{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Learning Practice 7 for the University of Tulsa's QM-7063 Data Mining Course\n",
    "# Classification and Regression Trees\n",
    "# # Professor: Dr. Abdulrashid, Spring 2023\n",
    "# Noah L. Schrick - 1492657\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from pathlib import Path\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.tree import export_text\n",
    "from sklearn import tree\n",
    "import scikitplot as skplt\n",
    "from dmba import regressionSummary, classificationSummary\n",
    "from dmba import plotDecisionTree\n",
    "from sklearn.model_selection import GridSearchCV\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 9.1\n",
    "\n",
    "Competitive Auctions on eBay.com. \n",
    "\n",
    "The file eBayAuctions.csv contains information on 1972 auctions that transacted on eBay.com during May–June 2004. The goal is to use these data to build a model that will classify auctions as competitive or non-competitive. A competitive auction is defined as an auction with at least two bids placed on the item auctioned. The data include variables that describe the item (auction category), the seller (his/her eBay rating), and the auction terms that the seller selected (auction duration, opening price, currency, day-of-week of auction close). In addition, we have the price at which the auction closed. The task is to predict whether or not the auction will be competitive. \n",
    "\n",
    "Data Preprocessing. \n",
    "Convert variable Duration into a categorical variable. Split the data into training (60%) and validation (40%) datasets. \n",
    "\n",
    "a. Fit a classification tree using all predictors. To avoid overfitting, set the minimum number of records in a terminal node to 50 and the maximum tree depth to 7. Write down the results in terms of rules. (Note: If you had to slightly reduce the number of predictors due to software limitations, or for clarity of presentation, which would be a good variable to choose?)\n",
    "b. Is this model practical for predicting the outcome of a new auction? \n",
    "c. Describe the interesting and uninteresting information that these rules provide.\n",
    "d. Fit another classification tree (using a tree with a minimum number of records per terminal node = 50 and maximum depth = 7), this time only with predictors that can be used for predicting the outcome of a new auction. Describe the resulting tree in terms of rules. Make sure to report the smallest set of rules required for classification.\n",
    "e. Plot the resulting tree on a scatter plot: Use the two axes for the two best (quantitative) predictors. Each auction will appear as a point, with coordinates corresponding to its values on those two predictors. Use different colors or symbols to separate competitive and noncompetitive auctions. Draw lines (you can sketch these by hand or use Python) at the values that create splits. Does this splitting seem\n",
    "reasonable with respect to the meaning of the two predictors? Does it seem to do a good job of separating the two classes?\n",
    "f. Examine the lift chart and the confusion matrix for the tree. What can you say about the predictive performance of this model?\n",
    "g. Based on this last tree, what can you conclude from these data about the chances of an auction obtaining at least two bids and its  relationship to the auction settings set by the seller (duration, opening price, ending day, currency)? What would you recommend for a seller as the strategy that will most likely lead to a competitive auction?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data pre-processing\n",
    "auction_df = pd.read_csv('eBayAuctions.csv')\n",
    "\n",
    "# Convert cols to categorical\n",
    "auction_df['Duration'] = auction_df['Duration'].astype('category')\n",
    "auction_df = pd.get_dummies(auction_df, prefix_sep='_', drop_first=True)\n",
    "\n",
    "# Spec outcome\n",
    "X = auction_df.drop(columns=['Competitive?'])\n",
    "y = auction_df['Competitive?']\n",
    "# 60/40 split\n",
    "train_X, valid_X, train_y, valid_y = train_test_split(X, y, test_size=0.4, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- OpenPrice <= 3.62\n",
      "|   |--- ClosePrice <= 3.64\n",
      "|   |   |--- OpenPrice <= 1.03\n",
      "|   |   |   |--- class: 1\n",
      "|   |   |--- OpenPrice >  1.03\n",
      "|   |   |   |--- OpenPrice <= 2.45\n",
      "|   |   |   |   |--- class: 0\n",
      "|   |   |   |--- OpenPrice >  2.45\n",
      "|   |   |   |   |--- class: 0\n",
      "|   |--- ClosePrice >  3.64\n",
      "|   |   |--- Duration_10 <= 0.50\n",
      "|   |   |   |--- class: 1\n",
      "|   |   |--- Duration_10 >  0.50\n",
      "|   |   |   |--- class: 1\n",
      "|--- OpenPrice >  3.62\n",
      "|   |--- ClosePrice <= 10.00\n",
      "|   |   |--- OpenPrice <= 4.97\n",
      "|   |   |   |--- class: 0\n",
      "|   |   |--- OpenPrice >  4.97\n",
      "|   |   |   |--- ClosePrice <= 6.82\n",
      "|   |   |   |   |--- class: 0\n",
      "|   |   |   |--- ClosePrice >  6.82\n",
      "|   |   |   |   |--- OpenPrice <= 7.99\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- OpenPrice >  7.99\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |--- ClosePrice >  10.00\n",
      "|   |   |--- OpenPrice <= 10.97\n",
      "|   |   |   |--- OpenPrice <= 9.89\n",
      "|   |   |   |   |--- class: 1\n",
      "|   |   |   |--- OpenPrice >  9.89\n",
      "|   |   |   |   |--- class: 1\n",
      "|   |   |--- OpenPrice >  10.97\n",
      "|   |   |   |--- sellerRating <= 813.00\n",
      "|   |   |   |   |--- class: 1\n",
      "|   |   |   |--- sellerRating >  813.00\n",
      "|   |   |   |   |--- sellerRating <= 2107.00\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- sellerRating >  2107.00\n",
      "|   |   |   |   |   |--- sellerRating <= 3499.00\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- sellerRating >  3499.00\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# a\n",
    "fullClassTree = DecisionTreeClassifier(random_state=1, min_samples_leaf=50, max_depth=7)\n",
    "fullClassTree.fit(train_X, train_y)\n",
    "tree_rules = export_text(fullClassTree, feature_names=list(train_X.columns))\n",
    "print(tree_rules)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# If you had to slightly reduce the number of predictors due to software limitations, or for clarity of presentation, which would be a good variable to choose?\n",
    "Category and Currency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix (Accuracy 0.8162)\n",
      "\n",
      "       Prediction\n",
      "Actual   0   1\n",
      "     0 305  48\n",
      "     1  97 339\n"
     ]
    }
   ],
   "source": [
    "# b\n",
    "classificationSummary(valid_y, fullClassTree.predict(valid_X))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# b. Is this model practical for predicting the outcome of a new auction? \n",
    "This model works well for the dataset provided, but is not practical. The primary issue is that this model uses closePrice to predict the outcome, and closePrice is not something known in advance. In addition, for this set of data, the tree is quick to build and use, and has a 81.62% accuracy. However, many of the rules appear overfitted for the data provided."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# c. Describe the interesting and uninteresting information that these rules provide.\n",
    "Of interest:\n",
    "    The tree starts the split with OpenPrice, and is able to cleanly make a binary split.\n",
    "\n",
    "Not of interest:\n",
    "    If the OpenPrice > 3.62, the next split is based on ClosePrice. However, it \"splits\" into a \"0\" category, meaning ClosePrice does not apply much to the training data. From here, the rules appear overfitted, choosing various price points to split at."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- OpenPrice <= 3.62\n",
      "|   |--- OpenPrice <= 1.04\n",
      "|   |   |--- sellerRating <= 3138.50\n",
      "|   |   |   |--- class: 1\n",
      "|   |   |--- sellerRating >  3138.50\n",
      "|   |   |   |--- class: 1\n",
      "|   |--- OpenPrice >  1.04\n",
      "|   |   |--- sellerRating <= 2365.50\n",
      "|   |   |   |--- sellerRating <= 1099.50\n",
      "|   |   |   |   |--- sellerRating <= 493.50\n",
      "|   |   |   |   |   |--- sellerRating <= 102.00\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- sellerRating >  102.00\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- sellerRating >  493.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |--- sellerRating >  1099.50\n",
      "|   |   |   |   |--- OpenPrice <= 3.32\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- OpenPrice >  3.32\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |--- sellerRating >  2365.50\n",
      "|   |   |   |--- class: 0\n",
      "|--- OpenPrice >  3.62\n",
      "|   |--- sellerRating <= 601.50\n",
      "|   |   |--- sellerRating <= 128.00\n",
      "|   |   |   |--- class: 1\n",
      "|   |   |--- sellerRating >  128.00\n",
      "|   |   |   |--- class: 1\n",
      "|   |--- sellerRating >  601.50\n",
      "|   |   |--- OpenPrice <= 9.89\n",
      "|   |   |   |--- sellerRating <= 3909.50\n",
      "|   |   |   |   |--- sellerRating <= 1847.50\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- sellerRating >  1847.50\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |   |--- sellerRating >  3909.50\n",
      "|   |   |   |   |--- class: 0\n",
      "|   |   |--- OpenPrice >  9.89\n",
      "|   |   |   |--- OpenPrice <= 11.99\n",
      "|   |   |   |   |--- class: 1\n",
      "|   |   |   |--- OpenPrice >  11.99\n",
      "|   |   |   |   |--- sellerRating <= 2430.00\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- sellerRating >  2430.00\n",
      "|   |   |   |   |   |--- OpenPrice <= 21.99\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- OpenPrice >  21.99\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# d\n",
    "auction_df_2 = pd.read_csv('eBayAuctions.csv')\n",
    "\n",
    "# Convert cols to categorical\n",
    "auction_df_2['Duration'] = auction_df_2['Duration'].astype('category')\n",
    "auction_df_2 = pd.get_dummies(auction_df_2, drop_first=True)\n",
    "\n",
    "# Spec outcome\n",
    "X_2 = auction_df_2.drop(list(auction_df_2.filter(regex = 'Category')), axis = 1)\n",
    "X_2 = X_2.drop(list(X_2.filter(regex = 'currency')), axis = 1)\n",
    "X_2 = X_2.drop(list(X_2.filter(regex = 'Competitive?')), axis = 1)\n",
    "X_2 = X_2.drop(list(X_2.filter(regex = 'ClosePrice')), axis = 1)\n",
    "y_2 = auction_df_2['Competitive?']\n",
    "\n",
    "# 60/40 split\n",
    "train_X_2, valid_X_2, train_y_2, valid_y_2 = train_test_split(X_2, y_2, test_size=0.4, random_state=1)\n",
    "\n",
    "fullClassTree_2 = DecisionTreeClassifier(random_state=1, min_samples_leaf=50, max_depth=7)\n",
    "fullClassTree_2.fit(train_X_2, train_y_2)\n",
    "tree_rules_2 = export_text(fullClassTree_2, feature_names=list(train_X_2.columns))\n",
    "print(tree_rules_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Open Price and Seller Rating on Competitive Auctions')"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# e Plot the resulting tree on a scatter plot\n",
    "#plt.scatter(X_2, y_2)\n",
    "preds = fullClassTree_2.predict(valid_X_2)\n",
    "plt.scatter(valid_X_2['OpenPrice'], valid_X_2['sellerRating'], c=preds)\n",
    "plt.xlabel(\"Open Price\")\n",
    "plt.ylabel(\"Seller Rating\")\n",
    "plt.legend(preds)\n",
    "\n",
    "plt.title(\"Open Price and Seller Rating on Competitive Auctions\")\n",
    "\n",
    "# len(X_2) == len(y_2)\n",
    "# Is splitting reasonable?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix (Accuracy 0.7148)\n",
      "\n",
      "       Prediction\n",
      "Actual   0   1\n",
      "     0 222 131\n",
      "     1  94 342\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# f lift chart and the confusion matrix\n",
    "classificationSummary(valid_y_2, fullClassTree_2.predict(valid_X_2))\n",
    "preds = fullClassTree_2.predict_proba(valid_X_2)\n",
    "\n",
    "skplt.metrics.plot_cumulative_gain(valid_y_2, preds)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# g. Based on this last tree, what can you conclude from these data about the chances of an auction obtaining at least two bids and its  relationship to the auction settings set by the seller (duration, opening price, ending day, currency)? What would you recommend for a seller as the strategy that will most likely lead to a competitive auction?\n",
    "\n",
    "For sellers with both a high rating and a low rating, the best strategy for a competitive auction is to have a lower OpenPrice. Starting with a higher OpenPrice, though possible for highly rated sellers, tends to result in an auction with one bid."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 9.3\n",
    "\n",
    "Predicting Prices of Used Cars (Regression Trees). \n",
    "\n",
    "The file ToyotaCorolla.csv contains the data on used cars (Toyota Corolla) on sale during late summer of 2004 in the Netherlands. It has 1436 records containing details on 38 attributes, including Price, Age, Kilometers, HP, and other specifications. The goal is to predict the price\n",
    "of a used Toyota Corolla based on its specifications. (The example in Section 9.7 is a subset of this dataset.) \n",
    "\n",
    "Data Preprocessing. Split the data into training (60%), and validation (40%) datasets.\n",
    "\n",
    "a. Run a full-grown regression tree (RT) with outcome variable Price and predictors Age_08_04, KM, Fuel_Type (first convert to dummies), HP, Automatic, Doors, Quarterly_Tax, Mfr_Guarantee, Guarantee_Period, Airco, Automatic_airco, CD_Player, Powered_Windows, Sport_Model, and Tow_Bar. Set random_state=1.\n",
    "    i. Which appear to be the three or four most important car specifications for predicting the car’s price?\n",
    "    ii. Compare the prediction errors of the training and validation sets by examining their RMS error and by plotting the two boxplots. How does the predictive performance of the validation set compare to the training set? Why does this occur?\n",
    "    iii. How might we achieve better validation predictive performance at the expense of training performance?\n",
    "    iv. Create a smaller tree by using GridSearchCV() with cv = 5 to find a fine-tuned tree. Compared to the full-grown tree, what is the predictive performance on the validation set?\n",
    "b. Let us see the effect of turning the price variable into a categorical variable. First, create a new variable that categorizes price into 20 bins. Now repartition the data keeping Binned_Price instead of Price. Run a classification tree with the same set of input variables as in the RT, and with Binned_Price as the output variable. As in the less deep regression tree, create a smaller tree by using GridSearchCV() with\n",
    "cv = 5 to find a fine-tuned tree.\n",
