       "1498  724379.406315     564379.406315  \n",
       "2180  676949.090637     493099.090637  \n",
       "2181  547177.406652     362427.406652  \n",
       "1760  421828.835095     323171.164905  \n",
       "1767  474372.771317     280627.228683  \n",
       "44    404642.442164     207014.557836  \n",
       "1182  348895.438547     198895.438547  \n",
       "433   392703.572447     190229.427553  \n",
       "2332  368289.449488     188291.550512  \n",
       "2445  437567.086794     187432.913206  \n",
       "\n",
       "[10 rows x 37 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df6.sort_values(by=['Prediction_Error', 'Sale_Price'], ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Age</th>\n",
       "      <th>BC_Bsmt_Unf_SF</th>\n",
       "      <th>BC_External_SF</th>\n",
       "      <th>Baths</th>\n",
       "      <th>Bedroom_Liv_Area_Ratio</th>\n",
       "      <th>Bsmt_Eval</th>\n",
       "      <th>Bsmt_Unf_SF</th>\n",
       "      <th>Central_Air</th>\n",
       "      <th>Exter_Qual</th>\n",
       "      <th>Fireplace_Eval</th>\n",
       "      <th>...</th>\n",
       "      <th>bldg_type_3</th>\n",
       "      <th>garage_type_1</th>\n",
       "      <th>hs_style_1</th>\n",
       "      <th>neighborhoods_1</th>\n",
       "      <th>neighborhoods_3</th>\n",
       "      <th>neighborhoods_4</th>\n",
       "      <th>sale_cond_1</th>\n",
       "      <th>Sale_Price</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Prediction_Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.0000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18.040000</td>\n",
       "      <td>3.494047</td>\n",
       "      <td>22.807556</td>\n",
       "      <td>3.270000</td>\n",
       "      <td>987.739667</td>\n",
       "      <td>7450.410504</td>\n",
       "      <td>684.6200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>3955.391764</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>408134.180000</td>\n",
       "      <td>353826.812204</td>\n",
       "      <td>150105.118808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.170238</td>\n",
       "      <td>0.832978</td>\n",
       "      <td>9.973550</td>\n",
       "      <td>0.770701</td>\n",
       "      <td>466.435897</td>\n",
       "      <td>4311.861175</td>\n",
       "      <td>573.7098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.735402</td>\n",
       "      <td>1805.720223</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.303046</td>\n",
       "      <td>0.274048</td>\n",
       "      <td>0.404061</td>\n",
       "      <td>0.274048</td>\n",
       "      <td>0.370328</td>\n",
       "      <td>0.478518</td>\n",
       "      <td>175073.130292</td>\n",
       "      <td>113752.146720</td>\n",
       "      <td>99068.385822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>470.333340</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>80400.000000</td>\n",
       "      <td>138059.517472</td>\n",
       "      <td>80414.751035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.360193</td>\n",
       "      <td>18.962324</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>686.875025</td>\n",
       "      <td>4962.268625</td>\n",
       "      <td>283.5000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2475.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>254969.750000</td>\n",
       "      <td>279620.886316</td>\n",
       "      <td>92013.362740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>3.670643</td>\n",
       "      <td>23.485247</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>816.125000</td>\n",
       "      <td>7122.737500</td>\n",
       "      <td>540.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3818.831450</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>447500.000000</td>\n",
       "      <td>362268.227889</td>\n",
       "      <td>115011.902493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.250000</td>\n",
       "      <td>3.945625</td>\n",
       "      <td>27.838413</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1109.000000</td>\n",
       "      <td>9054.056250</td>\n",
       "      <td>961.2500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5257.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>543418.000000</td>\n",
       "      <td>407468.811532</td>\n",
       "      <td>174865.513362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>114.000000</td>\n",
