R6_ml_machine_learning.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6️⃣ ML Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load libraries\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from IPython.display import display\n",
    "from sklearn.linear_model import ElasticNet, Lasso, Ridge\n",
    "# from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor #, AdaBoostRegressor\n",
    "from sklearn.model_selection import GridSearchCV #, RandomizedSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.svm import SVR\n",
    "import pickle\n",
    "\n",
    "# import helper functions made ad-hoc\n",
    "from R3_functions import chain_snap\n",
    "from R4_functions import regression_report_in_sample_out_of_sample\n",
    "from R6_functions import check_grid_search_hyperparameters, construct_preprocessor\n",
    "\n",
    "# Set random seed\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Load the train and validation datasets used in section 4️⃣ Create a baseline Model and carry out data cleaning procedure based on the previous findings**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_TRAIN_PATH = os.path.join(\"data\", \"data_train_processed.parquet\")\n",
    "DATA_VAL_PATH = os.path.join(\"data\", \"data_val_processed.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "• Size dataframe: (543, 15)\n",
      "• Count NaNs post processing: 0\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Area</th>\n",
       "      <th>Room</th>\n",
       "      <th>log(Price)</th>\n",
       "      <th>log(Area)</th>\n",
       "      <th>log(Room)</th>\n",
       "      <th>Zip_num</th>\n",
       "      <th>Region</th>\n",
       "      <th>Location</th>\n",
       "      <th>District</th>\n",
       "      <th>AR-ratio</th>\n",
       "      <th>log(AR-ratio)</th>\n",
       "      <th>Euclidean_distance</th>\n",
       "      <th>Haversine_distance</th>\n",
       "      <th>Manhattan_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>635000.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.36138</td>\n",
       "      <td>4.394449</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1078</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.295837</td>\n",
       "      <td>3.317122</td>\n",
       "      <td>3.346089</td>\n",
       "      <td>0.031533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>400000.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.89922</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1057</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>30.5</td>\n",
       "      <td>3.417727</td>\n",
       "      <td>1.947822</td>\n",
       "      <td>2.348155</td>\n",
       "      <td>0.036792</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Price  Area  Room  log(Price)  log(Area)  log(Room)  Zip_num  Region  \\\n",
       "0  635000.0  81.0   3.0    13.36138   4.394449   1.098612     1078       0   \n",
       "1  400000.0  61.0   2.0    12.89922   4.110874   0.693147     1057       0   \n",
       "\n",
       "   Location  District  AR-ratio  log(AR-ratio)  Euclidean_distance  \\\n",
       "0         7         8      27.0       3.295837            3.317122   \n",
       "1         5         7      30.5       3.417727            1.947822   \n",
       "\n",
       "   Haversine_distance  Manhattan_distance  \n",
       "0            3.346089            0.031533  \n",
       "1            2.348155            0.036792  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "• Train data head: None\n",
      "Count NaNs post processing: 0\n",
      "Size dataframe: (181, 15)\n"
     ]
    },
    {
     "data": {
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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>Price</th>\n",
       "      <th>Area</th>\n",
       "      <th>Room</th>\n",
       "      <th>log(Price)</th>\n",
       "      <th>log(Area)</th>\n",
       "      <th>log(Room)</th>\n",
       "      <th>Zip_num</th>\n",
       "      <th>Region</th>\n",
       "      <th>Location</th>\n",
       "      <th>District</th>\n",
       "      <th>AR-ratio</th>\n",
       "      <th>log(AR-ratio)</th>\n",
       "      <th>Euclidean_distance</th>\n",
       "      <th>Haversine_distance</th>\n",
       "      <th>Manhattan_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>425000.0</td>\n",
       "      <td>68</td>\n",
       "      <td>3</td>\n",
       "      <td>12.959844</td>\n",
       "      <td>4.219508</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1098</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>22.666667</td>\n",
       "      <td>3.120895</td>\n",
       "      <td>3.606198</td>\n",
       "      <td>4.174069</td>\n",
       "      <td>0.071974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285000.0</td>\n",
       "      <td>96</td>\n",
       "      <td>4</td>\n",
       "      <td>12.560244</td>\n",
       "      <td>4.564348</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>1104</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>3.178054</td>\n",
       "      <td>7.350449</td>\n",
       "      <td>8.209335</td>\n",
       "      <td>0.135124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "      Price  Area  Room  log(Price)  log(Area)  log(Room)  Zip_num  Region  \\\n",
       "0  425000.0    68     3   12.959844   4.219508   1.098612     1098       0   \n",
       "1  285000.0    96     4   12.560244   4.564348   1.386294     1104       1   \n",
       "\n",
       "   Location  District   AR-ratio  log(AR-ratio)  Euclidean_distance  \\\n",
       "0         9         8  22.666667       3.120895            3.606198   \n",
       "1         0         4  24.000000       3.178054            7.350449   \n",
       "\n",
       "   Haversine_distance  Manhattan_distance  \n",
       "0            4.174069            0.071974  \n",
       "1            8.209335            0.135124  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation data head: None\n"
     ]
    }
   ],
   "source": [
    "# Load train and validation set\n",
    "\n",
    "data_train = (\n",
    "    pd.read_parquet(DATA_TRAIN_PATH)\n",
    "    # drop Address and Zipcode as they are not useful\n",
    "    .drop(columns=[\"Address\", \"Zip\"])\n",
    "    # Drop Lat and Lon\n",
    "    .drop(columns=[\"Lat\", \"Lon\"])\n",
    "    # Drop Delivery Area as it contains too many categories\n",
    "    .drop(columns=[\"Delivery_area\"])\n",
    "    # Discard all rows with values Price > 2M\n",
    "    .loc[lambda x: x.Price < 2e6]\n",
    "    # Double check nan values as sklearn does not like them\n",
    "    .pipe(chain_snap, fn=lambda x: x.shape, msg=\"• Size dataframe\")\n",
    "    .pipe(\n",
    "         chain_snap, fn=lambda x: x.isna().sum().sum(), msg=\"• Count NaNs post processing\"\n",
    "    )\n",
    "    .pipe(chain_snap, fn=lambda x: display(x.head(2)), msg=\"• Train data head\")\n",
    ")\n",
    "\n",
    "data_val = (\n",
    "    pd.read_parquet(DATA_VAL_PATH)\n",
    "    # drop Address and Zipcode as they are not useful\n",
    "    .drop(columns=[\"Address\", \"Zip\"])\n",
    "    # Drop Lat and Lon\n",
    "    .drop(columns=[\"Lat\", \"Lon\"])\n",
    "    # Drop Delivery Area as it contains too many categories\n",
    "    .drop(columns=[\"Delivery_area\"])\n",
    "    # Discard all rows with values Price > 2M\n",
    "    .loc[lambda x: x.Price < 2e6]\n",
    "    # Double check nan values as sklearn does not like them\n",
    "    .pipe(\n",
    "         chain_snap, fn=lambda x: x.isna().sum().sum(), msg=\"Count NaNs post processing\"\n",
    "    )\n",
    "    .pipe(chain_snap, fn=lambda x: x.shape, msg=\"Size dataframe\")\n",
    "    .pipe(chain_snap, fn=lambda x: display(x.head(2)), msg=\"Validation data head\")\n",
    ")\n",
    "\n",
    "# let's consider Price and log(Price) as target. Depending on the model we will use one or the other\n",
    "TARGET = [\"Price\", \"log(Price)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split train and validation set into X and Y: Note that Y_train and Y_val contrains both Price and log(Price)\n",
    "X_train, Y_train = data_train.drop(TARGET, axis=1), data_train[TARGET]\n",
    "X_val, Y_val = data_val.drop(TARGET, axis=1), data_val[TARGET]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Discard or fill in missing values using [imputation techniques](https://www.kaggle.com/code/residentmario/simple-techniques-for-missing-data-imputation), e.g., using the median value**\n",
    "\n",
    "🙋‍♀️ scikit-learn will raise an error during training if the data contains Nan values;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Small proportion of missing data (0.03%) has been observed in target variable (i.e, Price), since it was dropped previously, therefore no need to proceed this step. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "• No missing values in train dataset\n",
      "• No missing values in validation dataset\n"
     ]
    }
   ],
   "source": [
    "if len(data_train)==len(data_train.dropna()):\n",
    "    print('• No missing values in train dataset')\n",
    "\n",
    "if len(data_val)==len(data_val.dropna()):\n",
    "    print('• No missing values in validation dataset')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Select a [cross-validation schema](https://scikit-learn.org/stable/modules/cross_validation.html) and an [evaluation metric](https://scikit-learn.org/stable/modules/model_evaluation.html) to be used for the hyperparameter tuning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set parameters for grid search\n",
    "scoring = (\n",
    "    \"r2\"  # type of metrics used to select the best model during hyperparameters tuning\n",
    ")\n",
    "cv = 5  # 5-fold cross-validation\n",
    "\n",
    "# Initialize empty dictionary and dataframe to store models and results, respectively\n",
    "ml_models, df_report_all = {}, pd.DataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4. Select features that have shown a reasonable predictive power with the target variable**\t\n",
    "\n",
    "💁‍♂️ You can further reduce the number of features by applying [feature selection techniques](https://neptune.ai/blog/feature-selection-methods), such as wrapper and embedded methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* First, using a wrapper method for feature selection:\n",
    "    \n",
    "    - Recursive feature elimination (REF):  this wrapper method returns features based on feature ranking with recursive feature elimination. The method itself utilises external estimator that assigns weights to the features. To do so, the estimator is trained on all features and each feature's importance is obtained through coeff_ or feature_importances_attribute. The least important feature is removed and this procedure is repeated until the desired number of features are selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "• RFE wrapper method suggested the best features are: 'Room', 'log(Area)', 'log(Room)', 'Region', 'Location', 'District', 'log(AR-ratio)', 'Euclidean_distance', 'Haversine_distance', 'Manhattan_distance'\n"
     ]
    }
   ],
   "source": [
    "# Adopting the wrapper method for feature selection\n",
    "from sklearn.feature_selection import RFE #,RFECV\n",
    "svr = SVR(kernel='linear')\n",
    "rfe = RFE(svr, n_features_to_select=0.8, step=1)\n",
    "# rfe = RFECV(svr, step = 1 , cv = 5, scoring = 'r2')\n",
    "rfe.fit(X_train, Y_train['log(Price)'])\n",
    "X_selection_train = rfe.transform(X_train)\n",
    "\n",
    "#returning the best performing features:\n",
    "X_train_wrapper = X_train.columns[rfe.support_].tolist()\n",
    "#returning the best performing features across train and validation dataset:\n",
    "print(f\"• RFE wrapper method suggested the best features are: {str(X_train_wrapper)[1:-1]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Results from RFE indicated consistent results from previous workbooks: \n",
    "    - Regarding selected numeric features, all highly correlated features to target variable is maintained. Similarly, we will only select `Manhattan_distance` among all tranformed distance variable, based on its shows strongest correclation to target \n",
    "    - Regarding nominal (categorical) features, `['Region', 'Location', 'District']` were selected. However, we learned that `District` has displayed small variation on target variable, and when including it, most of the groups are not exhibiting statsitically significant effect on `log(Price)`. Therefore, it will be discarded when training the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get subset of features coming from previous analysis to be used for model training\n",
    "numerical_features = [\"log(Area)\", \"log(Room)\", \"log(AR-ratio)\", \"Manhattan_distance\"]\n",
    "nominal_features = [\"Region\", \"Location\"]\n",
    "target = \"log(Price)\"\n",
    "\n",
    "# subset original dataframe to keep only the features we want to use\n",
    "X_train_sk, y_train_sk = X_train[numerical_features + nominal_features], Y_train[target]\n",
    "X_val_sk, y_val_sk = X_val[numerical_features + nominal_features], Y_val[target]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. Select the type of machine learning model and identify a hyperparameter set you want to tune during the learning process.** \n",
    "\n",
    "Check this [link](https://scikit-learn.org/stable/supervised_learning.html) for a complete list of models available on scikit-learn.\n",
    "\n",
    "⭐️ It is recommended to train a [sklearn pipeline](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html) which includes the data transformations such as [OneHotEncoding](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html) and [StandardScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) combined with the model. In this way, you will significantly reduce bugs and simplify the code during the deployment phase.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Train regularized Linear Regression**\n",