    "    i. Compare the smaller tree generated by the CT with the smaller tree generated by RT. Are they different? (Look at structure, the top predictors, size of tree, etc.) Why?\n",
    "    ii. Predict the price, using the smaller RT and CT, of a used Toyota Corolla with the specifications listed in Table 9.10.\n",
    "\n",
    "        TABLE 9.10 SPECIFICATIONS FOR A PARTICULAR TOYOTA COROLLA\n",
    "        Variable            Value\n",
    "        Age_-08_-04         77\n",
    "        KM                  117,000\n",
    "        Fuel_Type           Petrol\n",
    "        HP                  110\n",
    "        Automatic           No\n",
    "        Doors               5\n",
    "        Quarterly_Tax       100\n",
    "        Mfg_Guarantee       No\n",
    "        Guarantee_Period    3\n",
    "        Airco               Yes\n",
    "        Automatic_airco     No\n",
    "        CD_Player           No\n",
    "        Powered_Windows     No\n",
    "        Sport_Model         No\n",
    "        Tow_Bar             Yes\n",
    "\n",
    "    iii. Compare the predictions in terms of the predictors that were used, the magnitude of the difference between the two predictions, and the advantages and disadvantages of the two methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pre-processing\n",
    "toyotaCorolla_df = pd.read_csv('ToyotaCorolla.csv')\n",
    "\n",
    "toyotaCorolla_df = toyotaCorolla_df.rename(columns={'Age_08_04': 'Age', 'Quarterly_Tax': 'Tax'})\n",
    "\n",
    "predictors = ['Age', 'KM', 'Fuel_Type', 'HP', 'Met_Color', 'Automatic', 'CC', 'Doors', 'Tax', 'Weight']\n",
    "outcome = 'Price'\n",
    "\n",
    "X = pd.get_dummies(toyotaCorolla_df[predictors], drop_first=True)\n",
    "y = toyotaCorolla_df[outcome]\n",
    "train_X, valid_X, train_y, valid_y = train_test_split(X, y, test_size=0.4, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "DecisionTreeRegressor(random_state=1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a\n",
    "\n",
    "# create a regressor object\n",
    "regTree = DecisionTreeRegressor(random_state = 1) \n",
    "  \n",
    "# fit the regressor with X and Y data\n",
    "regTree.fit(train_X, train_y)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# i. Which appear to be the three or four most important car specifications for predicting the car’s price?\n",
    "   \n",
    "Age             8.41058553e-01 \n",
    "KM              5.89986658e-02\n",
    "HP              5.55734179e-02\n",
    "Met_Color       3.07059056e-03\n",
    "Automatic       1.27158901e-03\n",
    "CC              2.03786985e-03\n",
    "Doors           4.24503831e-03\n",
    "Tax             3.26629363e-03\n",
    "Weight          3.03948295e-02 \n",
    "Diesel Fuel     7.92420617e-05 \n",
    "Petrol Fuel     3.91028035e-06\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feat importance = [8.41058553e-01 5.89986658e-02 5.55734179e-02 3.07059056e-03\n",
      " 1.27158901e-03 2.03786985e-03 4.24503831e-03 3.26629363e-03\n",
      " 3.03948295e-02 7.92420617e-05 3.91028035e-06]\n",
      "|--- Age <= 32.50\n",
      "|   |--- HP <= 113.00\n",
      "|   |   |--- Age <= 21.00\n",
      "|   |   |   |--- Weight <= 1127.50\n",
      "|   |   |   |   |--- KM <= 24172.50\n",
      "|   |   |   |   |   |--- KM <= 12828.50\n",
      "|   |   |   |   |   |   |--- KM <= 113.00\n",
      "|   |   |   |   |   |   |   |--- Doors <= 4.50\n",
      "|   |   |   |   |   |   |   |   |--- CC <= 1500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [17795.00]\n",
      "|   |   |   |   |   |   |   |   |--- CC >  1500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [17900.00]\n",
      "|   |   |   |   |   |   |   |--- Doors >  4.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [18245.00]\n",
      "|   |   |   |   |   |   |--- KM >  113.00\n",
      "|   |   |   |   |   |   |   |--- KM <= 3190.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [21125.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  3190.00\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 16.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 6577.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [17650.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  6577.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 10.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  10.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- Age >  16.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [20750.00]\n",
      "|   |   |   |   |   |--- KM >  12828.50\n",
      "|   |   |   |   |   |   |--- HP <= 103.50\n",
      "|   |   |   |   |   |   |   |--- Age <= 14.00\n",
      "|   |   |   |   |   |   |   |   |--- Doors <= 4.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [18500.00]\n",
      "|   |   |   |   |   |   |   |   |--- Doors >  4.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [18450.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  14.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 18702.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1105.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Doors <= 3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [16950.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Doors >  3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [16868.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1105.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [17200.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  18702.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [16450.00]\n",
      "|   |   |   |   |   |   |--- HP >  103.50\n",
      "|   |   |   |   |   |   |   |--- Weight <= 1110.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [18500.00]\n",
      "|   |   |   |   |   |   |   |--- Weight >  1110.00\n",
      "|   |   |   |   |   |   |   |   |--- Weight <= 1117.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [19500.00]\n",
      "|   |   |   |   |   |   |   |   |--- Weight >  1117.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [18990.00]\n",
      "|   |   |   |   |--- KM >  24172.50\n",
      "|   |   |   |   |   |--- Weight <= 1102.50\n",
      "|   |   |   |   |   |   |--- Tax <= 52.00\n",
      "|   |   |   |   |   |   |   |--- value: [15850.00]\n",
      "|   |   |   |   |   |   |--- Tax >  52.00\n",
      "|   |   |   |   |   |   |   |--- value: [15950.00]\n",
      "|   |   |   |   |   |--- Weight >  1102.50\n",
      "|   |   |   |   |   |   |--- KM <= 33813.50\n",
      "|   |   |   |   |   |   |   |--- value: [16250.00]\n",
      "|   |   |   |   |   |   |--- KM >  33813.50\n",
      "|   |   |   |   |   |   |   |--- HP <= 103.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [16500.00]\n",
      "|   |   |   |   |   |   |   |--- HP >  103.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [16350.00]\n",
      "|   |   |   |--- Weight >  1127.50\n",
      "|   |   |   |   |--- Age <= 13.50\n",
      "|   |   |   |   |   |--- KM <= 10920.50\n",
      "|   |   |   |   |   |   |--- Age <= 10.00\n",
      "|   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [22500.00]\n",
      "|   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 2507.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [21500.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  2507.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [21950.00]\n",
      "|   |   |   |   |   |   |--- Age >  10.00\n",
      "|   |   |   |   |   |   |   |--- value: [20500.00]\n",
      "|   |   |   |   |   |--- KM >  10920.50\n",
      "|   |   |   |   |   |   |--- Age <= 10.50\n",
      "|   |   |   |   |   |   |   |--- value: [23750.00]\n",
      "|   |   |   |   |   |   |--- Age >  10.50\n",
      "|   |   |   |   |   |   |   |--- value: [24500.00]\n",
      "|   |   |   |   |--- Age >  13.50\n",
      "|   |   |   |   |   |--- Weight <= 1142.50\n",
      "|   |   |   |   |   |   |--- KM <= 18654.00\n",
      "|   |   |   |   |   |   |   |--- value: [19950.00]\n",
      "|   |   |   |   |   |   |--- KM >  18654.00\n",
      "|   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 31691.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [18450.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  31691.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [16950.00]\n",
      "|   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- CC <= 1500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [17950.00]\n",
      "|   |   |   |   |   |   |   |   |--- CC >  1500.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [18950.00]\n",
      "|   |   |   |   |   |--- Weight >  1142.50\n",
      "|   |   |   |   |   |   |--- HP <= 100.00\n",
      "|   |   |   |   |   |   |   |--- Age <= 19.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [19950.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  19.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [19250.00]\n",
      "|   |   |   |   |   |   |--- HP >  100.00\n",
      "|   |   |   |   |   |   |   |--- Doors <= 3.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [21950.00]\n",
      "|   |   |   |   |   |   |   |--- Doors >  3.50\n",
      "|   |   |   |   |   |   |   |   |--- Doors <= 4.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [20950.00]\n",
      "|   |   |   |   |   |   |   |   |--- Doors >  4.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [20500.00]\n",
      "|   |   |--- Age >  21.00\n",
      "|   |   |   |--- Weight <= 1175.00\n",
      "|   |   |   |   |--- HP <= 103.50\n",
      "|   |   |   |   |   |--- KM <= 36849.50\n",
      "|   |   |   |   |   |   |--- Age <= 28.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 32750.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 16208.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [16650.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  16208.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 31205.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 28353.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  28353.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  31205.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [14950.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  32750.50\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 24.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [15750.00]\n",
      "|   |   |   |   |   |   |   |   |--- Age >  24.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [17950.00]\n",
      "|   |   |   |   |   |   |--- Age >  28.50\n",
      "|   |   |   |   |   |   |   |--- Tax <= 52.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [12950.00]\n",
      "|   |   |   |   |   |   |   |--- Tax >  52.00\n",
      "|   |   |   |   |   |   |   |   |--- Weight <= 1080.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [12995.00]\n",
      "|   |   |   |   |   |   |   |   |--- Weight >  1080.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Doors <= 4.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [15750.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Doors >  4.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [14900.00]\n",
      "|   |   |   |   |   |--- KM >  36849.50\n",
      "|   |   |   |   |   |   |--- Doors <= 4.00\n",
      "|   |   |   |   |   |   |   |--- KM <= 55029.50\n",
      "|   |   |   |   |   |   |   |   |--- Fuel_Type_Diesel <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [13950.00]\n",
      "|   |   |   |   |   |   |   |   |--- Fuel_Type_Diesel >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 44348.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [13750.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [13950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  44348.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [13500.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  55029.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 72037.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [12950.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  72037.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [13750.00]\n",
      "|   |   |   |   |   |   |--- Doors >  4.00\n",
      "|   |   |   |   |   |   |   |--- value: [15250.00]\n",
      "|   |   |   |   |--- HP >  103.50\n",
      "|   |   |   |   |   |--- KM <= 44933.50\n",
      "|   |   |   |   |   |   |--- Doors <= 4.00\n",
      "|   |   |   |   |   |   |   |--- Age <= 29.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 30335.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Tax <= 52.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [16750.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Tax >  52.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 27131.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [16250.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  27131.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15950.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  30335.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 27.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [17495.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  27.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [16950.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  29.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [17950.00]\n",
      "|   |   |   |   |   |   |--- Doors >  4.00\n",
      "|   |   |   |   |   |   |   |--- KM <= 35344.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 32252.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 27.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [18450.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  27.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [19950.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [18950.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  32252.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [17950.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  35344.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 38063.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [16500.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  38063.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [15999.00]\n",
      "|   |   |   |   |   |--- KM >  44933.50\n",
      "|   |   |   |   |   |   |--- Weight <= 1090.00\n",
      "|   |   |   |   |   |   |   |--- value: [13250.00]\n",
      "|   |   |   |   |   |   |--- Weight >  1090.00\n",
      "|   |   |   |   |   |   |   |--- KM <= 52756.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [15500.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  52756.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [15950.00]\n",
      "|   |   |   |--- Weight >  1175.00\n",
      "|   |   |   |   |--- HP <= 100.00\n",
      "|   |   |   |   |   |--- KM <= 92125.00\n",
      "|   |   |   |   |   |   |--- Weight <= 1262.50\n",
      "|   |   |   |   |   |   |   |--- Age <= 28.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [17950.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  28.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [18600.00]\n",
      "|   |   |   |   |   |   |--- Weight >  1262.50\n",