       "      <td>4.304173</td>\n",
       "      <td>41.719830</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>2547.500000</td>\n",
       "      <td>25356.500000</td>\n",
       "      <td>2042.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9352.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "      <td>724379.406315</td>\n",
       "      <td>564379.406315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age  BC_Bsmt_Unf_SF  BC_External_SF      Baths  \\\n",
       "count   50.000000       50.000000       50.000000  50.000000   \n",
       "mean    18.040000        3.494047       22.807556   3.270000   \n",
       "std     30.170238        0.832978        9.973550   0.770701   \n",
       "min     -1.000000        0.000000        0.000000   2.000000   \n",
       "25%      1.000000        3.360193       18.962324   3.000000   \n",
       "50%      1.500000        3.670643       23.485247   3.500000   \n",
       "75%     18.250000        3.945625       27.838413   3.500000   \n",
       "max    114.000000        4.304173       41.719830   4.500000   \n",
       "\n",
       "       Bedroom_Liv_Area_Ratio     Bsmt_Eval  Bsmt_Unf_SF  Central_Air  \\\n",
       "count               50.000000     50.000000      50.0000         50.0   \n",
       "mean               987.739667   7450.410504     684.6200          0.0   \n",
       "std                466.435897   4311.861175     573.7098          0.0   \n",
       "min                470.333340      0.000000       0.0000          0.0   \n",
       "25%                686.875025   4962.268625     283.5000          0.0   \n",
       "50%                816.125000   7122.737500     540.0000          0.0   \n",
       "75%               1109.000000   9054.056250     961.2500          0.0   \n",
       "max               2547.500000  25356.500000    2042.0000          0.0   \n",
       "\n",
       "       Exter_Qual  Fireplace_Eval  ...  bldg_type_3  garage_type_1  \\\n",
       "count   50.000000       50.000000  ...         50.0      50.000000   \n",
       "mean     2.300000     3955.391764  ...          0.0       0.900000   \n",
       "std      0.735402     1805.720223  ...          0.0       0.303046   \n",
       "min      1.000000        0.000000  ...          0.0       0.000000   \n",
       "25%      2.000000     2475.000000  ...          0.0       1.000000   \n",
       "50%      2.000000     3818.831450  ...          0.0       1.000000   \n",
       "75%      3.000000     5257.750000  ...          0.0       1.000000   \n",
       "max      3.000000     9352.000000  ...          0.0       1.000000   \n",
       "\n",
       "       hs_style_1  neighborhoods_1  neighborhoods_3  neighborhoods_4  \\\n",
       "count   50.000000        50.000000        50.000000        50.000000   \n",
       "mean     0.920000         0.200000         0.080000         0.160000   \n",
       "std      0.274048         0.404061         0.274048         0.370328   \n",
       "min      0.000000         0.000000         0.000000         0.000000   \n",
       "25%      1.000000         0.000000         0.000000         0.000000   \n",
       "50%      1.000000         0.000000         0.000000         0.000000   \n",
       "75%      1.000000         0.000000         0.000000         0.000000   \n",
       "max      1.000000         1.000000         1.000000         1.000000   \n",
       "\n",
       "       sale_cond_1     Sale_Price      Predicted  Prediction_Error  \n",
       "count    50.000000      50.000000      50.000000         50.000000  \n",
       "mean      0.340000  408134.180000  353826.812204     150105.118808  \n",
       "std       0.478518  175073.130292  113752.146720      99068.385822  \n",
       "min       0.000000   80400.000000  138059.517472      80414.751035  \n",
       "25%       0.000000  254969.750000  279620.886316      92013.362740  \n",
       "50%       0.000000  447500.000000  362268.227889     115011.902493  \n",
       "75%       1.000000  543418.000000  407468.811532     174865.513362  \n",
       "max       1.000000  755000.000000  724379.406315     564379.406315  \n",
       "\n",
       "[8 rows x 37 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df6.sort_values(by=['Prediction_Error', 'Sale_Price'], ascending=False).head(50).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best on df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>BC_Bsmt_Unf_SF</th>\n",