    "\n",
    "Train regularized version of Linear Regression, namely: [*Ridge*](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html), [*Lasso*](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html) and [*ElasticNet*](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html) regression. \n",
    "\n",
    "Since they are an extension of Linear Regression, one should still comply with the model assumption, e.g. normal distribution. Since these models can carry out feature selection (embedded methods), we can relax the condition of having a feature set with a VIF score > 5 and let the model decide the optimal weights for each feature. \n",
    "\n",
    "<!-- 💡 The code below has been written in such a way that one can select among the three types of model above mentioned. -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Option 1: Ridge, Option 2: Lasso, Option 3: ElasticNet\n",
    "model_type = \"ElasticNet\"\n",
    "\n",
    "# Set hyperparameter range\n",
    "n_alphas = 100\n",
    "alpha_range = np.linspace(1e-3, 25, n_alphas)\n",
    "\n",
    "# Select model type\n",
    "if model_type == \"Ridge\":\n",
    "    model = Ridge(fit_intercept=True)\n",
    "elif model_type == \"Lasso\":\n",
    "    model = Lasso(fit_intercept=True)\n",
    "elif model_type == \"ElasticNet\":\n",
    "    model = ElasticNet(fit_intercept=True)\n",
    "else:\n",
    "    raise ValueError(\"Model type not supported\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;num_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;,\n",
       "                                                   &#x27;log(AR-ratio)&#x27;,\n",
       "                                                   &#x27;Manhattan_distance&#x27;]),\n",
       "                                                 (&#x27;nom&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;nom_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;nominal_encoder&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                                  [&#x27;Region&#x27;, &#x27;Location&#x27;])])),\n",
       "                (&#x27;ElasticNet&#x27;, ElasticNet())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;num_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;,\n",
       "                                                   &#x27;log(AR-ratio)&#x27;,\n",
       "                                                   &#x27;Manhattan_distance&#x27;]),\n",
       "                                                 (&#x27;nom&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;nom_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;nominal_encoder&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                                  [&#x27;Region&#x27;, &#x27;Location&#x27;])])),\n",
       "                (&#x27;ElasticNet&#x27;, ElasticNet())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;num_imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;, &#x27;log(AR-ratio)&#x27;,\n",
       "                                  &#x27;Manhattan_distance&#x27;]),\n",
       "                                (&#x27;nom&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;nom_imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;nominal_encoder&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                 [&#x27;Region&#x27;, &#x27;Location&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;, &#x27;log(AR-ratio)&#x27;, &#x27;Manhattan_distance&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nom</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Region&#x27;, &#x27;Location&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ElasticNet</label><div class=\"sk-toggleable__content\"><pre>ElasticNet()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('num_imputer',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['log(Area)', 'log(Room)',\n",
       "                                                   'log(AR-ratio)',\n",
       "                                                   'Manhattan_distance']),\n",
       "                                                 ('nom',\n",
       "                                                  Pipeline(steps=[('nom_imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('nominal_encoder',\n",
       "                                                                   OneHotEncoder(drop='first'))]),\n",
       "                                                  ['Region', 'Location'])])),\n",
       "                ('ElasticNet', ElasticNet())])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Construct the ml pipeline\n",
    "ml_candidate_lr_reg = Pipeline(\n",
    "    steps=[\n",
    "        (\"preprocessor\", construct_preprocessor(numerical_features, nominal_features)),\n",
    "        (model_type, model),\n",
    "    ]\n",
    ")\n",
    "\n",
    "display(ml_candidate_lr_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6. Train the model in cross-validation while optimising the hyperparameters.** \n",
    "\n",
    "If the optimal value of a hyperparameter is at the edge of the selected range, enlarge the range and newly train the model. For this task, we will use [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html). \n",
    "\n",
    "* Note: When dealing with a large sample size, one should consider using [RandomizedSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: ElasticNet__alpha=0.001 is on the minimum edge of the param_grid. Please Enlarge the param_grid of this parameter.\n"
     ]
    }
   ],
   "source": [
    "param_grid = {f\"{model_type}__alpha\": alpha_range}\n",
    "\n",
    "# Construct the grid search\n",
    "gridCV = GridSearchCV(\n",
    "    estimator=ml_candidate_lr_reg,  # model to train\n",
    "    param_grid=param_grid,          # hyperparameters to tune\n",
    "    n_jobs=-1,                      # use all cores\n",
    "    cv=cv,\n",
    "    verbose=0,\n",
    "    refit=True,                     # refit the best model on the whole dataset\n",
    "    scoring=scoring,                # metrics to optimize\n",
    ")\n",
    "\n",
    "# Train the model and optimize for the best alpha\n",
    "_ = gridCV.fit(X_train_sk, y_train_sk)\n",
    "\n",
    "# Save the best alpha (hyperparameter)\n",
    "best_alpha = gridCV.best_params_[f\"{model_type}__alpha\"]\n",
    "\n",
    "# check grid search hyperparameters if they are in the range limit\n",
    "check_grid_search_hyperparameters(gridCV, param_grid)\n",
    "\n",
    "# Store the trained model with hyperparameter tuned in the dictionary\n",
    "ml_models[model_type] = gridCV.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Due to convergency issue, Lasso and ElasticNet cannot have alpha < 0.001 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**7. Study the impact of the hyperparameters on the model performance and, if the model allows, assess the importance of each feature.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize a dataframe to store the coefficients of the model for each alpha\n",
    "df_alphas = pd.DataFrame(\n",
    "    index=alpha_range,\n",
    "    columns=numerical_features\n",
    "    + list(\n",
    "        gridCV.best_estimator_[\"preprocessor\"]\n",
    "        .transformers_[1][1][\"nominal_encoder\"]\n",
    "        .get_feature_names_out(input_features=nominal_features)\n",
    "    ),\n",
    ")\n",
    "\n",
    "# Loop over the alphas and store the coefficients of the model for each alpha\n",
    "for a in alpha_range:\n",
    "    ml_candidate_lr_reg[model_type].set_params(alpha=a)\n",
    "    ml_candidate_lr_reg.fit(X_train_sk, y_train_sk)\n",
    "\n",
    "    # store the coefficients of the model for each alpha\n",
    "    df_alphas.loc[a] = ml_candidate_lr_reg[model_type].coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n",
    "\n",
    "df_alphas.loc[best_alpha, :].sort_values().plot(\n",
    "    kind=\"barh\",\n",
    "    color='maroon',\n",
    "    title=f\"Feature Importance {model_type} best alpha = {best_alpha:.3f}\",\n",
    "    ax=axs[0],\n",
    ")\n",
    "\n",
    "sns.lineplot(\n",
    "    data=df_alphas,\n",
    "    ax=axs[1],\n",
    "    linewidth=2,\n",
    ")\n",
    "\n",
    "# set x-axis to log scale\n",
    "axs[1].set_xscale(\"log\")\n",
    "\n",
    "# add vertical line to indicate the best alpha\n",
    "plt.axvline(\n",
    "    best_alpha,\n",
    "    linestyle=\":\",\n",
    "    color='r',\n",
    "    linewidth=3,\n",
    "    label=\"Best alpha\",\n",
    ")\n",
    "\n",
    "axs[1].set_xlabel(\"alpha\")\n",
    "axs[1].set_ylabel(\"Standardized Coefficients\")\n",
    "axs[1].set_title(f\"{model_type} coefficients as a function of alpha\")\n",
    "\n",
    "# # move legend outside the plot\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the figure generated above as png file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create image folder if not exists\n",
    "\n",
    "if not os.path.exists(\"Image\"):\n",
    "    os.makedirs(\"Image\")\n",
    "\n",
    "# save the figure\n",
    "fig.savefig(f\"Image/{model_type}_coefficients.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8. Measure the model performance and keep in memory the trained models.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ML model type:  ElasticNet\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys(['Ridge', 'Lasso', 'ElasticNet'])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The model has already been stored in the dictionary ml_models\n",
    "print(\"ML model type: \", model_type)\n",
    "ml_models.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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></th>\n",
       "      <th>in-sample</th>\n",
       "      <th>out-of-sample</th>\n",
       "      <th>Difference (%)</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th>metrics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">ElasticNet (&lt;2M)</th>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.839</td>\n",
       "      <td>0.776</td>\n",
       "      <td>-8.118557</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.841</td>\n",
       "      <td>0.783</td>\n",
       "      <td>-7.407407</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>128015.560</td>\n",
       "      <td>160847.612</td>\n",
       "      <td>20.411899</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>76295.585</td>\n",
       "      <td>94241.836</td>\n",
       "      <td>19.042765</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max error</th>\n",
       "      <td>936922.104</td>\n",
       "      <td>1026379.548</td>\n",
       "      <td>8.715825</td>\n",
       "      <td>The maximum residual error</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      in-sample  out-of-sample  \\\n",
       "model            metrics                                         \n",
       "ElasticNet (<2M) r-squared                0.839          0.776   \n",
       "                 explained variance       0.841          0.783   \n",
       "                 RMSE                128015.560     160847.612   \n",
       "                 MAE                  76295.585      94241.836   \n",
       "                 max error           936922.104    1026379.548   \n",
       "\n",
       "                                     Difference (%)  \\\n",
       "model            metrics                              \n",
       "ElasticNet (<2M) r-squared                -8.118557   \n",
       "                 explained variance       -7.407407   \n",
       "                 RMSE                     20.411899   \n",
       "                 MAE                      19.042765   \n",
       "                 max error                 8.715825   \n",
       "\n",
       "                                                        Description  \n",
       "model            metrics                                             \n",
       "ElasticNet (<2M) r-squared             Coefficient of Determination  \n",
       "                 explained variance        Explained Variance Score  \n",
       "                 RMSE                Root Mean Squared Error (RMSE)  \n",
       "                 MAE                      Mean Absolute Error (MAE)  \n",
       "                 max error               The maximum residual error  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evalutate model performance in train and validation set\n",
    "y_fit = np.exp(gridCV.best_estimator_.predict(X_train_sk))\n",
    "y_pred = np.exp(gridCV.best_estimator_.predict(X_val_sk))\n",
    "\n",
    "# create dataframe with regression report\n",
    "df_report = regression_report_in_sample_out_of_sample(\n",
    "    y_train=np.exp(y_train_sk),\n",
    "    y_fit=y_fit,\n",
    "    y_test=np.exp(y_val_sk),\n",
    "    y_pred=y_pred,\n",
    "    model_name=f\"{model_type} (<2M)\",\n",
    ")\n",
    "\n",
    "# display report\n",
    "display(df_report)\n",
    "\n",
    "# append to dataframe\n",
    "df_report_all = pd.concat([df_report_all, df_report])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**9. Train several models by repeating the abovementioned procedure**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A. Train a Support Vector Machine (SVM)**\n",
    "\n",
    "* Train [Support Vector Machine](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html):\n",
    "\n",
    "    * As in the previous case, we can relax the condition of having a feature set with a VIF score > 5 and let the model decide the optimal weights for each feature via its regularization term `C`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get subset of features to be used in the model\n",
    "numerical_features = [\"log(Area)\", \"log(Room)\", \"log(AR-ratio)\", \"Manhattan_distance\"]\n",
    "nominal_features = [\"Region\", \"Location\"]\n",