      "|   |   |   |   |   |   |   |--- value: [19000.00]\n",
      "|   |   |   |   |   |--- KM >  92125.00\n",
      "|   |   |   |   |   |   |--- value: [16950.00]\n",
      "|   |   |   |   |--- HP >  100.00\n",
      "|   |   |   |   |   |--- Tax <= 167.00\n",
      "|   |   |   |   |   |   |--- value: [20500.00]\n",
      "|   |   |   |   |   |--- Tax >  167.00\n",
      "|   |   |   |   |   |   |--- value: [22250.00]\n",
      "|   |--- HP >  113.00\n",
      "|   |   |--- Age <= 5.50\n",
      "|   |   |   |--- KM <= 2000.50\n",
      "|   |   |   |   |--- value: [32500.00]\n",
      "|   |   |   |--- KM >  2000.50\n",
      "|   |   |   |   |--- value: [31000.00]\n",
      "|   |   |--- Age >  5.50\n",
      "|   |   |   |--- Doors <= 4.00\n",
      "|   |   |   |   |--- Age <= 25.00\n",
      "|   |   |   |   |   |--- value: [19950.00]\n",
      "|   |   |   |   |--- Age >  25.00\n",
      "|   |   |   |   |   |--- KM <= 28500.00\n",
      "|   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |--- value: [22000.00]\n",
      "|   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |--- value: [21500.00]\n",
      "|   |   |   |   |   |--- KM >  28500.00\n",
      "|   |   |   |   |   |   |--- KM <= 34065.50\n",
      "|   |   |   |   |   |   |   |--- value: [22750.00]\n",
      "|   |   |   |   |   |   |--- KM >  34065.50\n",
      "|   |   |   |   |   |   |   |--- value: [22500.00]\n",
      "|   |   |   |--- Doors >  4.00\n",
      "|   |   |   |   |--- KM <= 11626.50\n",
      "|   |   |   |   |   |--- value: [22950.00]\n",
      "|   |   |   |   |--- KM >  11626.50\n",
      "|   |   |   |   |   |--- Tax <= 149.00\n",
      "|   |   |   |   |   |   |--- Weight <= 1252.50\n",
      "|   |   |   |   |   |   |   |--- value: [23950.00]\n",
      "|   |   |   |   |   |   |--- Weight >  1252.50\n",
      "|   |   |   |   |   |   |   |--- value: [23000.00]\n",
      "|   |   |   |   |   |--- Tax >  149.00\n",
      "|   |   |   |   |   |   |--- value: [24950.00]\n",
      "|--- Age >  32.50\n",
      "|   |--- Age <= 56.50\n",
      "|   |   |--- Age <= 44.50\n",
      "|   |   |   |--- KM <= 130667.50\n",
      "|   |   |   |   |--- Weight <= 1027.50\n",
      "|   |   |   |   |   |--- Doors <= 4.00\n",
      "|   |   |   |   |   |   |--- KM <= 28817.00\n",
      "|   |   |   |   |   |   |   |--- Age <= 38.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 22957.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [10950.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  22957.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [11500.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  38.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [11950.00]\n",
      "|   |   |   |   |   |   |--- KM >  28817.00\n",
      "|   |   |   |   |   |   |   |--- Age <= 43.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 37160.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 35311.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  35311.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9900.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  37160.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 41784.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 37699.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10950.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  37699.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  41784.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 60227.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9950.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  60227.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10950.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  43.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 69148.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [11925.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [11750.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  69148.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [10950.00]\n",
      "|   |   |   |   |   |--- Doors >  4.00\n",
      "|   |   |   |   |   |   |--- value: [12750.00]\n",
      "|   |   |   |   |--- Weight >  1027.50\n",
      "|   |   |   |   |   |--- KM <= 48571.00\n",
      "|   |   |   |   |   |   |--- CC <= 1350.00\n",
      "|   |   |   |   |   |   |   |--- value: [10500.00]\n",
      "|   |   |   |   |   |   |--- CC >  1350.00\n",
      "|   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 30314.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [14950.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  30314.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1042.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 44224.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10950.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  44224.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11495.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1042.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Doors <= 3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [12200.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Doors >  3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- Weight <= 1057.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1042.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Doors <= 4.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Doors >  4.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [14350.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1042.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight <= 1050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [14950.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight >  1050.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [14990.00]\n",
      "|   |   |   |   |   |   |   |   |--- Weight >  1057.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1077.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Tax <= 52.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [13950.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Tax >  52.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1077.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 42029.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  42029.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |--- KM >  48571.00\n",
      "|   |   |   |   |   |   |--- Tax <= 203.50\n",
      "|   |   |   |   |   |   |   |--- Automatic <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- Tax <= 44.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9940.00]\n",
      "|   |   |   |   |   |   |   |   |--- Tax >  44.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight <= 1045.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight >  1045.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 88422.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  88422.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |--- Automatic >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 68838.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [13950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [14950.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  68838.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [11790.00]\n",
      "|   |   |   |   |   |   |--- Tax >  203.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 93477.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [13500.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  93477.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [14750.00]\n",
      "|   |   |   |--- KM >  130667.50\n",
      "|   |   |   |   |--- Tax <= 137.00\n",
      "|   |   |   |   |   |--- value: [4750.00]\n",
      "|   |   |   |   |--- Tax >  137.00\n",
      "|   |   |   |   |   |--- value: [9500.00]\n",
      "|   |   |--- Age >  44.50\n",
      "|   |   |   |--- HP <= 79.00\n",
      "|   |   |   |   |--- KM <= 108507.50\n",
      "|   |   |   |   |   |--- Age <= 50.50\n",
      "|   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |--- value: [10995.00]\n",
      "|   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 84270.00\n",
      "|   |   |   |   |   |   |   |   |--- Weight <= 1122.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [8695.00]\n",
      "|   |   |   |   |   |   |   |   |--- Weight >  1122.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [8750.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  84270.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [8950.00]\n",
      "|   |   |   |   |   |--- Age >  50.50\n",
      "|   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |--- Weight <= 1127.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [9950.00]\n",
      "|   |   |   |   |   |   |   |--- Weight >  1127.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [10950.00]\n",
      "|   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |--- value: [11450.00]\n",
      "|   |   |   |   |--- KM >  108507.50\n",
      "|   |   |   |   |   |--- Age <= 55.00\n",
      "|   |   |   |   |   |   |--- KM <= 161734.00\n",
      "|   |   |   |   |   |   |   |--- Age <= 52.50\n",
      "|   |   |   |   |   |   |   |   |--- Tax <= 124.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7750.00]\n",
      "|   |   |   |   |   |   |   |   |--- Tax >  124.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [8250.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  52.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 128153.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9950.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  128153.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Doors <= 4.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9450.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Doors >  4.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8950.00]\n",
      "|   |   |   |   |   |   |--- KM >  161734.00\n",
      "|   |   |   |   |   |   |   |--- KM <= 175069.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [7750.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  175069.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 187083.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7000.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  187083.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 52.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [6400.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  52.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [6500.00]\n",
      "|   |   |   |   |   |--- Age >  55.00\n",
      "|   |   |   |   |   |   |--- value: [5150.00]\n",
      "|   |   |   |--- HP >  79.00\n",
      "|   |   |   |   |--- Weight <= 1042.50\n",
      "|   |   |   |   |   |--- KM <= 88737.00\n",
      "|   |   |   |   |   |   |--- Doors <= 3.50\n",
      "|   |   |   |   |   |   |   |--- Age <= 47.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 53657.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 32560.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  32560.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8950.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  53657.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9950.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  47.50\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 50.50\n",
      "|   |   |   |   |   |   |   |   |   |--- HP <= 91.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [11950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- HP >  91.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 33444.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  33444.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |--- Age >  50.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 73250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 54961.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  54961.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  73250.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 55.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10250.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  55.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11450.00]\n",
      "|   |   |   |   |   |   |--- Doors >  3.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 46239.00\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 53.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [10500.00]\n",
      "|   |   |   |   |   |   |   |   |--- Age >  53.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [10995.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  46239.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 63379.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 50974.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [11750.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  50974.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [11950.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  63379.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 53.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9930.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  53.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 74497.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10950.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  74497.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11950.00]\n",
      "|   |   |   |   |   |--- KM >  88737.00\n",
      "|   |   |   |   |   |   |--- Age <= 54.00\n",
      "|   |   |   |   |   |   |   |--- Weight <= 1030.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [8950.00]\n",
      "|   |   |   |   |   |   |   |--- Weight >  1030.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [7900.00]\n",
      "|   |   |   |   |   |   |--- Age >  54.00\n",
      "|   |   |   |   |   |   |   |--- value: [10500.00]\n",
      "|   |   |   |   |--- Weight >  1042.50\n",
      "|   |   |   |   |   |--- KM <= 2655.00\n",
      "|   |   |   |   |   |   |--- value: [7500.00]\n",
      "|   |   |   |   |   |--- KM >  2655.00\n",
      "|   |   |   |   |   |   |--- KM <= 49768.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 49520.50\n",
      "|   |   |   |   |   |   |   |   |--- CC <= 1350.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [13750.00]\n",
      "|   |   |   |   |   |   |   |   |--- CC >  1350.00\n",
      "|   |   |   |   |   |   |   |   |   |--- HP <= 103.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Tax <= 52.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9950.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Tax >  52.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- HP >  103.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 46145.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  46145.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |--- KM >  49520.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [18950.00]\n",
      "|   |   |   |   |   |   |--- KM >  49768.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 121770.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 50184.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9650.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  50184.50\n",