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       "      <th>Fireplace_Eval</th>\n",
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       "      <th>neighborhoods_1</th>\n",
       "      <th>neighborhoods_3</th>\n",
       "      <th>neighborhoods_4</th>\n",
       "      <th>sale_cond_1</th>\n",
       "      <th>Sale_Price</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Prediction_Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2402</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.556804</td>\n",
       "      <td>22.718481</td>\n",
       "      <td>3.5</td>\n",
       "      <td>638.00000</td>\n",
       "      <td>4366.4995</td>\n",
       "      <td>426.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3315.1453</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>279000.0</td>\n",
       "      <td>279011.787151</td>\n",
       "      <td>11.787151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>43.0</td>\n",
       "      <td>4.022427</td>\n",
       "      <td>10.544686</td>\n",
       "      <td>4.0</td>\n",
       "      <td>458.00000</td>\n",
       "      <td>4763.1997</td>\n",
       "      <td>1128.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>136000.0</td>\n",
       "      <td>135985.511185</td>\n",
       "      <td>14.488815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1227</th>\n",
       "      <td>55.0</td>\n",
       "      <td>3.638512</td>\n",
       "      <td>16.335052</td>\n",
       "      <td>1.0</td>\n",
       "      <td>345.33334</td>\n",
       "      <td>2693.5999</td>\n",
       "      <td>505.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>112900.0</td>\n",
       "      <td>112917.821853</td>\n",
       "      <td>17.821853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1091</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.608105</td>\n",
       "      <td>19.794222</td>\n",
       "      <td>3.0</td>\n",
       "      <td>679.00000</td>\n",
       "      <td>4151.2495</td>\n",
       "      <td>474.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2716.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>217300.0</td>\n",
       "      <td>217326.790566</td>\n",
       "      <td>26.790566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>98.0</td>\n",
       "      <td>3.781024</td>\n",
       "      <td>20.762966</td>\n",
       "      <td>1.0</td>\n",
       "      <td>680.00000</td>\n",
       "      <td>1546.9999</td>\n",
       "      <td>680.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>111000.0</td>\n",
       "      <td>111047.183449</td>\n",
       "      <td>47.183449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>30.0</td>\n",
       "      <td>3.759443</td>\n",
       "      <td>21.226215</td>\n",
       "      <td>2.5</td>\n",
       "      <td>521.33330</td>\n",
       "      <td>1998.7499</td>\n",
       "      <td>650.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2708.9275</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>171000.0</td>\n",
       "      <td>171055.578695</td>\n",
       "      <td>55.578695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2232</th>\n",
       "      <td>67.0</td>\n",
       "      <td>3.455262</td>\n",
       "      <td>7.532824</td>\n",
       "      <td>1.0</td>\n",
       "      <td>405.00000</td>\n",
       "      <td>1871.9999</td>\n",
       "      <td>345.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>141000.0</td>\n",
       "      <td>141055.673238</td>\n",
       "      <td>55.673238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>32.0</td>\n",
       "      <td>3.625021</td>\n",
       "      <td>11.487687</td>\n",
       "      <td>3.5</td>\n",
       "      <td>630.33330</td>\n",
       "      <td>4008.6000</td>\n",
       "      <td>491.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3275.3080</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>212500.0</td>\n",
       "      <td>212440.766693</td>\n",
       "      <td>59.233307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>31.0</td>\n",
       "      <td>3.091660</td>\n",
       "      <td>26.543766</td>\n",
       "      <td>3.5</td>\n",
       "      <td>517.33330</td>\n",
       "      <td>2192.4749</td>\n",
       "      <td>163.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2688.1428</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>179900.0</td>\n",
       "      <td>179836.972408</td>\n",