    "target = \"log(Price)\"\n",
    "\n",
    "# subset original dataframe to keep only the features we want to use\n",
    "X_train_sk, y_train_sk = X_train[numerical_features + nominal_features], Y_train[target]\n",
    "X_val_sk, y_val_sk = X_val[numerical_features + nominal_features], Y_val[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;num_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;,\n",
       "                                                   &#x27;log(AR-ratio)&#x27;,\n",
       "                                                   &#x27;Manhattan_distance&#x27;]),\n",
       "                                                 (&#x27;nom&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;nom_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;nominal_encoder&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                                  [&#x27;Region&#x27;, &#x27;Location&#x27;])])),\n",
       "                (&#x27;SVM&#x27;, SVR())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;num_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;,\n",
       "                                                   &#x27;log(AR-ratio)&#x27;,\n",
       "                                                   &#x27;Manhattan_distance&#x27;]),\n",
       "                                                 (&#x27;nom&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;nom_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;nominal_encoder&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                                  [&#x27;Region&#x27;, &#x27;Location&#x27;])])),\n",
       "                (&#x27;SVM&#x27;, SVR())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;num_imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;, &#x27;log(AR-ratio)&#x27;,\n",
       "                                  &#x27;Manhattan_distance&#x27;]),\n",
       "                                (&#x27;nom&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;nom_imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;nominal_encoder&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                 [&#x27;Region&#x27;, &#x27;Location&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;, &#x27;log(AR-ratio)&#x27;, &#x27;Manhattan_distance&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nom</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Region&#x27;, &#x27;Location&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('num_imputer',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['log(Area)', 'log(Room)',\n",
       "                                                   'log(AR-ratio)',\n",
       "                                                   'Manhattan_distance']),\n",
       "                                                 ('nom',\n",
       "                                                  Pipeline(steps=[('nom_imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('nominal_encoder',\n",
       "                                                                   OneHotEncoder(drop='first'))]),\n",
       "                                                  ['Region', 'Location'])])),\n",
       "                ('SVM', SVR())])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct a sklearn pipeline\n",
    "model_type = \"SVM\"\n",
    "ml_candidate_SVM = Pipeline(\n",
    "    steps=[\n",
    "        (\"preprocessor\", construct_preprocessor(numerical_features, nominal_features)),\n",
    "        (model_type, SVR(kernel=\"rbf\")),  # fix type of Kernel\n",
    "    ]\n",
    ")\n",
    "\n",
    "# visualize the pipeline\n",
    "ml_candidate_SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set hyperparameter range\n",
    "param_grid = {\n",
    "    f\"{model_type}__C\": np.linspace(0.01, 1, 20, endpoint=True),\n",
    "}\n",
    "\n",
    "# Construct the grid search\n",
    "gridCV = GridSearchCV(\n",
    "    estimator=ml_candidate_SVM,  # model to train\n",
    "    param_grid=param_grid,  # hyperparameters to tune\n",
    "    n_jobs=-1,  # use all cores\n",
    "    cv=cv,\n",
    "    verbose=0,\n",
    "    refit=True,  # refit the best model on the whole dataset\n",
    "    scoring=scoring,  # metrics to optimize\n",
    ")\n",
    "\n",
    "# Train the model and optimize for the best hyperparameter\n",
    "_ = gridCV.fit(X_train_sk, y_train_sk)\n",
    "\n",
    "# check grid search hyperparameters if they are in the range limit\n",
    "check_grid_search_hyperparameters(gridCV, param_grid)\n",
    "\n",
    "# Store the trained model with hyperparameter tuned in the dictionary\n",
    "ml_models[model_type] = gridCV.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot mean_crossval_score vs hyperparameter with std_test_score as error bar\n",
    "best_index = np.argmax(gridCV.cv_results_[\"mean_test_score\"])\n",
    "\n",
    "fig, axs = plt.subplots(nrows=1, ncols=1, figsize=(7, 3))\n",
    "\n",
    "axs.errorbar(\n",
    "    list(gridCV.cv_results_[f\"param_{model_type}__C\"]),\n",
    "    list(gridCV.cv_results_[\"mean_test_score\"]),\n",
    "    yerr=gridCV.cv_results_[\"std_test_score\"],\n",
    "    fmt=\"o\",\n",
    "    label=scoring,\n",
    ")\n",
    "\n",
    "axs.errorbar(\n",
    "    list(gridCV.cv_results_[f\"param_{model_type}__C\"])[best_index],\n",
    "    list(gridCV.cv_results_[\"mean_test_score\"])[best_index],\n",
    "    yerr=gridCV.cv_results_[\"std_test_score\"][best_index],\n",
    "    fmt=\"o\",\n",
    "    color=\"red\",\n",
    "    label=f\"best {scoring}\",\n",
    ")\n",
    "\n",
    "axs.set_xlabel(\"C\")\n",
    "axs.set_ylabel(scoring)\n",
    "axs.set_title(f\"Grid search {model_type} with {scoring} as metric\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "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",
       "    }\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></th>\n",
       "      <th>in-sample</th>\n",
       "      <th>out-of-sample</th>\n",
       "      <th>Difference (%)</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th>metrics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">SVM (&lt;2M)</th>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.888</td>\n",
       "      <td>0.767</td>\n",
       "      <td>-15.775750</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.889</td>\n",
       "      <td>0.775</td>\n",
       "      <td>-14.709677</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>106966.148</td>\n",
       "      <td>164174.974</td>\n",
       "      <td>34.846253</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>62828.520</td>\n",
       "      <td>94789.639</td>\n",
       "      <td>33.717946</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max error</th>\n",
       "      <td>838421.432</td>\n",
       "      <td>958934.445</td>\n",
       "      <td>12.567388</td>\n",
       "      <td>The maximum residual error</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               in-sample  out-of-sample  Difference (%)  \\\n",
       "model     metrics                                                         \n",
       "SVM (<2M) r-squared                0.888          0.767      -15.775750   \n",
       "          explained variance       0.889          0.775      -14.709677   \n",
       "          RMSE                106966.148     164174.974       34.846253   \n",
       "          MAE                  62828.520      94789.639       33.717946   \n",
       "          max error           838421.432     958934.445       12.567388   \n",
       "\n",
       "                                                 Description  \n",
       "model     metrics                                             \n",
       "SVM (<2M) r-squared             Coefficient of Determination  \n",
       "          explained variance        Explained Variance Score  \n",
       "          RMSE                Root Mean Squared Error (RMSE)  \n",
       "          MAE                      Mean Absolute Error (MAE)  \n",
       "          max error               The maximum residual error  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evalutate model performance in train and validation set\n",
    "y_fit = np.exp(gridCV.best_estimator_.predict(X_train_sk))\n",
    "y_pred = np.exp(gridCV.best_estimator_.predict(X_val_sk))\n",
    "\n",
    "df_report = regression_report_in_sample_out_of_sample(\n",
    "    y_train=np.exp(y_train_sk),\n",
    "    y_fit=y_fit,\n",
    "    y_test=np.exp(y_val_sk),\n",
    "    y_pred=y_pred,\n",
    "    model_name=f\"{model_type} (<2M)\",\n",
    ")\n",
    "\n",
    "# display report\n",
    "display(df_report)\n",
    "\n",
    "# append to dataframe\n",
    "df_report_all = pd.concat([df_report_all, df_report])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**B. Train a regression model with ensemble methods**\n",
    "\n",
    "* Train a regression model with ensemble methods:\n",
    "    - [*Random Forest*](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor)/[Gradient Boosting Regressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor): \n",
    "        * As in the previous case, we can relax the condition of having a feature set with a VIF score > 5 and let the model decide the optimal weights for each feature via its regularization term `max_depth`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get subset of features to be used in the model\n",
    "numerical_features = [\"log(Area)\", \"log(Room)\", \"log(AR-ratio)\", \"Manhattan_distance\"]\n",
    "nominal_features = [\"Region\", \"Location\"]\n",
    "target = \"log(Price)\"\n",
    "\n",
    "# subset original dataframe to keep only the features we want to use\n",
    "X_train_sk, y_train_sk = X_train[numerical_features + nominal_features], Y_train[target]\n",
    "X_val_sk, y_val_sk = X_val[numerical_features + nominal_features], Y_val[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Option 1: RandomForest, Option 2: GradientBoosting\n",
    "model_type = \"GradientBoosting\"\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators_range= np.arange(1,30,3) #np.linspace(1, 100, num = 10, dtype=int)\n",
    "\n",
    "# # Number of features to consider at every split\n",
    "# max_features = ['auto', 'sqrt']\n",
    "\n",
    "# Maximum number of levels in tree\n",
    "max_depth_range= np.arange(1,50,5) #[int(x) for x in np.linspace(1, 200, num = 15)] #\n",
    "\n",
    "# Select model type\n",
    "if model_type == \"RandomForest\":\n",
    "    model = RandomForestRegressor()\n",
    "# elif model_type == \"AdaBoost\":\n",
    "#     model = AdaBoostRegressor()\n",
    "elif model_type == \"GradientBoosting\":\n",
    "    model = GradientBoostingRegressor()\n",
    "else:\n",
    "    raise ValueError(\"Model type not supported\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;num_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;,\n",
       "                                                   &#x27;log(AR-ratio)&#x27;,\n",
       "                                                   &#x27;Manhattan_distance&#x27;]),\n",
       "                                                 (&#x27;nom&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;nom_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;nominal_encoder&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                                  [&#x27;Region&#x27;, &#x27;Location&#x27;])])),\n",
       "                (&#x27;GradientBoosting&#x27;, GradientBoostingRegressor())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;num_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;,\n",
       "                                                   &#x27;log(AR-ratio)&#x27;,\n",
       "                                                   &#x27;Manhattan_distance&#x27;]),\n",
       "                                                 (&#x27;nom&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;nom_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;nominal_encoder&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                                  [&#x27;Region&#x27;, &#x27;Location&#x27;])])),\n",
       "                (&#x27;GradientBoosting&#x27;, GradientBoostingRegressor())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;num_imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;, &#x27;log(AR-ratio)&#x27;,\n",
       "                                  &#x27;Manhattan_distance&#x27;]),\n",
       "                                (&#x27;nom&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;nom_imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;nominal_encoder&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                 [&#x27;Region&#x27;, &#x27;Location&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;, &#x27;log(AR-ratio)&#x27;, &#x27;Manhattan_distance&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-50\" type=\"checkbox\" ><label for=\"sk-estimator-id-50\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-51\" type=\"checkbox\" ><label for=\"sk-estimator-id-51\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nom</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Region&#x27;, &#x27;Location&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-52\" type=\"checkbox\" ><label for=\"sk-estimator-id-52\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-53\" type=\"checkbox\" ><label for=\"sk-estimator-id-53\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-54\" type=\"checkbox\" ><label for=\"sk-estimator-id-54\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('num_imputer',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['log(Area)', 'log(Room)',\n",
       "                                                   'log(AR-ratio)',\n",
       "                                                   'Manhattan_distance']),\n",
       "                                                 ('nom',\n",