      "|   |   |   |   |   |   |   |   |   |--- CC <= 1800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 101388.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  101388.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- CC >  1800.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [12450.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  121770.00\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 48.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9250.00]\n",
      "|   |   |   |   |   |   |   |   |--- Age >  48.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1077.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1077.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9900.00]\n",
      "|   |--- Age >  56.50\n",
      "|   |   |--- Age <= 68.50\n",
      "|   |   |   |--- KM <= 115857.50\n",
      "|   |   |   |   |--- Weight <= 1017.50\n",
      "|   |   |   |   |   |--- KM <= 57279.50\n",
      "|   |   |   |   |   |   |--- KM <= 32717.50\n",
      "|   |   |   |   |   |   |   |--- Weight <= 1007.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [9450.00]\n",
      "|   |   |   |   |   |   |   |--- Weight >  1007.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 25089.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9500.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  25089.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 29794.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10000.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  29794.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9950.00]\n",
      "|   |   |   |   |   |   |--- KM >  32717.50\n",
      "|   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 48490.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Tax <= 44.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Tax >  44.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9995.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  48490.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 62.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  62.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9250.00]\n",
      "|   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 66.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 35641.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9250.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  35641.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 38000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8250.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  38000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |--- Age >  66.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7900.00]\n",
      "|   |   |   |   |   |--- KM >  57279.50\n",
      "|   |   |   |   |   |   |--- KM <= 59632.00\n",
      "|   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [7500.00]\n",
      "|   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [7750.00]\n",
      "|   |   |   |   |   |   |--- KM >  59632.00\n",
      "|   |   |   |   |   |   |   |--- KM <= 64132.00\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 65.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 60988.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  60988.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8500.00]\n",
      "|   |   |   |   |   |   |   |   |--- Age >  65.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9500.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  64132.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 89686.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 77542.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 74944.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  74944.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  77542.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 65.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  65.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- KM >  89686.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 92327.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8750.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  92327.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Doors <= 3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8450.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Doors >  3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8750.00]\n",
      "|   |   |   |   |--- Weight >  1017.50\n",
      "|   |   |   |   |   |--- Weight <= 1060.00\n",
      "|   |   |   |   |   |   |--- KM <= 50556.00\n",
      "|   |   |   |   |   |   |   |--- Age <= 62.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 42607.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Automatic <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 36055.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10900.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  36055.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Automatic >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10750.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  42607.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Doors <= 3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 45964.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9750.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  45964.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- Doors >  3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- HP <= 98.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10500.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- HP >  98.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10450.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  62.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 33557.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 66.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 27917.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9245.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  27917.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10450.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  66.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10500.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- KM >  33557.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 46750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 38033.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  38033.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  46750.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- CC <= 1450.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10495.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- CC >  1450.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9950.00]\n",
      "|   |   |   |   |   |   |--- KM >  50556.00\n",
      "|   |   |   |   |   |   |   |--- KM <= 86879.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 54739.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Doors <= 4.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 52758.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  52758.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- Doors >  4.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7350.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  54739.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Doors <= 4.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 63.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  63.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |--- Doors >  4.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |--- KM >  86879.50\n",
      "|   |   |   |   |   |   |   |   |--- Weight <= 1037.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 62.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight <= 1027.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8950.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight >  1027.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8750.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  62.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9250.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- Weight >  1037.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Automatic <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 106018.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  106018.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- Automatic >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9750.00]\n",
      "|   |   |   |   |   |--- Weight >  1060.00\n",
      "|   |   |   |   |   |   |--- KM <= 98074.00\n",
      "|   |   |   |   |   |   |   |--- KM <= 28317.00\n",
      "|   |   |   |   |   |   |   |   |--- Automatic <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [10950.00]\n",
      "|   |   |   |   |   |   |   |   |--- Automatic >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [12500.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  28317.00\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 67.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1080.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 66.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  66.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1080.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 60.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  60.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |--- Age >  67.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight <= 1072.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7750.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight >  1072.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 58583.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  58583.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |--- KM >  98074.00\n",
      "|   |   |   |   |   |   |   |--- Tax <= 52.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [8250.00]\n",
      "|   |   |   |   |   |   |   |--- Tax >  52.00\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 59.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9950.00]\n",
      "|   |   |   |   |   |   |   |   |--- Age >  59.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 111484.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight <= 1087.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight >  1087.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9895.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  111484.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Tax <= 141.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9950.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Tax >  141.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9450.00]\n",
      "|   |   |   |--- KM >  115857.50\n",
      "|   |   |   |   |--- KM <= 138378.00\n",
      "|   |   |   |   |   |--- KM <= 117782.00\n",
      "|   |   |   |   |   |   |--- KM <= 116500.00\n",
      "|   |   |   |   |   |   |   |--- value: [8000.00]\n",
      "|   |   |   |   |   |   |--- KM >  116500.00\n",
      "|   |   |   |   |   |   |   |--- Fuel_Type_Petrol <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [6950.00]\n",
      "|   |   |   |   |   |   |   |--- Fuel_Type_Petrol >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [7250.00]\n",
      "|   |   |   |   |   |--- KM >  117782.00\n",
      "|   |   |   |   |   |   |--- Weight <= 1057.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 125948.00\n",
      "|   |   |   |   |   |   |   |   |--- Doors <= 4.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7950.00]\n",
      "|   |   |   |   |   |   |   |   |--- Doors >  4.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7995.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  125948.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [7750.00]\n",
      "|   |   |   |   |   |   |--- Weight >  1057.50\n",
      "|   |   |   |   |   |   |   |--- Weight <= 1117.50\n",
      "|   |   |   |   |   |   |   |   |--- Doors <= 4.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Automatic <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8750.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Automatic >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8600.00]\n",
      "|   |   |   |   |   |   |   |   |--- Doors >  4.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 67.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9450.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  67.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8950.00]\n",
      "|   |   |   |   |   |   |   |--- Weight >  1117.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 132600.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 61.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7500.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  61.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7950.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  132600.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1127.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1127.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8950.00]\n",
      "|   |   |   |   |--- KM >  138378.00\n",
      "|   |   |   |   |   |--- HP <= 100.00\n",
      "|   |   |   |   |   |   |--- Doors <= 3.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 183542.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 147944.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 139984.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [6900.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  139984.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7350.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  147944.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1065.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [6950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1065.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [6900.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  183542.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 197575.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7500.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  197575.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7900.00]\n",
      "|   |   |   |   |   |   |--- Doors >  3.50\n",
      "|   |   |   |   |   |   |   |--- Age <= 63.00\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 58.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6950.00]\n",
      "|   |   |   |   |   |   |   |   |--- Age >  58.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6900.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  63.00\n",
      "|   |   |   |   |   |   |   |   |--- Weight <= 1117.50\n",
      "|   |   |   |   |   |   |   |   |   |--- HP <= 79.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [5751.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- HP >  79.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [5950.00]\n",
      "|   |   |   |   |   |   |   |   |--- Weight >  1117.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6250.00]\n",