       "      <td>63.027592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>22.0</td>\n",
       "      <td>3.709825</td>\n",
       "      <td>27.461220</td>\n",
       "      <td>2.5</td>\n",
       "      <td>571.33330</td>\n",
       "      <td>3013.4998</td>\n",
       "      <td>586.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2968.7350</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>196500.0</td>\n",
       "      <td>196432.555392</td>\n",
       "      <td>67.444608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age  BC_Bsmt_Unf_SF  BC_External_SF  Baths  Bedroom_Liv_Area_Ratio  \\\n",
       "2402   2.0        3.556804       22.718481    3.5               638.00000   \n",
       "216   43.0        4.022427       10.544686    4.0               458.00000   \n",
       "1227  55.0        3.638512       16.335052    1.0               345.33334   \n",
       "1091   1.0        3.608105       19.794222    3.0               679.00000   \n",
       "1287  98.0        3.781024       20.762966    1.0               680.00000   \n",
       "81    30.0        3.759443       21.226215    2.5               521.33330   \n",
       "2232  67.0        3.455262        7.532824    1.0               405.00000   \n",
       "113   32.0        3.625021       11.487687    3.5               630.33330   \n",
       "2351  31.0        3.091660       26.543766    3.5               517.33330   \n",
       "114   22.0        3.709825       27.461220    2.5               571.33330   \n",
       "\n",
       "      Bsmt_Eval  Bsmt_Unf_SF  Central_Air  Exter_Qual  Fireplace_Eval  ...  \\\n",
       "2402  4366.4995        426.0          0.0         2.0       3315.1453  ...   \n",
       "216   4763.1997       1128.0          1.0         0.0          0.0000  ...   \n",
       "1227  2693.5999        505.0          0.0         1.0          0.0000  ...   \n",
       "1091  4151.2495        474.0          0.0         2.0       2716.0000  ...   \n",
       "1287  1546.9999        680.0          1.0         1.0          0.0000  ...   \n",
       "81    1998.7499        650.0          0.0         1.0       2708.9275  ...   \n",
       "2232  1871.9999        345.0          0.0         1.0          0.0000  ...   \n",
       "113   4008.6000        491.0          0.0         1.0       3275.3080  ...   \n",
       "2351  2192.4749        163.0          0.0         1.0       2688.1428  ...   \n",
       "114   3013.4998        586.0          0.0         1.0       2968.7350  ...   \n",
       "\n",
       "      bldg_type_3  garage_type_1  hs_style_1  neighborhoods_1  \\\n",
       "2402          0.0            1.0         1.0              0.0   \n",
       "216           1.0            0.0         1.0              1.0   \n",
       "1227          0.0            1.0         1.0              1.0   \n",
       "1091          0.0            1.0         1.0              0.0   \n",
       "1287          0.0            1.0         1.0              1.0   \n",
       "81            0.0            1.0         1.0              1.0   \n",
       "2232          0.0            0.0         0.0              1.0   \n",
       "113           0.0            1.0         0.0              0.0   \n",
       "2351          0.0            1.0         1.0              0.0   \n",
       "114           0.0            1.0         1.0              0.0   \n",
       "\n",
       "      neighborhoods_3  neighborhoods_4  sale_cond_1  Sale_Price  \\\n",
       "2402              0.0              0.0          1.0    279000.0   \n",
       "216               0.0              0.0          1.0    136000.0   \n",
       "1227              0.0              0.0          1.0    112900.0   \n",
       "1091              0.0              0.0          0.0    217300.0   \n",
       "1287              0.0              0.0          1.0    111000.0   \n",
       "81                0.0              0.0          1.0    171000.0   \n",
       "2232              0.0              0.0          1.0    141000.0   \n",
       "113               1.0              0.0          1.0    212500.0   \n",
       "2351              1.0              0.0          1.0    179900.0   \n",
       "114               1.0              0.0          1.0    196500.0   \n",
       "\n",
       "          Predicted  Prediction_Error  \n",
       "2402  279011.787151         11.787151  \n",
       "216   135985.511185         14.488815  \n",
       "1227  112917.821853         17.821853  \n",