       "                                                  Pipeline(steps=[('nom_imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('nominal_encoder',\n",
       "                                                                   OneHotEncoder(drop='first'))]),\n",
       "                                                  ['Region', 'Location'])])),\n",
       "                ('GradientBoosting', GradientBoostingRegressor())])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Construct the ml pipeline\n",
    "ml_candidate_em = Pipeline(\n",
    "    steps=[\n",
    "        (\"preprocessor\", construct_preprocessor(numerical_features, nominal_features)),\n",
    "        (model_type, model),\n",
    "    ]\n",
    ")\n",
    "\n",
    "display(ml_candidate_em)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {f\"{model_type}__max_depth\": max_depth_range,\n",
    "              f\"{model_type}__n_estimators\": n_estimators_range}\n",
    "\n",
    "# Construct the grid search\n",
    "gridCV = GridSearchCV(\n",
    "    estimator=ml_candidate_em,  # model to train\n",
    "    param_grid=param_grid,      # hyperparameters to tune\n",
    "    n_jobs=-1,                  # use all cores\n",
    "    cv=cv,\n",
    "    verbose=0,\n",
    "    refit=True,                 # refit the best model on the whole dataset\n",
    "    scoring=scoring,            # metrics to optimize\n",
    ")\n",
    "\n",
    "# Train the model and optimize for the best alpha\n",
    "_ = gridCV.fit(X_train_sk, y_train_sk)\n",
    "\n",
    "# Save the best hyperparameter\n",
    "best_hyperparameter= gridCV.best_params_\n",
    "\n",
    "# check grid search hyperparameters if they are in the range limit\n",
    "check_grid_search_hyperparameters(gridCV, param_grid)\n",
    "\n",
    "# Store the trained model with hyperparameter tuned in the dictionary\n",
    "ml_models[model_type] = gridCV.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reshape the mean test scores to match the grid search dimensions\n",
    "scores_matrix = gridCV.cv_results_[\"mean_test_score\"].reshape(len(max_depth_range), len(n_estimators_range))\n",
    "plt.figure(figsize=(30, 10)) \n",
    "# visualise the heatmap\n",
    "sns.heatmap(scores_matrix,\n",
    "            xticklabels=n_estimators_range,\n",
    "            yticklabels=max_depth_range,\n",
    "            annot=True,\n",
    "            fmt='.3f',\n",
    "            cmap='Blues',\n",
    "            cbar=False,\n",
    "            annot_kws={\"fontsize\": 6.5},\n",
    "            # linewidths=0.5\n",
    "            )\n",
    "\n",
    "# Adding axis titles\n",
    "plt.xlabel('Number of Estimators')\n",
    "plt.ylabel('Max Depth')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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></th>\n",
       "      <th>in-sample</th>\n",
       "      <th>out-of-sample</th>\n",
       "      <th>Difference (%)</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th>metrics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">GradientBoosting (&lt;2M)</th>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.958</td>\n",
       "      <td>0.757</td>\n",
       "      <td>-26.552180</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.959</td>\n",
       "      <td>0.765</td>\n",
       "      <td>-25.359477</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>65764.343</td>\n",
       "      <td>167425.234</td>\n",
       "      <td>60.720173</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>39991.850</td>\n",
       "      <td>101810.207</td>\n",
       "      <td>60.719214</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max error</th>\n",
       "      <td>412316.707</td>\n",
       "      <td>912993.378</td>\n",
       "      <td>54.839025</td>\n",
       "      <td>The maximum residual error</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            in-sample  out-of-sample  \\\n",
       "model                  metrics                                         \n",
       "GradientBoosting (<2M) r-squared                0.958          0.757   \n",
       "                       explained variance       0.959          0.765   \n",
       "                       RMSE                 65764.343     167425.234   \n",
       "                       MAE                  39991.850     101810.207   \n",
       "                       max error           412316.707     912993.378   \n",
       "\n",
       "                                           Difference (%)  \\\n",
       "model                  metrics                              \n",
       "GradientBoosting (<2M) r-squared               -26.552180   \n",
       "                       explained variance      -25.359477   \n",
       "                       RMSE                     60.720173   \n",
       "                       MAE                      60.719214   \n",
       "                       max error                54.839025   \n",
       "\n",
       "                                                              Description  \n",
       "model                  metrics                                             \n",
       "GradientBoosting (<2M) r-squared             Coefficient of Determination  \n",
       "                       explained variance        Explained Variance Score  \n",
       "                       RMSE                Root Mean Squared Error (RMSE)  \n",
       "                       MAE                      Mean Absolute Error (MAE)  \n",
       "                       max error               The maximum residual error  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evalutate model performance in train and validation set\n",
    "y_fit = np.exp(gridCV.best_estimator_.predict(X_train_sk))\n",
    "y_pred = np.exp(gridCV.best_estimator_.predict(X_val_sk))\n",
    "\n",
    "df_report = regression_report_in_sample_out_of_sample(\n",
    "    y_train=np.exp(y_train_sk),\n",
    "    y_fit=y_fit,\n",
    "    y_test=np.exp(y_val_sk),\n",
    "    y_pred=y_pred,\n",
    "    model_name=f\"{model_type} (<2M)\",\n",
    ")\n",
    "\n",
    "# display report\n",
    "display(df_report)\n",
    "\n",
    "# append to dataframe\n",
    "df_report_all = pd.concat([df_report_all, df_report])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time to compute the importances: 0.164 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x300 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.inspection import permutation_importance\n",
    "import time\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "result = permutation_importance(\n",
    "    gridCV, X_train_sk, y_train_sk, n_repeats=10, random_state=42, n_jobs=-1\n",
    ")\n",
    "elapsed_time = time.time() - start_time\n",
    "print(f\"Elapsed time to compute the importances: {elapsed_time:.3f} seconds\")\n",
    "\n",
    "forest_importances = pd.Series(result.importances_mean, index=X_train_sk.columns)\n",
    "sorted_importances_rf = forest_importances.sort_values(ascending=False)\n",
    "\n",
    "plt.figure(figsize=(8, 3)) \n",
    "fig, ax = plt.subplots()\n",
    "sorted_importances_rf.plot.bar(yerr=result.importances_std, ax=ax)\n",
    "plt.xticks(rotation=30)\n",
    "ax.set_title(\"Feature importances using permutation on full model\")\n",
    "ax.set_ylabel(\"Mean accuracy decrease\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**10. compare their performance and pick the best-performing model (don't forget to compare them models against the baseline model).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "• Loading baseline performance file from: \n",
      "  artifacts/2024-01-11-08-37-12_baseline_model/baseline_model_performance.csv\n"
     ]
    }
   ],
   "source": [
    "# Get list of folder in artifacts\n",
    "artifacts_filelist = os.listdir(\"artifacts\")\n",
    "\n",
    "# Select the folder name in list which contains the string \"baseline\"\n",
    "baseline_path = [s for s in artifacts_filelist if \"baseline\" in s][0]\n",
    "\n",
    "baseline_filelist = os.listdir(os.path.join(\"artifacts\", baseline_path))\n",
    "# Select the file name in list which contains the string \"performance\"\n",
    "baseline_performance_file = [s for s in baseline_filelist if \"performance\" in s][0]\n",
    "\n",
    "performance_file = os.path.join(\"artifacts\", baseline_path, baseline_performance_file)\n",
    "\n",
    "print(\"• Loading baseline performance file from: \\n \", performance_file)\n",
    "# Load csv file. Note. We apply reset_index().set_index() to be able to merge the dataframe afterwards\n",
    "df_baseline = (\n",
    "    pd.read_csv(performance_file, index_col=0)\n",
    "    .reset_index()\n",
    "    .set_index([\"model\", \"metrics\"])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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></th>\n",
       "      <th>in-sample</th>\n",
       "      <th>out-of-sample</th>\n",
       "      <th>Difference (%)</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th>metrics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">OLS (&lt;2M)</th>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.786</td>\n",
       "      <td>0.716</td>\n",
       "      <td>-9.776536</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.789</td>\n",
       "      <td>0.729</td>\n",
       "      <td>-8.230453</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>147571.708</td>\n",
       "      <td>181023.072</td>\n",
       "      <td>18.479061</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>91023.514</td>\n",
       "      <td>111734.697</td>\n",
       "      <td>18.536035</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max error</th>\n",
       "      <td>897621.898</td>\n",
       "      <td>989269.060</td>\n",
       "      <td>9.264129</td>\n",
       "      <td>The maximum residual error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.786</td>\n",
       "      <td>0.716</td>\n",
       "      <td>-9.776536</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.789</td>\n",
       "      <td>0.729</td>\n",
       "      <td>-8.230453</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>147571.708</td>\n",
       "      <td>181023.072</td>\n",
       "      <td>18.479061</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>91023.514</td>\n",
       "      <td>111734.697</td>\n",
       "      <td>18.536035</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max error</th>\n",
       "      <td>897621.898</td>\n",
       "      <td>989269.060</td>\n",
       "      <td>9.264129</td>\n",
       "      <td>The maximum residual error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.786</td>\n",
       "      <td>0.716</td>\n",
       "      <td>-9.776536</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.789</td>\n",
       "      <td>0.729</td>\n",
       "      <td>-8.230453</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>147571.708</td>\n",
       "      <td>181023.072</td>\n",
       "      <td>18.479061</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>91023.514</td>\n",
       "      <td>111734.697</td>\n",
       "      <td>18.536035</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max error</th>\n",
       "      <td>897621.898</td>\n",
       "      <td>989269.060</td>\n",
       "      <td>9.264129</td>\n",
       "      <td>The maximum residual error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.786</td>\n",
       "      <td>0.716</td>\n",
       "      <td>-9.776536</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.789</td>\n",
       "      <td>0.729</td>\n",
       "      <td>-8.230453</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>147571.708</td>\n",
       "      <td>181023.072</td>\n",
       "      <td>18.479061</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>91023.514</td>\n",
       "      <td>111734.697</td>\n",
       "      <td>18.536035</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max error</th>\n",
       "      <td>897621.898</td>\n",
       "      <td>989269.060</td>\n",
       "      <td>9.264129</td>\n",
       "      <td>The maximum residual error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.786</td>\n",
       "      <td>0.716</td>\n",
       "      <td>-9.776536</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.789</td>\n",
       "      <td>0.729</td>\n",
       "      <td>-8.230453</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>147571.708</td>\n",
       "      <td>181023.072</td>\n",
       "      <td>18.479061</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>91023.514</td>\n",
       "      <td>111734.697</td>\n",
       "      <td>18.536035</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max error</th>\n",
       "      <td>897621.898</td>\n",
       "      <td>989269.060</td>\n",
       "      <td>9.264129</td>\n",
       "      <td>The maximum residual error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.786</td>\n",
       "      <td>0.716</td>\n",
       "      <td>-9.776536</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.789</td>\n",
       "      <td>0.729</td>\n",
       "      <td>-8.230453</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>147571.708</td>\n",
       "      <td>181023.072</td>\n",
       "      <td>18.479061</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>91023.514</td>\n",
       "      <td>111734.697</td>\n",
       "      <td>18.536035</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max error</th>\n",
       "      <td>897621.898</td>\n",
       "      <td>989269.060</td>\n",
       "      <td>9.264129</td>\n",
       "      <td>The maximum residual error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Ridge (&lt;2M)</th>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.840</td>\n",
       "      <td>0.777</td>\n",
       "      <td>-8.108108</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.841</td>\n",