      "|   |   |   |   |   |--- HP >  100.00\n",
      "|   |   |   |   |   |   |--- Weight <= 1057.50\n",
      "|   |   |   |   |   |   |   |--- value: [7950.00]\n",
      "|   |   |   |   |   |   |--- Weight >  1057.50\n",
      "|   |   |   |   |   |   |   |--- value: [8450.00]\n",
      "|   |   |--- Age >  68.50\n",
      "|   |   |   |--- KM <= 100859.50\n",
      "|   |   |   |   |--- Weight <= 1067.50\n",
      "|   |   |   |   |   |--- KM <= 48613.50\n",
      "|   |   |   |   |   |   |--- KM <= 46453.50\n",
      "|   |   |   |   |   |   |   |--- Age <= 72.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 16966.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7250.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  16966.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 19772.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 18008.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8500.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  18008.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10845.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  19772.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight <= 1035.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight >  1035.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |--- Age >  72.50\n",
      "|   |   |   |   |   |   |   |   |--- Automatic <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 45458.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 39056.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  39056.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  45458.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8500.00]\n",
      "|   |   |   |   |   |   |   |   |--- Automatic >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8500.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8950.00]\n",
      "|   |   |   |   |   |   |--- KM >  46453.50\n",
      "|   |   |   |   |   |   |   |--- value: [10500.00]\n",
      "|   |   |   |   |   |--- KM >  48613.50\n",
      "|   |   |   |   |   |   |--- Weight <= 1025.00\n",
      "|   |   |   |   |   |   |   |--- KM <= 56607.00\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 78.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7750.00]\n",
      "|   |   |   |   |   |   |   |   |--- Age >  78.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9500.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  56607.00\n",
      "|   |   |   |   |   |   |   |   |--- Weight <= 1007.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 74.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  74.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7750.00]\n",
      "|   |   |   |   |   |   |   |   |--- Weight >  1007.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Tax <= 44.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [5950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Tax >  44.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 71581.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  71581.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |--- Weight >  1025.00\n",
      "|   |   |   |   |   |   |   |--- Age <= 71.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 82181.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 52904.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8750.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  52904.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- HP <= 98.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- HP >  98.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |--- KM >  82181.50\n",
      "|   |   |   |   |   |   |   |   |   |--- HP <= 98.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- HP >  98.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight <= 1042.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7950.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight >  1042.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |--- Age >  71.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 64653.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 51263.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7500.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  51263.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |--- KM >  64653.50\n",
      "|   |   |   |   |   |   |   |   |   |--- CC <= 1450.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 73188.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  73188.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- CC >  1450.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 70081.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  70081.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |--- Weight >  1067.50\n",
      "|   |   |   |   |   |--- Weight <= 1109.50\n",
      "|   |   |   |   |   |   |--- Weight <= 1077.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 93453.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 62433.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 48177.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 72.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7450.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  72.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  48177.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 55138.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  55138.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |--- KM >  62433.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 66396.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 64975.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  64975.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7400.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  66396.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 70193.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  70193.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |--- KM >  93453.50\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 79.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9500.00]\n",
      "|   |   |   |   |   |   |   |   |--- Age >  79.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9250.00]\n",
      "|   |   |   |   |   |   |--- Weight >  1077.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 94889.50\n",
      "|   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1082.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 78.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  78.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8750.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1082.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Doors <= 4.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10000.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Doors >  4.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9900.00]\n",
      "|   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Automatic <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 74.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9200.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  74.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Automatic >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- HP <= 108.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- HP >  108.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [6900.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  94889.50\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 76.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 97289.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8900.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  97289.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8950.00]\n",
      "|   |   |   |   |   |   |   |   |--- Age >  76.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6990.00]\n",
      "|   |   |   |   |   |--- Weight >  1109.50\n",
      "|   |   |   |   |   |   |--- KM <= 87025.00\n",
      "|   |   |   |   |   |   |   |--- KM <= 20047.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [6950.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  20047.00\n",
      "|   |   |   |   |   |   |   |   |--- Doors <= 3.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [8950.00]\n",
      "|   |   |   |   |   |   |   |   |--- Doors >  3.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 46190.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8950.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  46190.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7450.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |--- KM >  87025.00\n",
      "|   |   |   |   |   |   |   |--- value: [6950.00]\n",
      "|   |   |   |--- KM >  100859.50\n",
      "|   |   |   |   |--- Weight <= 1047.50\n",
      "|   |   |   |   |   |--- KM <= 144778.00\n",
      "|   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |--- Age <= 73.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [5950.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  73.00\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 77.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6750.00]\n",
      "|   |   |   |   |   |   |   |   |--- Age >  77.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6500.00]\n",
      "|   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 103560.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 102150.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7750.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  102150.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7950.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  103560.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 111118.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 73.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [6500.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  73.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Doors <= 3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [6750.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Doors >  3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [6800.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  111118.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1040.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 72.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7350.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  72.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1040.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [6950.00]\n",
      "|   |   |   |   |   |--- KM >  144778.00\n",
      "|   |   |   |   |   |   |--- Doors <= 4.50\n",
      "|   |   |   |   |   |   |   |--- Age <= 76.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [5600.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  76.00\n",
      "|   |   |   |   |   |   |   |   |--- HP <= 98.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5800.00]\n",
      "|   |   |   |   |   |   |   |   |--- HP >  98.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5950.00]\n",
      "|   |   |   |   |   |   |--- Doors >  4.50\n",
      "|   |   |   |   |   |   |   |--- value: [6750.00]\n",
      "|   |   |   |   |--- Weight >  1047.50\n",
      "|   |   |   |   |   |--- KM <= 199116.50\n",
      "|   |   |   |   |   |   |--- CC <= 1800.00\n",
      "|   |   |   |   |   |   |   |--- KM <= 165693.50\n",
      "|   |   |   |   |   |   |   |   |--- Weight <= 1089.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1080.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 79.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  79.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1080.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 110579.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8450.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  110579.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8500.00]\n",
      "|   |   |   |   |   |   |   |   |--- Weight >  1089.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Fuel_Type_Petrol <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [6450.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Fuel_Type_Petrol >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [6500.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  165693.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [5950.00]\n",
      "|   |   |   |   |   |   |--- CC >  1800.00\n",
      "|   |   |   |   |   |   |   |--- Weight <= 1137.50\n",
      "|   |   |   |   |   |   |   |   |--- Doors <= 3.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 151254.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Doors <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7950.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Doors >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [6900.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  151254.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8950.00]\n",
      "|   |   |   |   |   |   |   |   |--- Doors >  3.50\n",
      "|   |   |   |   |   |   |   |   |   |--- HP <= 81.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8500.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- HP >  81.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [9500.00]\n",
      "|   |   |   |   |   |   |   |--- Weight >  1137.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 171460.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 74.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7750.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  74.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7950.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  171460.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6950.00]\n",
      "|   |   |   |   |   |--- KM >  199116.50\n",
      "|   |   |   |   |   |   |--- Age <= 78.50\n",
      "|   |   |   |   |   |   |   |--- Weight <= 1117.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [6750.00]\n",
      "|   |   |   |   |   |   |   |--- Weight >  1117.50\n",
      "|   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5900.00]\n",
      "|   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6000.00]\n",
      "|   |   |   |   |   |   |--- Age >  78.50\n",
      "|   |   |   |   |   |   |   |--- value: [6950.00]\n",
      "\n",
      "Index(['Age', 'KM', 'HP', 'Met_Color', 'Automatic', 'CC', 'Doors', 'Tax',\n",
      "       'Weight', 'Fuel_Type_Diesel', 'Fuel_Type_Petrol'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "feat_importance = regTree.tree_.compute_feature_importances(normalize=True)\n",
    "print(\"feat importance = \" + str(feat_importance))\n",
    "\n",
    "tree_rules_2 = export_text(regTree, feature_names=list(train_X.columns))\n",
    "print(tree_rules_2)\n",
    "\n",
    "print(X.columns)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ii. Compare the prediction errors of the training and validation sets by examining their RMS error and by plotting the two boxplots. How does the predictive performance of the validation set compare to the training set? Why does this occur?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Regression statistics\n",