       "1091  217326.790566         26.790566  \n",
       "1287  111047.183449         47.183449  \n",
       "81    171055.578695         55.578695  \n",
       "2232  141055.673238         55.673238  \n",
       "113   212440.766693         59.233307  \n",
       "2351  179836.972408         63.027592  \n",
       "114   196432.555392         67.444608  \n",
       "\n",
       "[10 rows x 37 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df6.sort_values(by=['Prediction_Error', 'Sale_Price']).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Age</th>\n",
       "      <th>BC_Bsmt_Unf_SF</th>\n",
       "      <th>BC_External_SF</th>\n",
       "      <th>Baths</th>\n",
       "      <th>Bedroom_Liv_Area_Ratio</th>\n",
       "      <th>Bsmt_Eval</th>\n",
       "      <th>Bsmt_Unf_SF</th>\n",
       "      <th>Central_Air</th>\n",
       "      <th>Exter_Qual</th>\n",
       "      <th>Fireplace_Eval</th>\n",
       "      <th>...</th>\n",
       "      <th>bldg_type_3</th>\n",
       "      <th>garage_type_1</th>\n",
       "      <th>hs_style_1</th>\n",
       "      <th>neighborhoods_1</th>\n",
       "      <th>neighborhoods_3</th>\n",
       "      <th>neighborhoods_4</th>\n",
       "      <th>sale_cond_1</th>\n",
       "      <th>Sale_Price</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Prediction_Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.920000</td>\n",
       "      <td>3.353171</td>\n",
       "      <td>16.684932</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>467.651663</td>\n",
       "      <td>2921.436896</td>\n",
       "      <td>514.360000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1556.340350</td>\n",
       "      <td>...</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>0.86000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>169408.020000</td>\n",
       "      <td>169414.560893</td>\n",
       "      <td>212.034718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>28.613426</td>\n",
       "      <td>0.920362</td>\n",
       "      <td>7.509941</td>\n",
       "      <td>0.770899</td>\n",
       "      <td>117.712326</td>\n",
       "      <td>962.812833</td>\n",
       "      <td>359.023208</td>\n",
       "      <td>0.274048</td>\n",
       "      <td>0.451754</td>\n",
       "      <td>1567.272741</td>\n",
       "      <td>...</td>\n",
       "      <td>0.197949</td>\n",
       "      <td>0.453557</td>\n",
       "      <td>0.35051</td>\n",
       "      <td>0.494872</td>\n",
       "      <td>0.453557</td>\n",
       "      <td>0.197949</td>\n",
       "      <td>0.328261</td>\n",
       "      <td>49510.929436</td>\n",
       "      <td>49504.483076</td>\n",
       "      <td>134.040695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>95000.000000</td>\n",
       "      <td>95099.161213</td>\n",
       "      <td>11.787151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.750000</td>\n",
       "      <td>3.208697</td>\n",
       "      <td>12.101474</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>368.833330</td>\n",
       "      <td>2336.100000</td>\n",
       "      <td>209.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>131675.000000</td>\n",
       "      <td>131942.408786</td>\n",
       "      <td>99.147919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.500000</td>\n",
       "      <td>3.589369</td>\n",
       "      <td>18.279463</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>455.166670</td>\n",
       "      <td>2824.399900</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1577.898250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>156450.000000</td>\n",
       "      <td>156599.365027</td>\n",
       "      <td>176.818957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>3.803708</td>\n",
       "      <td>21.830725</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>553.833325</td>\n",
       "      <td>3693.112450</td>\n",
       "      <td>713.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2732.696000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>204416.250000</td>\n",
       "      <td>204176.851263</td>\n",
       "      <td>341.947519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>118.000000</td>\n",
       "      <td>4.114147</td>\n",
       "      <td>32.471565</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>703.500000</td>\n",
       "      <td>4763.199700</td>\n",