       "      <td>0.783</td>\n",
       "      <td>-7.407407</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>127800.591</td>\n",
       "      <td>160521.179</td>\n",
       "      <td>20.383969</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>76344.930</td>\n",
       "      <td>93887.806</td>\n",
       "      <td>18.684936</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max error</th>\n",
       "      <td>942098.599</td>\n",
       "      <td>1031360.528</td>\n",
       "      <td>8.654775</td>\n",
       "      <td>The maximum residual error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Lasso (&lt;2M)</th>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.839</td>\n",
       "      <td>0.775</td>\n",
       "      <td>-8.258065</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.840</td>\n",
       "      <td>0.782</td>\n",
       "      <td>-7.416880</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>128215.794</td>\n",
       "      <td>161111.259</td>\n",
       "      <td>20.417856</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>76340.622</td>\n",
       "      <td>94568.402</td>\n",
       "      <td>19.274704</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max error</th>\n",
       "      <td>932198.697</td>\n",
       "      <td>1022674.555</td>\n",
       "      <td>8.846984</td>\n",
       "      <td>The maximum residual error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">ElasticNet (&lt;2M)</th>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.839</td>\n",
       "      <td>0.776</td>\n",
       "      <td>-8.118557</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.841</td>\n",
       "      <td>0.783</td>\n",
       "      <td>-7.407407</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>128015.560</td>\n",
       "      <td>160847.612</td>\n",
       "      <td>20.411899</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>76295.585</td>\n",
       "      <td>94241.836</td>\n",
       "      <td>19.042765</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max error</th>\n",
       "      <td>936922.104</td>\n",
       "      <td>1026379.548</td>\n",
       "      <td>8.715825</td>\n",
       "      <td>The maximum residual error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">SVM (&lt;2M)</th>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.888</td>\n",
       "      <td>0.767</td>\n",
       "      <td>-15.775750</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.889</td>\n",
       "      <td>0.775</td>\n",
       "      <td>-14.709677</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>106966.148</td>\n",
       "      <td>164174.974</td>\n",
       "      <td>34.846253</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>62828.520</td>\n",
       "      <td>94789.639</td>\n",
       "      <td>33.717946</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max error</th>\n",
       "      <td>838421.432</td>\n",
       "      <td>958934.445</td>\n",
       "      <td>12.567388</td>\n",
       "      <td>The maximum residual error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">RandomForest (&lt;2M)</th>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.965</td>\n",
       "      <td>0.763</td>\n",
       "      <td>-26.474443</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.965</td>\n",
       "      <td>0.770</td>\n",
       "      <td>-25.324675</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>59907.004</td>\n",
       "      <td>165284.822</td>\n",
       "      <td>63.755290</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>32678.834</td>\n",
       "      <td>100354.137</td>\n",
       "      <td>67.436485</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max error</th>\n",
       "      <td>404931.552</td>\n",
       "      <td>926065.776</td>\n",
       "      <td>56.273997</td>\n",
       "      <td>The maximum residual error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">GradientBoosting (&lt;2M)</th>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.958</td>\n",
       "      <td>0.757</td>\n",
       "      <td>-26.552180</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.959</td>\n",
       "      <td>0.765</td>\n",
       "      <td>-25.359477</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>65764.343</td>\n",
       "      <td>167425.234</td>\n",
       "      <td>60.720173</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>39991.850</td>\n",
       "      <td>101810.207</td>\n",
       "      <td>60.719214</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max error</th>\n",
       "      <td>412316.707</td>\n",
       "      <td>912993.378</td>\n",
       "      <td>54.839025</td>\n",
       "      <td>The maximum residual error</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            in-sample  out-of-sample  \\\n",
       "model                  metrics                                         \n",
       "OLS (<2M)              r-squared                0.786          0.716   \n",
       "                       explained variance       0.789          0.729   \n",
       "                       RMSE                147571.708     181023.072   \n",
       "                       MAE                  91023.514     111734.697   \n",
       "                       max error           897621.898     989269.060   \n",
       "                       r-squared                0.786          0.716   \n",
       "                       explained variance       0.789          0.729   \n",
       "                       RMSE                147571.708     181023.072   \n",
       "                       MAE                  91023.514     111734.697   \n",
       "                       max error           897621.898     989269.060   \n",
       "                       r-squared                0.786          0.716   \n",
       "                       explained variance       0.789          0.729   \n",
       "                       RMSE                147571.708     181023.072   \n",
       "                       MAE                  91023.514     111734.697   \n",
       "                       max error           897621.898     989269.060   \n",
       "                       r-squared                0.786          0.716   \n",
       "                       explained variance       0.789          0.729   \n",
       "                       RMSE                147571.708     181023.072   \n",
       "                       MAE                  91023.514     111734.697   \n",
       "                       max error           897621.898     989269.060   \n",
       "                       r-squared                0.786          0.716   \n",
       "                       explained variance       0.789          0.729   \n",
       "                       RMSE                147571.708     181023.072   \n",
       "                       MAE                  91023.514     111734.697   \n",
       "                       max error           897621.898     989269.060   \n",
       "                       r-squared                0.786          0.716   \n",
       "                       explained variance       0.789          0.729   \n",
       "                       RMSE                147571.708     181023.072   \n",
       "                       MAE                  91023.514     111734.697   \n",
       "                       max error           897621.898     989269.060   \n",
       "Ridge (<2M)            r-squared                0.840          0.777   \n",
       "                       explained variance       0.841          0.783   \n",
       "                       RMSE                127800.591     160521.179   \n",
       "                       MAE                  76344.930      93887.806   \n",
       "                       max error           942098.599    1031360.528   \n",
       "Lasso (<2M)            r-squared                0.839          0.775   \n",
       "                       explained variance       0.840          0.782   \n",
       "                       RMSE                128215.794     161111.259   \n",
       "                       MAE                  76340.622      94568.402   \n",
       "                       max error           932198.697    1022674.555   \n",
       "ElasticNet (<2M)       r-squared                0.839          0.776   \n",
       "                       explained variance       0.841          0.783   \n",
       "                       RMSE                128015.560     160847.612   \n",
       "                       MAE                  76295.585      94241.836   \n",
       "                       max error           936922.104    1026379.548   \n",
       "SVM (<2M)              r-squared                0.888          0.767   \n",
       "                       explained variance       0.889          0.775   \n",
       "                       RMSE                106966.148     164174.974   \n",
       "                       MAE                  62828.520      94789.639   \n",
       "                       max error           838421.432     958934.445   \n",
       "RandomForest (<2M)     r-squared                0.965          0.763   \n",
       "                       explained variance       0.965          0.770   \n",
       "                       RMSE                 59907.004     165284.822   \n",
       "                       MAE                  32678.834     100354.137   \n",
       "                       max error           404931.552     926065.776   \n",
       "GradientBoosting (<2M) r-squared                0.958          0.757   \n",
       "                       explained variance       0.959          0.765   \n",
       "                       RMSE                 65764.343     167425.234   \n",
       "                       MAE                  39991.850     101810.207   \n",
       "                       max error           412316.707     912993.378   \n",
       "\n",
       "                                           Difference (%)  \\\n",
       "model                  metrics                              \n",
       "OLS (<2M)              r-squared                -9.776536   \n",
       "                       explained variance       -8.230453   \n",
       "                       RMSE                     18.479061   \n",
       "                       MAE                      18.536035   \n",
       "                       max error                 9.264129   \n",
       "                       r-squared                -9.776536   \n",
       "                       explained variance       -8.230453   \n",
       "                       RMSE                     18.479061   \n",
       "                       MAE                      18.536035   \n",
       "                       max error                 9.264129   \n",
       "                       r-squared                -9.776536   \n",
       "                       explained variance       -8.230453   \n",
       "                       RMSE                     18.479061   \n",
       "                       MAE                      18.536035   \n",
       "                       max error                 9.264129   \n",
       "                       r-squared                -9.776536   \n",
       "                       explained variance       -8.230453   \n",
       "                       RMSE                     18.479061   \n",
       "                       MAE                      18.536035   \n",
       "                       max error                 9.264129   \n",
       "                       r-squared                -9.776536   \n",
       "                       explained variance       -8.230453   \n",
       "                       RMSE                     18.479061   \n",
       "                       MAE                      18.536035   \n",
       "                       max error                 9.264129   \n",
       "                       r-squared                -9.776536   \n",
       "                       explained variance       -8.230453   \n",
       "                       RMSE                     18.479061   \n",
       "                       MAE                      18.536035   \n",
       "                       max error                 9.264129   \n",
       "Ridge (<2M)            r-squared                -8.108108   \n",
       "                       explained variance       -7.407407   \n",
       "                       RMSE                     20.383969   \n",
       "                       MAE                      18.684936   \n",
       "                       max error                 8.654775   \n",
       "Lasso (<2M)            r-squared                -8.258065   \n",
       "                       explained variance       -7.416880   \n",
       "                       RMSE                     20.417856   \n",
       "                       MAE                      19.274704   \n",
       "                       max error                 8.846984   \n",
       "ElasticNet (<2M)       r-squared                -8.118557   \n",
       "                       explained variance       -7.407407   \n",
       "                       RMSE                     20.411899   \n",
       "                       MAE                      19.042765   \n",
       "                       max error                 8.715825   \n",
       "SVM (<2M)              r-squared               -15.775750   \n",
       "                       explained variance      -14.709677   \n",
       "                       RMSE                     34.846253   \n",
       "                       MAE                      33.717946   \n",
       "                       max error                12.567388   \n",
       "RandomForest (<2M)     r-squared               -26.474443   \n",
       "                       explained variance      -25.324675   \n",