      "\n",
      "                      Mean Error (ME) : 0.0000\n",
      "       Root Mean Squared Error (RMSE) : 1101.0597\n",
      "            Mean Absolute Error (MAE) : 800.4555\n",
      "          Mean Percentage Error (MPE) : -1.0396\n",
      "Mean Absolute Percentage Error (MAPE) : 7.8185\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "                      Mean Error (ME) : 4.9461\n",
      "       Root Mean Squared Error (RMSE) : 1230.7881\n",
      "            Mean Absolute Error (MAE) : 943.1207\n",
      "          Mean Percentage Error (MPE) : -1.2718\n",
      "Mean Absolute Percentage Error (MAPE) : 9.4966\n"
     ]
    }
   ],
   "source": [
    "regressionSummary(train_y, regTree.predict(train_X))\n",
    "regressionSummary(valid_y, regTree.predict(valid_X))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# iii. How might we achieve better validation predictive performance at the expense of training performance?\n",
    "We could obtain better validation predictive performance by making the training set smaller. In this way, we can avoid overtraining and overfitting on the data.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# iv. Create a smaller tree by using GridSearchCV() with cv = 5 to find a fine-tuned tree. Compared to the full-grown tree, what is the predictive performance on the validation set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial parameters:  {'max_depth': 5, 'min_impurity_decrease': 0.001, 'min_samples_split': 10}\n",
      "Improved parameters:  {'max_depth': 5, 'min_impurity_decrease': 0, 'min_samples_split': 14}\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "                      Mean Error (ME) : 0.0000\n",
      "       Root Mean Squared Error (RMSE) : 1101.0597\n",
      "            Mean Absolute Error (MAE) : 800.4555\n",
      "          Mean Percentage Error (MPE) : -1.0396\n",
      "Mean Absolute Percentage Error (MAPE) : 7.8185\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "                      Mean Error (ME) : 4.9461\n",
      "       Root Mean Squared Error (RMSE) : 1230.7881\n",
      "            Mean Absolute Error (MAE) : 943.1207\n",
      "          Mean Percentage Error (MPE) : -1.2718\n",
      "Mean Absolute Percentage Error (MAPE) : 9.4966\n"
     ]
    }
   ],
   "source": [
    "# user grid search to find optimized tree\n",
    "param_grid = {\n",
    "'max_depth': [5, 10, 15, 20, 25],\n",
    "'min_impurity_decrease': [0, 0.001, 0.005, 0.01],\n",
    "'min_samples_split': [10, 20, 30, 40, 50],\n",
    "}\n",
    "\n",
    "gridSearch = GridSearchCV(DecisionTreeRegressor(), param_grid, cv=5, n_jobs=-1)\n",
    "gridSearch.fit(train_X, train_y)\n",
    "\n",
    "print('Initial parameters: ', gridSearch.best_params_)\n",
    "\n",
    "param_grid = {\n",
    "'max_depth': [3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n",
    "'min_impurity_decrease': [0, 0.001, 0.002, 0.003, 0.005, 0.006, 0.007, 0.008],\n",
    "'min_samples_split': [14, 15, 16, 18, 20, ],\n",
    "}\n",
    "\n",
    "gridSearch = GridSearchCV(DecisionTreeRegressor(), param_grid, cv=5, n_jobs=-1)\n",
    "gridSearch.fit(train_X, train_y)\n",
    "\n",
    "print('Improved parameters: ', gridSearch.best_params_)\n",
    "regTree = gridSearch.best_estimator_\n",
    "\n",
    "regressionSummary(train_y, regTree.predict(train_X))\n",
    "regressionSummary(valid_y, regTree.predict(valid_X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_df = toyotaCorolla_df\n",
    "\n",
    "toyota_b = toyotaCorolla_df\n",
    "toyota_b['Price'] = pd.cut(tmp_df.Price, bins=20, labels=False, include_lowest=True)\n",
    "\n",
    "predictors = ['Age', 'KM', 'Fuel_Type', 'HP', 'Met_Color', 'Automatic', 'CC', 'Doors', 'Tax', 'Weight']\n",
    "outcome = 'Price'\n",
    "\n",
    "X = pd.get_dummies(toyota_b[predictors], drop_first=True)\n",
    "y = toyota_b[outcome]\n",
    "train_X, valid_X, train_y, valid_y = train_test_split(X, y, test_size=0.4, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Regression statistics\n",
      "\n",
      "               Mean Error (ME) : 0.0000\n",
      "Root Mean Squared Error (RMSE) : 0.0000\n",
      "     Mean Absolute Error (MAE) : 0.0000\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "               Mean Error (ME) : 0.0052\n",
      "Root Mean Squared Error (RMSE) : 1.1041\n",
      "     Mean Absolute Error (MAE) : 0.7774\n",
      "|--- Age <= 32.50\n",
      "|   |--- HP <= 113.00\n",
      "|   |   |--- Age <= 21.00\n",
      "|   |   |   |--- Weight <= 1127.50\n",
      "|   |   |   |   |--- KM <= 24172.50\n",
      "|   |   |   |   |   |--- Weight <= 1102.50\n",
      "|   |   |   |   |   |   |--- HP <= 97.50\n",
      "|   |   |   |   |   |   |   |--- value: [8.00]\n",
      "|   |   |   |   |   |   |--- HP >  97.50\n",
      "|   |   |   |   |   |   |   |--- Age <= 11.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [9.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  11.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [10.00]\n",
      "|   |   |   |   |   |--- Weight >  1102.50\n",
      "|   |   |   |   |   |   |--- KM <= 16711.50\n",
      "|   |   |   |   |   |   |   |--- Age <= 4.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [11.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  4.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 6577.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  6577.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 16.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  16.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [11.00]\n",
      "|   |   |   |   |   |   |--- KM >  16711.50\n",
      "|   |   |   |   |   |   |   |--- Weight <= 1115.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 18702.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  18702.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [8.00]\n",
      "|   |   |   |   |   |   |   |--- Weight >  1115.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [10.00]\n",
      "|   |   |   |   |--- KM >  24172.50\n",
      "|   |   |   |   |   |--- value: [8.00]\n",
      "|   |   |   |--- Weight >  1127.50\n",
      "|   |   |   |   |--- Age <= 13.50\n",
      "|   |   |   |   |   |--- KM <= 10920.50\n",
      "|   |   |   |   |   |   |--- Age <= 10.00\n",
      "|   |   |   |   |   |   |   |--- value: [12.00]\n",
      "|   |   |   |   |   |   |--- Age >  10.00\n",
      "|   |   |   |   |   |   |   |--- value: [11.00]\n",
      "|   |   |   |   |   |--- KM >  10920.50\n",
      "|   |   |   |   |   |   |--- Age <= 10.50\n",
      "|   |   |   |   |   |   |   |--- value: [13.00]\n",
      "|   |   |   |   |   |   |--- Age >  10.50\n",
      "|   |   |   |   |   |   |   |--- value: [14.00]\n",
      "|   |   |   |   |--- Age >  13.50\n",
      "|   |   |   |   |   |--- Weight <= 1142.50\n",
      "|   |   |   |   |   |   |--- KM <= 30555.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 18654.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [11.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  18654.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [10.00]\n",
      "|   |   |   |   |   |   |--- KM >  30555.50\n",
      "|   |   |   |   |   |   |   |--- Age <= 18.50\n",
      "|   |   |   |   |   |   |   |   |--- HP <= 103.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9.00]\n",
      "|   |   |   |   |   |   |   |   |--- HP >  103.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [8.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  18.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [10.00]\n",
      "|   |   |   |   |   |--- Weight >  1142.50\n",
      "|   |   |   |   |   |   |--- KM <= 57049.50\n",
      "|   |   |   |   |   |   |   |--- Weight <= 1262.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [11.00]\n",
      "|   |   |   |   |   |   |   |--- Weight >  1262.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [12.00]\n",
      "|   |   |   |   |   |   |--- KM >  57049.50\n",
      "|   |   |   |   |   |   |   |--- value: [10.00]\n",
      "|   |   |--- Age >  21.00\n",
      "|   |   |   |--- Weight <= 1175.00\n",
      "|   |   |   |   |--- HP <= 103.50\n",
      "|   |   |   |   |   |--- KM <= 36849.50\n",
      "|   |   |   |   |   |   |--- Age <= 28.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 32750.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 31205.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 28353.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 16208.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [8.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  16208.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  28353.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  31205.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  32750.50\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 24.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [8.00]\n",
      "|   |   |   |   |   |   |   |   |--- Age >  24.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9.00]\n",
      "|   |   |   |   |   |   |--- Age >  28.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 27263.50\n",
      "|   |   |   |   |   |   |   |   |--- Tax <= 52.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.00]\n",
      "|   |   |   |   |   |   |   |   |--- Tax >  52.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 31.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  31.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [8.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  27263.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [6.00]\n",
      "|   |   |   |   |   |--- KM >  36849.50\n",
      "|   |   |   |   |   |   |--- Doors <= 4.00\n",
      "|   |   |   |   |   |   |   |--- value: [6.00]\n",
      "|   |   |   |   |   |   |--- Doors >  4.00\n",
      "|   |   |   |   |   |   |   |--- value: [7.00]\n",
      "|   |   |   |   |--- HP >  103.50\n",
      "|   |   |   |   |   |--- KM <= 35861.00\n",
      "|   |   |   |   |   |   |--- Doors <= 4.00\n",
      "|   |   |   |   |   |   |   |--- Age <= 29.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 33382.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [8.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  33382.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  29.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [9.00]\n",
      "|   |   |   |   |   |   |--- Doors >  4.00\n",
      "|   |   |   |   |   |   |   |--- Age <= 29.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 28408.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1122.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [10.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1122.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [11.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  28408.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [10.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  29.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [9.00]\n",
      "|   |   |   |   |   |--- KM >  35861.00\n",
      "|   |   |   |   |   |   |--- Weight <= 1090.00\n",
      "|   |   |   |   |   |   |   |--- value: [6.00]\n",
      "|   |   |   |   |   |   |--- Weight >  1090.00\n",
      "|   |   |   |   |   |   |   |--- Age <= 26.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [7.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  26.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [8.00]\n",
      "|   |   |   |--- Weight >  1175.00\n",
      "|   |   |   |   |--- HP <= 100.00\n",
      "|   |   |   |   |   |--- KM <= 92125.00\n",
      "|   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |--- value: [10.00]\n",
      "|   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |--- Age <= 28.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [9.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  28.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [10.00]\n",
      "|   |   |   |   |   |--- KM >  92125.00\n",
      "|   |   |   |   |   |   |--- value: [8.00]\n",
      "|   |   |   |   |--- HP >  100.00\n",
      "|   |   |   |   |   |--- Fuel_Type_Diesel <= 0.50\n",
      "|   |   |   |   |   |   |--- value: [11.00]\n",
      "|   |   |   |   |   |--- Fuel_Type_Diesel >  0.50\n",
      "|   |   |   |   |   |   |--- value: [12.00]\n",
      "|   |--- HP >  113.00\n",
      "|   |   |--- Tax <= 258.50\n",
      "|   |   |   |--- Age <= 20.50\n",
      "|   |   |   |   |--- Weight <= 1295.00\n",
      "|   |   |   |   |   |--- value: [13.00]\n",
      "|   |   |   |   |--- Weight >  1295.00\n",
      "|   |   |   |   |   |--- KM <= 19126.50\n",
      "|   |   |   |   |   |   |--- value: [14.00]\n",
      "|   |   |   |   |   |--- KM >  19126.50\n",
      "|   |   |   |   |   |   |--- value: [13.00]\n",
      "|   |   |   |--- Age >  20.50\n",
      "|   |   |   |   |--- Age <= 25.00\n",
      "|   |   |   |   |   |--- value: [11.00]\n",
      "|   |   |   |   |--- Age >  25.00\n",
      "|   |   |   |   |   |--- Age <= 30.50\n",
      "|   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |--- value: [12.00]\n",
      "|   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |--- value: [13.00]\n",
      "|   |   |   |   |   |--- Age >  30.50\n",
      "|   |   |   |   |   |   |--- value: [12.00]\n",
      "|   |   |--- Tax >  258.50\n",
      "|   |   |   |--- KM <= 2000.50\n",
      "|   |   |   |   |--- value: [19.00]\n",
      "|   |   |   |--- KM >  2000.50\n",
      "|   |   |   |   |--- value: [18.00]\n",
      "|--- Age >  32.50\n",
      "|   |--- Age <= 56.50\n",
      "|   |   |--- Age <= 44.50\n",
      "|   |   |   |--- KM <= 130667.50\n",
      "|   |   |   |   |--- Weight <= 1027.50\n",
      "|   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |--- Age <= 39.00\n",
      "|   |   |   |   |   |   |   |--- KM <= 41784.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 22957.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  22957.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  41784.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |--- Age >  39.00\n",
      "|   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |--- KM <= 28811.50\n",
      "|   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |--- KM >  28811.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 37160.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 35311.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  35311.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  37160.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 63465.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 41.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  41.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  63465.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |--- Weight >  1027.50\n",
      "|   |   |   |   |   |--- KM <= 48571.00\n",
      "|   |   |   |   |   |   |--- CC <= 1350.00\n",