       "      <td>1368.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5207.802200</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>280000.000000</td>\n",
       "      <td>280328.224381</td>\n",
       "      <td>444.752512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age  BC_Bsmt_Unf_SF  BC_External_SF      Baths  \\\n",
       "count   50.000000       50.000000       50.000000  50.000000   \n",
       "mean    36.920000        3.353171       16.684932   2.240000   \n",
       "std     28.613426        0.920362        7.509941   0.770899   \n",
       "min      0.000000        0.000000        0.000000   1.000000   \n",
       "25%      9.750000        3.208697       12.101474   2.000000   \n",
       "50%     39.500000        3.589369       18.279463   2.000000   \n",
       "75%     50.000000        3.803708       21.830725   2.500000   \n",
       "max    118.000000        4.114147       32.471565   4.000000   \n",
       "\n",
       "       Bedroom_Liv_Area_Ratio    Bsmt_Eval  Bsmt_Unf_SF  Central_Air  \\\n",
       "count               50.000000    50.000000    50.000000    50.000000   \n",
       "mean               467.651663  2921.436896   514.360000     0.080000   \n",
       "std                117.712326   962.812833   359.023208     0.274048   \n",
       "min                288.000000     0.000000     0.000000     0.000000   \n",
       "25%                368.833330  2336.100000   209.250000     0.000000   \n",
       "50%                455.166670  2824.399900   456.000000     0.000000   \n",
       "75%                553.833325  3693.112450   713.250000     0.000000   \n",
       "max                703.500000  4763.199700  1368.000000     1.000000   \n",
       "\n",
       "       Exter_Qual  Fireplace_Eval  ...  bldg_type_3  garage_type_1  \\\n",
       "count   50.000000       50.000000  ...    50.000000      50.000000   \n",
       "mean     1.200000     1556.340350  ...     0.040000       0.720000   \n",
       "std      0.451754     1567.272741  ...     0.197949       0.453557   \n",
       "min      0.000000        0.000000  ...     0.000000       0.000000   \n",
       "25%      1.000000        0.000000  ...     0.000000       0.000000   \n",
       "50%      1.000000     1577.898250  ...     0.000000       1.000000   \n",
       "75%      1.000000     2732.696000  ...     0.000000       1.000000   \n",
       "max      2.000000     5207.802200  ...     1.000000       1.000000   \n",
       "\n",
       "       hs_style_1  neighborhoods_1  neighborhoods_3  neighborhoods_4  \\\n",
       "count    50.00000        50.000000        50.000000        50.000000   \n",
       "mean      0.86000         0.600000         0.280000         0.040000   \n",
       "std       0.35051         0.494872         0.453557         0.197949   \n",
       "min       0.00000         0.000000         0.000000         0.000000   \n",
       "25%       1.00000         0.000000         0.000000         0.000000   \n",
       "50%       1.00000         1.000000         0.000000         0.000000   \n",
       "75%       1.00000         1.000000         1.000000         0.000000   \n",
       "max       1.00000         1.000000         1.000000         1.000000   \n",
       "\n",
       "       sale_cond_1     Sale_Price      Predicted  Prediction_Error  \n",
       "count    50.000000      50.000000      50.000000         50.000000  \n",
       "mean      0.880000  169408.020000  169414.560893        212.034718  \n",
       "std       0.328261   49510.929436   49504.483076        134.040695  \n",
       "min       0.000000   95000.000000   95099.161213         11.787151  \n",
       "25%       1.000000  131675.000000  131942.408786         99.147919  \n",
       "50%       1.000000  156450.000000  156599.365027        176.818957  \n",
       "75%       1.000000  204416.250000  204176.851263        341.947519  \n",
       "max       1.000000  280000.000000  280328.224381        444.752512  \n",
       "\n",
       "[8 rows x 37 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df6.sort_values(by=['Prediction_Error', 'Sale_Price']).head(50).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### Final Comment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Elastic Net Regressor tested present a lot of variance in their results, which is hard to account for.\n",
    "\n",
    "The most probable cause are the number of estimators."
   ]
  }
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