       "                       RMSE                     63.755290   \n",
       "                       MAE                      67.436485   \n",
       "                       max error                56.273997   \n",
       "GradientBoosting (<2M) r-squared               -26.552180   \n",
       "                       explained variance      -25.359477   \n",
       "                       RMSE                     60.720173   \n",
       "                       MAE                      60.719214   \n",
       "                       max error                54.839025   \n",
       "\n",
       "                                                              Description  \n",
       "model                  metrics                                             \n",
       "OLS (<2M)              r-squared             Coefficient of Determination  \n",
       "                       explained variance        Explained Variance Score  \n",
       "                       RMSE                Root Mean Squared Error (RMSE)  \n",
       "                       MAE                      Mean Absolute Error (MAE)  \n",
       "                       max error               The maximum residual error  \n",
       "                       r-squared             Coefficient of Determination  \n",
       "                       explained variance        Explained Variance Score  \n",
       "                       RMSE                Root Mean Squared Error (RMSE)  \n",
       "                       MAE                      Mean Absolute Error (MAE)  \n",
       "                       max error               The maximum residual error  \n",
       "                       r-squared             Coefficient of Determination  \n",
       "                       explained variance        Explained Variance Score  \n",
       "                       RMSE                Root Mean Squared Error (RMSE)  \n",
       "                       MAE                      Mean Absolute Error (MAE)  \n",
       "                       max error               The maximum residual error  \n",
       "                       r-squared             Coefficient of Determination  \n",
       "                       explained variance        Explained Variance Score  \n",
       "                       RMSE                Root Mean Squared Error (RMSE)  \n",
       "                       MAE                      Mean Absolute Error (MAE)  \n",
       "                       max error               The maximum residual error  \n",
       "                       r-squared             Coefficient of Determination  \n",
       "                       explained variance        Explained Variance Score  \n",
       "                       RMSE                Root Mean Squared Error (RMSE)  \n",
       "                       MAE                      Mean Absolute Error (MAE)  \n",
       "                       max error               The maximum residual error  \n",
       "                       r-squared             Coefficient of Determination  \n",
       "                       explained variance        Explained Variance Score  \n",
       "                       RMSE                Root Mean Squared Error (RMSE)  \n",
       "                       MAE                      Mean Absolute Error (MAE)  \n",
       "                       max error               The maximum residual error  \n",
       "Ridge (<2M)            r-squared             Coefficient of Determination  \n",
       "                       explained variance        Explained Variance Score  \n",
       "                       RMSE                Root Mean Squared Error (RMSE)  \n",
       "                       MAE                      Mean Absolute Error (MAE)  \n",
       "                       max error               The maximum residual error  \n",
       "Lasso (<2M)            r-squared             Coefficient of Determination  \n",
       "                       explained variance        Explained Variance Score  \n",
       "                       RMSE                Root Mean Squared Error (RMSE)  \n",
       "                       MAE                      Mean Absolute Error (MAE)  \n",
       "                       max error               The maximum residual error  \n",
       "ElasticNet (<2M)       r-squared             Coefficient of Determination  \n",
       "                       explained variance        Explained Variance Score  \n",
       "                       RMSE                Root Mean Squared Error (RMSE)  \n",
       "                       MAE                      Mean Absolute Error (MAE)  \n",
       "                       max error               The maximum residual error  \n",
       "SVM (<2M)              r-squared             Coefficient of Determination  \n",
       "                       explained variance        Explained Variance Score  \n",
       "                       RMSE                Root Mean Squared Error (RMSE)  \n",
       "                       MAE                      Mean Absolute Error (MAE)  \n",
       "                       max error               The maximum residual error  \n",
       "RandomForest (<2M)     r-squared             Coefficient of Determination  \n",
       "                       explained variance        Explained Variance Score  \n",
       "                       RMSE                Root Mean Squared Error (RMSE)  \n",
       "                       MAE                      Mean Absolute Error (MAE)  \n",
       "                       max error               The maximum residual error  \n",
       "GradientBoosting (<2M) r-squared             Coefficient of Determination  \n",
       "                       explained variance        Explained Variance Score  \n",
       "                       RMSE                Root Mean Squared Error (RMSE)  \n",
       "                       MAE                      Mean Absolute Error (MAE)  \n",
       "                       max error               The maximum residual error  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Concatenate baseline to existing dataframe\n",
    "df_report_all = pd.concat([df_baseline, df_report_all])\n",
    "display(df_report_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>in-sample</th>\n",
       "      <th>out-of-sample</th>\n",
       "      <th>Difference (%)</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th>metrics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ridge (&lt;2M)</th>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.840</td>\n",
       "      <td>0.777</td>\n",
       "      <td>-8.108108</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ElasticNet (&lt;2M)</th>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.839</td>\n",
       "      <td>0.776</td>\n",
       "      <td>-8.118557</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso (&lt;2M)</th>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.839</td>\n",
       "      <td>0.775</td>\n",
       "      <td>-8.258065</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM (&lt;2M)</th>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.888</td>\n",
       "      <td>0.767</td>\n",
       "      <td>-15.775750</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForest (&lt;2M)</th>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.965</td>\n",
       "      <td>0.763</td>\n",
       "      <td>-26.474443</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GradientBoosting (&lt;2M)</th>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.958</td>\n",
       "      <td>0.757</td>\n",
       "      <td>-26.552180</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">OLS (&lt;2M)</th>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.786</td>\n",
       "      <td>0.716</td>\n",
       "      <td>-9.776536</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.786</td>\n",
       "      <td>0.716</td>\n",
       "      <td>-9.776536</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.786</td>\n",
       "      <td>0.716</td>\n",
       "      <td>-9.776536</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.786</td>\n",
       "      <td>0.716</td>\n",
       "      <td>-9.776536</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.786</td>\n",
       "      <td>0.716</td>\n",
       "      <td>-9.776536</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.786</td>\n",
       "      <td>0.716</td>\n",
       "      <td>-9.776536</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  in-sample  out-of-sample  Difference (%)  \\\n",
       "model                  metrics                                               \n",
       "Ridge (<2M)            r-squared      0.840          0.777       -8.108108   \n",
       "ElasticNet (<2M)       r-squared      0.839          0.776       -8.118557   \n",
       "Lasso (<2M)            r-squared      0.839          0.775       -8.258065   \n",
       "SVM (<2M)              r-squared      0.888          0.767      -15.775750   \n",
       "RandomForest (<2M)     r-squared      0.965          0.763      -26.474443   \n",
       "GradientBoosting (<2M) r-squared      0.958          0.757      -26.552180   \n",
       "OLS (<2M)              r-squared      0.786          0.716       -9.776536   \n",
       "                       r-squared      0.786          0.716       -9.776536   \n",
       "                       r-squared      0.786          0.716       -9.776536   \n",
       "                       r-squared      0.786          0.716       -9.776536   \n",
       "                       r-squared      0.786          0.716       -9.776536   \n",
       "                       r-squared      0.786          0.716       -9.776536   \n",
       "\n",
       "                                                   Description  \n",
       "model                  metrics                                  \n",
       "Ridge (<2M)            r-squared  Coefficient of Determination  \n",
       "ElasticNet (<2M)       r-squared  Coefficient of Determination  \n",
       "Lasso (<2M)            r-squared  Coefficient of Determination  \n",
       "SVM (<2M)              r-squared  Coefficient of Determination  \n",
       "RandomForest (<2M)     r-squared  Coefficient of Determination  \n",
       "GradientBoosting (<2M) r-squared  Coefficient of Determination  \n",
       "OLS (<2M)              r-squared  Coefficient of Determination  \n",
       "                       r-squared  Coefficient of Determination  \n",
       "                       r-squared  Coefficient of Determination  \n",
       "                       r-squared  Coefficient of Determination  \n",
       "                       r-squared  Coefficient of Determination  \n",
       "                       r-squared  Coefficient of Determination  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>in-sample</th>\n",
       "      <th>out-of-sample</th>\n",
       "      <th>Difference (%)</th>\n",
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       "      <th>Ridge (&lt;2M)</th>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.841</td>\n",
       "      <td>0.783</td>\n",
       "      <td>-7.407407</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ElasticNet (&lt;2M)</th>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.841</td>\n",
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       "      <th>Lasso (&lt;2M)</th>\n",
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       "      <td>0.840</td>\n",
       "      <td>0.782</td>\n",
       "      <td>-7.416880</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM (&lt;2M)</th>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.889</td>\n",
       "      <td>0.775</td>\n",
       "      <td>-14.709677</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForest (&lt;2M)</th>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.965</td>\n",
       "      <td>0.770</td>\n",
       "      <td>-25.324675</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GradientBoosting (&lt;2M)</th>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.959</td>\n",
       "      <td>0.765</td>\n",
       "      <td>-25.359477</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">OLS (&lt;2M)</th>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.789</td>\n",
       "      <td>0.729</td>\n",
       "      <td>-8.230453</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.789</td>\n",
       "      <td>0.729</td>\n",
       "      <td>-8.230453</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.789</td>\n",
       "      <td>0.729</td>\n",
       "      <td>-8.230453</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.789</td>\n",
       "      <td>0.729</td>\n",
       "      <td>-8.230453</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.789</td>\n",
       "      <td>0.729</td>\n",
       "      <td>-8.230453</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.789</td>\n",
       "      <td>0.729</td>\n",
       "      <td>-8.230453</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           in-sample  out-of-sample  \\\n",
       "model                  metrics                                        \n",
       "Ridge (<2M)            explained variance      0.841          0.783   \n",
       "ElasticNet (<2M)       explained variance      0.841          0.783   \n",
       "Lasso (<2M)            explained variance      0.840          0.782   \n",
       "SVM (<2M)              explained variance      0.889          0.775   \n",
       "RandomForest (<2M)     explained variance      0.965          0.770   \n",
       "GradientBoosting (<2M) explained variance      0.959          0.765   \n",
       "OLS (<2M)              explained variance      0.789          0.729   \n",
       "                       explained variance      0.789          0.729   \n",
       "                       explained variance      0.789          0.729   \n",