      "|   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |--- CC >  1350.00\n",
      "|   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 30314.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  30314.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1042.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 38.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  38.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1042.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- Weight <= 1057.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1042.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Doors <= 4.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Doors >  4.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [7.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1042.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7.00]\n",
      "|   |   |   |   |   |   |   |   |--- Weight >  1057.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1067.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 42.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  42.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [6.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1067.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 14162.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  14162.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |--- KM >  48571.00\n",
      "|   |   |   |   |   |   |--- Tax <= 44.00\n",
      "|   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |--- Tax >  44.00\n",
      "|   |   |   |   |   |   |   |--- Tax <= 203.50\n",
      "|   |   |   |   |   |   |   |   |--- Automatic <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 55000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  55000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 81924.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  81924.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- Automatic >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 56359.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [7.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  56359.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight <= 1107.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight >  1107.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [6.00]\n",
      "|   |   |   |   |   |   |   |--- Tax >  203.50\n",
      "|   |   |   |   |   |   |   |   |--- Weight <= 1187.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.00]\n",
      "|   |   |   |   |   |   |   |   |--- Weight >  1187.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7.00]\n",
      "|   |   |   |--- KM >  130667.50\n",
      "|   |   |   |   |--- Tax <= 137.00\n",
      "|   |   |   |   |   |--- value: [0.00]\n",
      "|   |   |   |   |--- Tax >  137.00\n",
      "|   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |--- Age >  44.50\n",
      "|   |   |   |--- KM <= 99594.00\n",
      "|   |   |   |   |--- HP <= 103.50\n",
      "|   |   |   |   |   |--- Doors <= 4.00\n",
      "|   |   |   |   |   |   |--- KM <= 9657.00\n",
      "|   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |--- KM >  9657.00\n",
      "|   |   |   |   |   |   |   |--- Age <= 48.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  48.50\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 49.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 33444.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  33444.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 44003.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  44003.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- Age >  49.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 54.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 51974.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  51974.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  54.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 60484.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  60484.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |--- Doors >  4.00\n",
      "|   |   |   |   |   |   |--- Age <= 53.50\n",
      "|   |   |   |   |   |   |   |--- Fuel_Type_Diesel <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 82608.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 51.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  51.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  82608.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |   |--- Fuel_Type_Diesel >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |--- Age >  53.50\n",
      "|   |   |   |   |   |   |   |--- HP <= 91.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [6.00]\n",
      "|   |   |   |   |   |   |   |--- HP >  91.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |--- HP >  103.50\n",
      "|   |   |   |   |   |--- KM <= 49768.50\n",
      "|   |   |   |   |   |   |--- KM <= 49520.50\n",
      "|   |   |   |   |   |   |   |--- Weight <= 1042.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 46239.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  46239.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |   |--- Weight >  1042.50\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 51.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 48.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 45.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [6.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  45.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  48.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 47686.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  47686.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |   |   |--- Age >  51.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 54.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 53.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  53.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  54.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |--- KM >  49520.50\n",
      "|   |   |   |   |   |   |   |--- value: [10.00]\n",
      "|   |   |   |   |   |--- KM >  49768.50\n",
      "|   |   |   |   |   |   |--- KM <= 50184.50\n",
      "|   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |--- KM >  50184.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 56676.00\n",
      "|   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1077.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1077.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  56676.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 66295.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 50.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  50.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 53.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  53.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- KM >  66295.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 69251.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 54.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [6.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  54.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  69251.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 46.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  46.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |--- KM >  99594.00\n",
      "|   |   |   |   |--- KM <= 175069.50\n",
      "|   |   |   |   |   |--- Age <= 55.50\n",
      "|   |   |   |   |   |   |--- Weight <= 1152.50\n",
      "|   |   |   |   |   |   |   |--- Age <= 54.50\n",
      "|   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1129.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1129.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 48.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  48.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 101659.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  101659.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |--- Age >  54.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |--- Weight >  1152.50\n",
      "|   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |--- Age >  55.50\n",
      "|   |   |   |   |   |   |--- value: [0.00]\n",
      "|   |   |   |   |--- KM >  175069.50\n",
      "|   |   |   |   |   |--- value: [1.00]\n",
      "|   |--- Age >  56.50\n",
      "|   |   |--- Age <= 68.50\n",
      "|   |   |   |--- KM <= 138378.00\n",
      "|   |   |   |   |--- KM <= 74797.50\n",
      "|   |   |   |   |   |--- Weight <= 1022.50\n",
      "|   |   |   |   |   |   |--- KM <= 57279.50\n",
      "|   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 44527.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  44527.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 59.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |--- Age >  59.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 38000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 32717.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  32717.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  38000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |--- KM >  57279.50\n",
      "|   |   |   |   |   |   |   |--- Age <= 61.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  61.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 59632.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  59632.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 65045.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  65045.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 67000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  67000.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |--- Weight >  1022.50\n",
      "|   |   |   |   |   |   |--- KM <= 36461.50\n",
      "|   |   |   |   |   |   |   |--- Weight <= 1080.00\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 63.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |   |--- Age >  63.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Age <= 64.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Age >  64.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 24766.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  24766.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |--- Weight >  1080.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |--- KM >  36461.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 73918.50\n",
      "|   |   |   |   |   |   |   |   |--- Weight <= 1095.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1060.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 62.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  62.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1060.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight <= 1067.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight >  1067.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |--- Weight >  1095.00\n",
      "|   |   |   |   |   |   |   |   |   |--- CC <= 1450.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- CC >  1450.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  73918.50\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 61.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |   |   |--- Age >  61.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |--- KM >  74797.50\n",
      "|   |   |   |   |   |--- Weight <= 1060.00\n",
      "|   |   |   |   |   |   |--- KM <= 109011.50\n",
      "|   |   |   |   |   |   |   |--- Weight <= 1017.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 77542.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 74944.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  74944.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  77542.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 80541.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  80541.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 65.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  65.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |--- Weight >  1017.50\n",
      "|   |   |   |   |   |   |   |   |--- HP <= 98.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 85935.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 60.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  60.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  85935.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |--- HP >  98.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 88573.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 80628.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  80628.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  88573.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 92863.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  92863.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |--- KM >  109011.50\n",
      "|   |   |   |   |   |   |   |--- Weight <= 1027.50\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 66.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |--- Age >  66.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |--- Weight >  1027.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |--- Weight >  1060.00\n",
      "|   |   |   |   |   |   |--- KM <= 96824.00\n",
      "|   |   |   |   |   |   |   |--- KM <= 94549.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 74937.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  74937.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 76991.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  76991.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 67.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  67.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  94549.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |   |   |   |   |--- KM >  96824.00\n",
      "|   |   |   |   |   |   |   |--- Weight <= 1112.00\n",
      "|   |   |   |   |   |   |   |   |--- Tax <= 52.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |--- Tax >  52.00\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1080.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1080.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 67.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  67.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |--- Weight >  1112.00\n",
      "|   |   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 124696.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [1.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  124696.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- HP <= 91.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Doors <= 4.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Doors >  4.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- HP >  91.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |--- KM >  138378.00\n",
      "|   |   |   |   |--- HP <= 100.00\n",
      "|   |   |   |   |   |--- Doors <= 3.50\n",
      "|   |   |   |   |   |   |--- KM <= 183542.00\n",