       "                       explained variance      0.789          0.729   \n",
       "                       explained variance      0.789          0.729   \n",
       "                       explained variance      0.789          0.729   \n",
       "\n",
       "                                           Difference (%)  \\\n",
       "model                  metrics                              \n",
       "Ridge (<2M)            explained variance       -7.407407   \n",
       "ElasticNet (<2M)       explained variance       -7.407407   \n",
       "Lasso (<2M)            explained variance       -7.416880   \n",
       "SVM (<2M)              explained variance      -14.709677   \n",
       "RandomForest (<2M)     explained variance      -25.324675   \n",
       "GradientBoosting (<2M) explained variance      -25.359477   \n",
       "OLS (<2M)              explained variance       -8.230453   \n",
       "                       explained variance       -8.230453   \n",
       "                       explained variance       -8.230453   \n",
       "                       explained variance       -8.230453   \n",
       "                       explained variance       -8.230453   \n",
       "                       explained variance       -8.230453   \n",
       "\n",
       "                                                        Description  \n",
       "model                  metrics                                       \n",
       "Ridge (<2M)            explained variance  Explained Variance Score  \n",
       "ElasticNet (<2M)       explained variance  Explained Variance Score  \n",
       "Lasso (<2M)            explained variance  Explained Variance Score  \n",
       "SVM (<2M)              explained variance  Explained Variance Score  \n",
       "RandomForest (<2M)     explained variance  Explained Variance Score  \n",
       "GradientBoosting (<2M) explained variance  Explained Variance Score  \n",
       "OLS (<2M)              explained variance  Explained Variance Score  \n",
       "                       explained variance  Explained Variance Score  \n",
       "                       explained variance  Explained Variance Score  \n",
       "                       explained variance  Explained Variance Score  \n",
       "                       explained variance  Explained Variance Score  \n",
       "                       explained variance  Explained Variance Score  "
      ]
     },
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       "      <th></th>\n",
       "      <th>in-sample</th>\n",
       "      <th>out-of-sample</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ridge (&lt;2M)</th>\n",
       "      <th>RMSE</th>\n",
       "      <td>127800.591</td>\n",
       "      <td>160521.179</td>\n",
       "      <td>20.383969</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ElasticNet (&lt;2M)</th>\n",
       "      <th>RMSE</th>\n",
       "      <td>128015.560</td>\n",
       "      <td>160847.612</td>\n",
       "      <td>20.411899</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso (&lt;2M)</th>\n",
       "      <th>RMSE</th>\n",
       "      <td>128215.794</td>\n",
       "      <td>161111.259</td>\n",
       "      <td>20.417856</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM (&lt;2M)</th>\n",
       "      <th>RMSE</th>\n",
       "      <td>106966.148</td>\n",
       "      <td>164174.974</td>\n",
       "      <td>34.846253</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForest (&lt;2M)</th>\n",
       "      <th>RMSE</th>\n",
       "      <td>59907.004</td>\n",
       "      <td>165284.822</td>\n",
       "      <td>63.755290</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GradientBoosting (&lt;2M)</th>\n",
       "      <th>RMSE</th>\n",
       "      <td>65764.343</td>\n",
       "      <td>167425.234</td>\n",
       "      <td>60.720173</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">OLS (&lt;2M)</th>\n",
       "      <th>RMSE</th>\n",
       "      <td>147571.708</td>\n",
       "      <td>181023.072</td>\n",
       "      <td>18.479061</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>147571.708</td>\n",
       "      <td>181023.072</td>\n",
       "      <td>18.479061</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>147571.708</td>\n",
       "      <td>181023.072</td>\n",
       "      <td>18.479061</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>147571.708</td>\n",
       "      <td>181023.072</td>\n",
       "      <td>18.479061</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>147571.708</td>\n",
       "      <td>181023.072</td>\n",
       "      <td>18.479061</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>147571.708</td>\n",
       "      <td>181023.072</td>\n",
       "      <td>18.479061</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 in-sample  out-of-sample  Difference (%)  \\\n",
       "model                  metrics                                              \n",
       "Ridge (<2M)            RMSE     127800.591     160521.179       20.383969   \n",
       "ElasticNet (<2M)       RMSE     128015.560     160847.612       20.411899   \n",
       "Lasso (<2M)            RMSE     128215.794     161111.259       20.417856   \n",
       "SVM (<2M)              RMSE     106966.148     164174.974       34.846253   \n",
       "RandomForest (<2M)     RMSE      59907.004     165284.822       63.755290   \n",
       "GradientBoosting (<2M) RMSE      65764.343     167425.234       60.720173   \n",
       "OLS (<2M)              RMSE     147571.708     181023.072       18.479061   \n",
       "                       RMSE     147571.708     181023.072       18.479061   \n",
       "                       RMSE     147571.708     181023.072       18.479061   \n",
       "                       RMSE     147571.708     181023.072       18.479061   \n",
       "                       RMSE     147571.708     181023.072       18.479061   \n",
       "                       RMSE     147571.708     181023.072       18.479061   \n",
       "\n",
       "                                                   Description  \n",
       "model                  metrics                                  \n",
       "Ridge (<2M)            RMSE     Root Mean Squared Error (RMSE)  \n",
       "ElasticNet (<2M)       RMSE     Root Mean Squared Error (RMSE)  \n",
       "Lasso (<2M)            RMSE     Root Mean Squared Error (RMSE)  \n",
       "SVM (<2M)              RMSE     Root Mean Squared Error (RMSE)  \n",
       "RandomForest (<2M)     RMSE     Root Mean Squared Error (RMSE)  \n",
       "GradientBoosting (<2M) RMSE     Root Mean Squared Error (RMSE)  \n",
       "OLS (<2M)              RMSE     Root Mean Squared Error (RMSE)  \n",
       "                       RMSE     Root Mean Squared Error (RMSE)  \n",
       "                       RMSE     Root Mean Squared Error (RMSE)  \n",
       "                       RMSE     Root Mean Squared Error (RMSE)  \n",
       "                       RMSE     Root Mean Squared Error (RMSE)  \n",
       "                       RMSE     Root Mean Squared Error (RMSE)  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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></th>\n",
       "      <th>in-sample</th>\n",
       "      <th>out-of-sample</th>\n",
       "      <th>Difference (%)</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th>metrics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ridge (&lt;2M)</th>\n",
       "      <th>MAE</th>\n",
       "      <td>76344.930</td>\n",
       "      <td>93887.806</td>\n",
       "      <td>18.684936</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ElasticNet (&lt;2M)</th>\n",
       "      <th>MAE</th>\n",
       "      <td>76295.585</td>\n",
       "      <td>94241.836</td>\n",
       "      <td>19.042765</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso (&lt;2M)</th>\n",
       "      <th>MAE</th>\n",
       "      <td>76340.622</td>\n",
       "      <td>94568.402</td>\n",
       "      <td>19.274704</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM (&lt;2M)</th>\n",
       "      <th>MAE</th>\n",
       "      <td>62828.520</td>\n",
       "      <td>94789.639</td>\n",
       "      <td>33.717946</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForest (&lt;2M)</th>\n",
       "      <th>MAE</th>\n",
       "      <td>32678.834</td>\n",
       "      <td>100354.137</td>\n",
       "      <td>67.436485</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GradientBoosting (&lt;2M)</th>\n",
       "      <th>MAE</th>\n",
       "      <td>39991.850</td>\n",
       "      <td>101810.207</td>\n",
       "      <td>60.719214</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">OLS (&lt;2M)</th>\n",
       "      <th>MAE</th>\n",
       "      <td>91023.514</td>\n",
       "      <td>111734.697</td>\n",
       "      <td>18.536035</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>91023.514</td>\n",
       "      <td>111734.697</td>\n",
       "      <td>18.536035</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>91023.514</td>\n",
       "      <td>111734.697</td>\n",
       "      <td>18.536035</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>91023.514</td>\n",
       "      <td>111734.697</td>\n",
       "      <td>18.536035</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>91023.514</td>\n",
       "      <td>111734.697</td>\n",
       "      <td>18.536035</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>91023.514</td>\n",
       "      <td>111734.697</td>\n",
       "      <td>18.536035</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                in-sample  out-of-sample  Difference (%)  \\\n",
       "model                  metrics                                             \n",
       "Ridge (<2M)            MAE      76344.930      93887.806       18.684936   \n",
       "ElasticNet (<2M)       MAE      76295.585      94241.836       19.042765   \n",
       "Lasso (<2M)            MAE      76340.622      94568.402       19.274704   \n",
       "SVM (<2M)              MAE      62828.520      94789.639       33.717946   \n",
       "RandomForest (<2M)     MAE      32678.834     100354.137       67.436485   \n",
       "GradientBoosting (<2M) MAE      39991.850     101810.207       60.719214   \n",
       "OLS (<2M)              MAE      91023.514     111734.697       18.536035   \n",
       "                       MAE      91023.514     111734.697       18.536035   \n",
       "                       MAE      91023.514     111734.697       18.536035   \n",
       "                       MAE      91023.514     111734.697       18.536035   \n",
       "                       MAE      91023.514     111734.697       18.536035   \n",
       "                       MAE      91023.514     111734.697       18.536035   \n",
       "\n",
       "                                              Description  \n",
       "model                  metrics                             \n",
       "Ridge (<2M)            MAE      Mean Absolute Error (MAE)  \n",
       "ElasticNet (<2M)       MAE      Mean Absolute Error (MAE)  \n",
       "Lasso (<2M)            MAE      Mean Absolute Error (MAE)  \n",
       "SVM (<2M)              MAE      Mean Absolute Error (MAE)  \n",
       "RandomForest (<2M)     MAE      Mean Absolute Error (MAE)  \n",
       "GradientBoosting (<2M) MAE      Mean Absolute Error (MAE)  \n",
       "OLS (<2M)              MAE      Mean Absolute Error (MAE)  \n",
       "                       MAE      Mean Absolute Error (MAE)  \n",
       "                       MAE      Mean Absolute Error (MAE)  \n",
       "                       MAE      Mean Absolute Error (MAE)  \n",
       "                       MAE      Mean Absolute Error (MAE)  \n",
       "                       MAE      Mean Absolute Error (MAE)  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOTE: First row is the BEST performing model.\n"
     ]
    }
   ],
   "source": [
    "# Group performance by metrics for a better comparison between models\n",
    "for score in [\"r-squared\", 'explained variance',\"RMSE\", \"MAE\"]:\n",
    "    ascending = False if score in [\"r-squared\",'explained variance'] else True\n",
    "    display(\n",
    "        df_report_all\n",
    "        # access dataframe with multi-index using query\n",
    "        .query(f\"metrics == '{score}'\")\n",
    "        # sort values base on out-of-sample score\n",
    "        .sort_values(by=\"out-of-sample\", ascending=ascending)\n",
    "    )\n",
    "print(\"NOTE: First row is the BEST performing model.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " * As a **Candidate model** we will consider a `RidgeRegression` trained within a range of `price < 2M Eur` with  `[\"log(Area)\", \"log(Room)\", \"log(AR-ratio)\", \"Manhattan_distance\"]` as numerical features, `[\"Region\", \"Location\"]` as nominal features and `\"log(Price)\"` as target variable. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**11. Save the model as a pickle file with its model performance and relevant metadata similar to the baseline model computed in 4️⃣ Create a baseline Model.**\n",
    "\n",
    "🚨 This model is not production ready.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "• Artifacts will be saved in the folder:  2024-03-30-15-22-49_Ridge\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;num_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;,\n",
       "                                                   &#x27;log(AR-ratio)&#x27;,\n",
       "                                                   &#x27;Manhattan_distance&#x27;]),\n",
       "                                                 (&#x27;nom&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;nom_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;nominal_encoder&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                                  [&#x27;Region&#x27;, &#x27;Location&#x27;])])),\n",
       "                (&#x27;Ridge&#x27;, Ridge(alpha=0.2535151515151515))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-55\" type=\"checkbox\" ><label for=\"sk-estimator-id-55\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;num_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;,\n",
       "                                                   &#x27;log(AR-ratio)&#x27;,\n",
       "                                                   &#x27;Manhattan_distance&#x27;]),\n",
       "                                                 (&#x27;nom&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;nom_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;nominal_encoder&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                                  [&#x27;Region&#x27;, &#x27;Location&#x27;])])),\n",
       "                (&#x27;Ridge&#x27;, Ridge(alpha=0.2535151515151515))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-56\" type=\"checkbox\" ><label for=\"sk-estimator-id-56\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;num_imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;, &#x27;log(AR-ratio)&#x27;,\n",
       "                                  &#x27;Manhattan_distance&#x27;]),\n",
       "                                (&#x27;nom&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;nom_imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;nominal_encoder&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                 [&#x27;Region&#x27;, &#x27;Location&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-57\" type=\"checkbox\" ><label for=\"sk-estimator-id-57\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;, &#x27;log(AR-ratio)&#x27;, &#x27;Manhattan_distance&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-58\" type=\"checkbox\" ><label for=\"sk-estimator-id-58\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-59\" type=\"checkbox\" ><label for=\"sk-estimator-id-59\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-60\" type=\"checkbox\" ><label for=\"sk-estimator-id-60\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nom</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Region&#x27;, &#x27;Location&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-61\" type=\"checkbox\" ><label for=\"sk-estimator-id-61\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-62\" type=\"checkbox\" ><label for=\"sk-estimator-id-62\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-63\" type=\"checkbox\" ><label for=\"sk-estimator-id-63\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge(alpha=0.2535151515151515)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('num_imputer',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['log(Area)', 'log(Room)',\n",
       "                                                   'log(AR-ratio)',\n",
       "                                                   'Manhattan_distance']),\n",
       "                                                 ('nom',\n",
       "                                                  Pipeline(steps=[('nom_imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('nominal_encoder',\n",
       "                                                                   OneHotEncoder(drop='first'))]),\n",
       "                                                  ['Region', 'Location'])])),\n",
       "                ('Ridge', Ridge(alpha=0.2535151515151515))])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate folder name\n",
    "MODEL_NAME = \"Ridge\"\n",
    "MODEL = ml_models[\"Ridge\"]\n",
    "timestamp = pd.Timestamp.now().strftime(\"%Y-%m-%d-%H-%M-%S\")\n",
    "FOLDER_NAME = f\"{timestamp}_{MODEL_NAME}\"\n",
    "print(\"• Artifacts will be saved in the folder: \", FOLDER_NAME)\n",
    "# display the model before storing it\n",
    "MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create folder if not exists\n",
    "ARTIFACTS_DIR = \"./artifacts\"\n",
    "if not os.path.exists(os.path.join(ARTIFACTS_DIR, FOLDER_NAME)):\n",
    "    os.makedirs(os.path.join(ARTIFACTS_DIR, FOLDER_NAME))\n",
    "\n",
    "# Save model as a pickle file\n",
    "with open(os.path.join(ARTIFACTS_DIR, FOLDER_NAME, MODEL_NAME + \".pkl\"), \"wb\") as f:\n",
    "    pickle.dump(MODEL, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;num_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;,\n",
       "                                                   &#x27;log(AR-ratio)&#x27;,\n",
       "                                                   &#x27;Manhattan_distance&#x27;]),\n",
       "                                                 (&#x27;nom&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;nom_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;nominal_encoder&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                                  [&#x27;Region&#x27;, &#x27;Location&#x27;])])),\n",
       "                (&#x27;Ridge&#x27;, Ridge(alpha=0.2535151515151515))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-64\" type=\"checkbox\" ><label for=\"sk-estimator-id-64\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;num_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;,\n",
       "                                                   &#x27;log(AR-ratio)&#x27;,\n",
       "                                                   &#x27;Manhattan_distance&#x27;]),\n",
       "                                                 (&#x27;nom&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;nom_imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;nominal_encoder&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                                  [&#x27;Region&#x27;, &#x27;Location&#x27;])])),\n",
       "                (&#x27;Ridge&#x27;, Ridge(alpha=0.2535151515151515))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-65\" type=\"checkbox\" ><label for=\"sk-estimator-id-65\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;num_imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;, &#x27;log(AR-ratio)&#x27;,\n",
       "                                  &#x27;Manhattan_distance&#x27;]),\n",
       "                                (&#x27;nom&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;nom_imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;nominal_encoder&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                 [&#x27;Region&#x27;, &#x27;Location&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-66\" type=\"checkbox\" ><label for=\"sk-estimator-id-66\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;log(Area)&#x27;, &#x27;log(Room)&#x27;, &#x27;log(AR-ratio)&#x27;, &#x27;Manhattan_distance&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-67\" type=\"checkbox\" ><label for=\"sk-estimator-id-67\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-68\" type=\"checkbox\" ><label for=\"sk-estimator-id-68\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-69\" type=\"checkbox\" ><label for=\"sk-estimator-id-69\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nom</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Region&#x27;, &#x27;Location&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-70\" type=\"checkbox\" ><label for=\"sk-estimator-id-70\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-71\" type=\"checkbox\" ><label for=\"sk-estimator-id-71\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-72\" type=\"checkbox\" ><label for=\"sk-estimator-id-72\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge(alpha=0.2535151515151515)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('num_imputer',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['log(Area)', 'log(Room)',\n",
       "                                                   'log(AR-ratio)',\n",
       "                                                   'Manhattan_distance']),\n",
       "                                                 ('nom',\n",
       "                                                  Pipeline(steps=[('nom_imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('nominal_encoder',\n",
       "                                                                   OneHotEncoder(drop='first'))]),\n",
       "                                                  ['Region', 'Location'])])),\n",
       "                ('Ridge', Ridge(alpha=0.2535151515151515))])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load model and perform sanity check\n",
    "with open(os.path.join(ARTIFACTS_DIR, FOLDER_NAME, MODEL_NAME + \".pkl\"), \"rb\") as f:\n",
    "    candidate_model = pickle.load(f)\n",
    "\n",
    "candidate_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "• Model coefficients: \n",
      "  [ 0.21402621  0.16127193  0.11782287 -0.07748445 -0.31270028  0.20402928\n",
      " -0.14922474 -0.1201662   0.16753715 -0.20295068  0.26941082 -0.0062357\n",
      "  0.15030034]\n",
      "• Model hyperparameter: \n",
      "  0.2535151515151515\n"
     ]
    }
   ],
   "source": [
    "# The coefficients and hyperparameter values should match\n",
    "assert np.allclose(\n",
    "    ml_models[MODEL_NAME][\"Ridge\"].coef_, candidate_model[\"Ridge\"].coef_, atol=1e-3\n",
    "), \"Coefficients are not the same\"\n",
    "assert np.allclose(\n",
    "    ml_models[MODEL_NAME][\"Ridge\"].alpha, candidate_model[\"Ridge\"].alpha, atol=1e-3\n",
    "), \"Coefficients are not the same\"\n",
    "\n",
    "# print the coefficient values\n",
    "print(\"• Model coefficients: \\n \", candidate_model[\"Ridge\"].coef_)\n",
    "print(\"• Model hyperparameter: \\n \", candidate_model[\"Ridge\"].alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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></th>\n",
       "      <th>in-sample</th>\n",
       "      <th>out-of-sample</th>\n",
       "      <th>Difference (%)</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th>metrics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Ridge</th>\n",
       "      <th>r-squared</th>\n",
       "      <td>0.840</td>\n",
       "      <td>0.777</td>\n",
       "      <td>-8.108108</td>\n",
       "      <td>Coefficient of Determination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained variance</th>\n",
       "      <td>0.841</td>\n",
       "      <td>0.783</td>\n",
       "      <td>-7.407407</td>\n",
       "      <td>Explained Variance Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>127800.591</td>\n",
       "      <td>160521.179</td>\n",
       "      <td>20.383969</td>\n",
       "      <td>Root Mean Squared Error (RMSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>76344.930</td>\n",
       "      <td>93887.806</td>\n",
       "      <td>18.684936</td>\n",
       "      <td>Mean Absolute Error (MAE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max error</th>\n",
       "      <td>942098.599</td>\n",
       "      <td>1031360.528</td>\n",
       "      <td>8.654775</td>\n",
       "      <td>The maximum residual error</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           in-sample  out-of-sample  Difference (%)  \\\n",
       "model metrics                                                         \n",
       "Ridge r-squared                0.840          0.777       -8.108108   \n",
       "      explained variance       0.841          0.783       -7.407407   \n",
       "      RMSE                127800.591     160521.179       20.383969   \n",
       "      MAE                  76344.930      93887.806       18.684936   \n",
       "      max error           942098.599    1031360.528        8.654775   \n",
       "\n",
       "                                             Description  \n",
       "model metrics                                             \n",
       "Ridge r-squared             Coefficient of Determination  \n",
       "      explained variance        Explained Variance Score  \n",
       "      RMSE                Root Mean Squared Error (RMSE)  \n",
       "      MAE                      Mean Absolute Error (MAE)  \n",
       "      max error               The maximum residual error  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate report for the candidate model and compare it with the previous one\n",
    "y_fit = np.exp(candidate_model.predict(X_train_sk))\n",
    "y_pred = np.exp(candidate_model.predict(X_val_sk))\n",
    "\n",
    "df_candidate_report = regression_report_in_sample_out_of_sample(\n",
    "    y_train=np.exp(y_train_sk),\n",
    "    y_fit=y_fit,\n",
    "    y_test=np.exp(y_val_sk),\n",
    "    y_pred=y_pred,\n",
    "    model_name=MODEL_NAME,\n",
    ")\n",
    "\n",
    "display(df_candidate_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store the report in a dataframe\n",
    "df_candidate_report.to_csv(\n",
    "    os.path.join(ARTIFACTS_DIR, FOLDER_NAME, MODEL_NAME + \"_performance.csv\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "Metadata = f\"\"\"\n",
    "---\n",
    "library_name: sklearn\n",
    "tags:\n",
    "- Dutch house price prediction\n",
    "- tabular-regression\n",
    "license: NA\n",
    "datasets:\n",
    "- {DATA_TRAIN_PATH}\n",
    "- {DATA_VAL_PATH}\n",
    "model: \n",
    "- 'ridge regression'\n",
    "metrics:\n",
    "- 'r-squared'\n",
    "features:\n",
    "- {X_train_sk.columns.tolist()}\n",
    "target:\n",
    "- {y_train_sk.name}\n",
    "additional_information: \n",
    "- Dataset is bounded to samples with price < 2M\n",
    "- The Dataset contains only houses from Amsterdam\n",
    "- IMPORTANT: this model is not production ready\n",
    "---\n",
    "\"\"\"\n",
    "\n",
    "# store string as a markdown file\n",
    "with open(\n",
    "    os.path.join(ARTIFACTS_DIR, FOLDER_NAME, MODEL_NAME + \"_metadata.md\"), \"w\"\n",
    ") as f:\n",
    "    f.write(Metadata)"
   ]
  }
 ],
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