      "|   |   |   |   |   |   |   |--- KM <= 147944.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 139984.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  139984.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  147944.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [1.00]\n",
      "|   |   |   |   |   |   |--- KM >  183542.00\n",
      "|   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |--- Doors >  3.50\n",
      "|   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |--- Fuel_Type_Diesel <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [1.00]\n",
      "|   |   |   |   |   |   |   |--- Fuel_Type_Diesel >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [0.00]\n",
      "|   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |--- value: [1.00]\n",
      "|   |   |   |   |--- HP >  100.00\n",
      "|   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |--- Age >  68.50\n",
      "|   |   |   |--- KM <= 100859.50\n",
      "|   |   |   |   |--- Weight <= 1037.50\n",
      "|   |   |   |   |   |--- KM <= 19772.00\n",
      "|   |   |   |   |   |   |--- KM <= 18008.00\n",
      "|   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |--- KM >  18008.00\n",
      "|   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |--- KM >  19772.00\n",
      "|   |   |   |   |   |   |--- KM <= 78844.50\n",
      "|   |   |   |   |   |   |   |--- Tax <= 44.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [1.00]\n",
      "|   |   |   |   |   |   |   |--- Tax >  44.00\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 71.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1012.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1012.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 70.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  70.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1.00]\n",
      "|   |   |   |   |   |   |   |   |--- Age >  71.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1025.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 77293.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  77293.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1025.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 75.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  75.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |--- KM >  78844.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 82389.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [1.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  82389.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 89906.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  89906.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 94415.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [1.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  94415.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |--- Weight >  1037.50\n",
      "|   |   |   |   |   |--- Weight <= 1042.50\n",
      "|   |   |   |   |   |   |--- Age <= 78.50\n",
      "|   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |--- Age >  78.50\n",
      "|   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |--- Weight >  1042.50\n",
      "|   |   |   |   |   |   |--- Automatic <= 0.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 19473.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [1.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  19473.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 57947.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1072.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 76.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  76.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1072.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight <= 1094.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight >  1094.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- KM >  57947.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 68615.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 70.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  70.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  68615.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 69971.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  69971.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13\n",
      "|   |   |   |   |   |   |--- Automatic >  0.50\n",
      "|   |   |   |   |   |   |   |--- Age <= 75.50\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 73.50\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 65035.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age <= 70.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Age >  70.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  65035.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |--- Age >  73.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |--- Age >  75.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 33482.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  33482.00\n",
      "|   |   |   |   |   |   |   |   |   |--- HP <= 108.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Tax <= 77.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Tax >  77.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- HP >  108.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight <= 1087.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight >  1087.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1.00]\n",
      "|   |   |   |--- KM >  100859.50\n",
      "|   |   |   |   |--- Weight <= 1047.50\n",
      "|   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |--- KM <= 146096.50\n",
      "|   |   |   |   |   |   |   |--- value: [1.00]\n",
      "|   |   |   |   |   |   |--- KM >  146096.50\n",
      "|   |   |   |   |   |   |   |--- value: [0.00]\n",
      "|   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |--- KM <= 133318.50\n",
      "|   |   |   |   |   |   |   |--- KM <= 111118.00\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 103560.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  103560.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.00]\n",
      "|   |   |   |   |   |   |   |--- KM >  111118.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |--- KM >  133318.50\n",
      "|   |   |   |   |   |   |   |--- value: [1.00]\n",
      "|   |   |   |   |--- Weight >  1047.50\n",
      "|   |   |   |   |   |--- KM <= 199116.50\n",
      "|   |   |   |   |   |   |--- CC <= 1800.00\n",
      "|   |   |   |   |   |   |   |--- Weight <= 1089.50\n",
      "|   |   |   |   |   |   |   |   |--- KM <= 165693.50\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight <= 1072.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM <= 102961.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- KM >  102961.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |--- Weight >  1072.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |--- KM >  165693.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.00]\n",
      "|   |   |   |   |   |   |   |--- Weight >  1089.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [1.00]\n",
      "|   |   |   |   |   |   |--- CC >  1800.00\n",
      "|   |   |   |   |   |   |   |--- Met_Color <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- Weight <= 1145.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |--- Weight >  1145.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.00]\n",
      "|   |   |   |   |   |   |   |--- Met_Color >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- Age <= 73.00\n",
      "|   |   |   |   |   |   |   |   |   |--- KM <= 148802.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [1.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- KM >  148802.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight <= 1132.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- Weight >  1132.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |--- Age >  73.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |--- KM >  199116.50\n",
      "|   |   |   |   |   |   |--- value: [1.00]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# create a regressor object\n",
    "regTree_2 = DecisionTreeRegressor(random_state = 1) \n",
    "  \n",
    "# fit the regressor with X and Y data\n",
    "regTree_2.fit(train_X, train_y)\n",
    "\n",
    "regressionSummary(train_y, regTree_2.predict(train_X))\n",
    "regressionSummary(valid_y, regTree_2.predict(valid_X))\n",
    "\n",
    "tree_rules = export_text(regTree_2, feature_names=list(train_X.columns))\n",
    "print(tree_rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial parameters:  {'max_depth': 5, 'min_impurity_decrease': 0.001, 'min_samples_split': 10}\n",
      "Improved parameters:  {'max_depth': 10, 'min_impurity_decrease': 0.007, 'min_samples_split': 14}\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "               Mean Error (ME) : -0.0000\n",
      "Root Mean Squared Error (RMSE) : 0.7722\n",
      "     Mean Absolute Error (MAE) : 0.6005\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "               Mean Error (ME) : 0.0318\n",
      "Root Mean Squared Error (RMSE) : 0.9191\n",
      "     Mean Absolute Error (MAE) : 0.7104\n",
      "|--- Age <= 32.50\n",
      "|   |--- HP <= 113.00\n",
      "|   |   |--- Age <= 21.00\n",
      "|   |   |   |--- Weight <= 1127.50\n",
      "|   |   |   |   |--- KM <= 24172.50\n",
      "|   |   |   |   |   |--- value: [9.58]\n",
      "|   |   |   |   |--- KM >  24172.50\n",
      "|   |   |   |   |   |--- value: [8.00]\n",
      "|   |   |   |--- Weight >  1127.50\n",
      "|   |   |   |   |--- Age <= 13.50\n",
      "|   |   |   |   |   |--- value: [12.25]\n",
      "|   |   |   |   |--- Age >  13.50\n",
      "|   |   |   |   |   |--- value: [10.43]\n",
      "|   |   |--- Age >  21.00\n",
      "|   |   |   |--- Weight <= 1175.00\n",
      "|   |   |   |   |--- HP <= 103.50\n",
      "|   |   |   |   |   |--- KM <= 36849.50\n",
      "|   |   |   |   |   |   |--- value: [7.47]\n",
      "|   |   |   |   |   |--- KM >  36849.50\n",
      "|   |   |   |   |   |   |--- value: [6.11]\n",
      "|   |   |   |   |--- HP >  103.50\n",
      "|   |   |   |   |   |--- KM <= 35861.00\n",
      "|   |   |   |   |   |   |--- value: [9.09]\n",
      "|   |   |   |   |   |--- KM >  35861.00\n",
      "|   |   |   |   |   |   |--- value: [7.62]\n",
      "|   |   |   |--- Weight >  1175.00\n",
      "|   |   |   |   |--- value: [9.62]\n",
      "|   |--- HP >  113.00\n",
      "|   |   |--- value: [13.67]\n",
      "|--- Age >  32.50\n",
      "|   |--- Age <= 56.50\n",
      "|   |   |--- Age <= 44.50\n",
      "|   |   |   |--- KM <= 130667.50\n",
      "|   |   |   |   |--- Weight <= 1027.50\n",
      "|   |   |   |   |   |--- value: [4.40]\n",
      "|   |   |   |   |--- Weight >  1027.50\n",
      "|   |   |   |   |   |--- value: [5.47]\n",
      "|   |   |   |--- KM >  130667.50\n",
      "|   |   |   |   |--- value: [1.50]\n",
      "|   |   |--- Age >  44.50\n",
      "|   |   |   |--- KM <= 99594.00\n",
      "|   |   |   |   |--- HP <= 103.50\n",
      "|   |   |   |   |   |--- value: [3.75]\n",
      "|   |   |   |   |--- HP >  103.50\n",
      "|   |   |   |   |   |--- KM <= 49768.50\n",
      "|   |   |   |   |   |   |--- KM <= 49520.50\n",
      "|   |   |   |   |   |   |   |--- value: [4.86]\n",
      "|   |   |   |   |   |   |--- KM >  49520.50\n",
      "|   |   |   |   |   |   |   |--- value: [10.00]\n",
      "|   |   |   |   |   |--- KM >  49768.50\n",
      "|   |   |   |   |   |   |--- value: [4.22]\n",
      "|   |   |   |--- KM >  99594.00\n",
      "|   |   |   |   |--- KM <= 175069.50\n",
      "|   |   |   |   |   |--- Age <= 55.50\n",
      "|   |   |   |   |   |   |--- value: [3.00]\n",
      "|   |   |   |   |   |--- Age >  55.50\n",
      "|   |   |   |   |   |   |--- value: [0.00]\n",
      "|   |   |   |   |--- KM >  175069.50\n",
      "|   |   |   |   |   |--- value: [1.00]\n",
      "|   |--- Age >  56.50\n",
      "|   |   |--- Age <= 68.50\n",
      "|   |   |   |--- KM <= 138378.00\n",
      "|   |   |   |   |--- KM <= 74797.50\n",
      "|   |   |   |   |   |--- Weight <= 1022.50\n",
      "|   |   |   |   |   |   |--- value: [2.76]\n",
      "|   |   |   |   |   |--- Weight >  1022.50\n",
      "|   |   |   |   |   |   |--- value: [3.32]\n",
      "|   |   |   |   |--- KM >  74797.50\n",
      "|   |   |   |   |   |--- value: [2.78]\n",
      "|   |   |   |--- KM >  138378.00\n",
      "|   |   |   |   |--- value: [1.31]\n",
      "|   |   |--- Age >  68.50\n",
      "|   |   |   |--- KM <= 100859.50\n",
      "|   |   |   |   |--- value: [2.19]\n",
      "|   |   |   |--- KM >  100859.50\n",
      "|   |   |   |   |--- value: [1.60]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# user grid search to find optimized tree\n",
    "param_grid = {\n",
    "'max_depth': [5, 10, 15, 20, 25],\n",
    "'min_impurity_decrease': [0, 0.001, 0.005, 0.01],\n",
    "'min_samples_split': [10, 20, 30, 40, 50],\n",
    "}\n",
    "\n",
    "gridSearch = GridSearchCV(DecisionTreeRegressor(), param_grid, cv=5, n_jobs=-1)\n",
    "gridSearch.fit(train_X, train_y)\n",
    "\n",
    "print('Initial parameters: ', gridSearch.best_params_)\n",
    "\n",
    "param_grid = {\n",
    "'max_depth': [3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n",
    "'min_impurity_decrease': [0, 0.001, 0.002, 0.003, 0.005, 0.006, 0.007, 0.008],\n",
    "'min_samples_split': [14, 15, 16, 18, 20, ],\n",
    "}\n",
    "\n",
    "gridSearch = GridSearchCV(DecisionTreeRegressor(), param_grid, cv=5, n_jobs=-1)\n",
    "gridSearch.fit(train_X, train_y)\n",
    "\n",
    "print('Improved parameters: ', gridSearch.best_params_)\n",
    "regTree = gridSearch.best_estimator_\n",
    "\n",
    "regressionSummary(train_y, regTree.predict(train_X))\n",
    "regressionSummary(valid_y, regTree.predict(valid_X))\n",
    "\n",
    "tree_rules = export_text(regTree, feature_names=list(train_X.columns))\n",
    "print(tree_rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feat importance = [8.34116999e-01 6.49751986e-02 4.89798659e-02 4.35859661e-03\n",
      " 9.71243610e-04 1.66871248e-03 3.21335501e-03 1.23398252e-02\n",
      " 2.86637608e-02 5.42556408e-04 1.69886849e-04]\n",
      "feat importance = [0.89325319 0.03900886 0.05005252 0.         0.         0.\n",
      " 0.         0.         0.01768544 0.         0.        ]\n",
      "Index(['Age', 'KM', 'HP', 'Met_Color', 'Automatic', 'CC', 'Doors', 'Tax',\n",
      "       'Weight', 'Fuel_Type_Diesel', 'Fuel_Type_Petrol'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# i\n",
    "# RT\n",
    "feat_importance = regTree_2.tree_.compute_feature_importances(normalize=True)\n",
    "print (\"feat importance = \" + str(feat_importance))\n",
    "      \n",
    "# GridSearchCV\n",
    "feat_importance = regTree.tree_.compute_feature_importances(normalize=True)\n",
    "print (\"feat importance = \" + str(feat_importance))\n",
    "\n",
    "print(X.columns)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top Predictors\n",
    "Full-tree:          GridSearchCV:\n",
    "Age                 Age\n",
    "KM                  HP\n",
    "HP                  KM\n",
    "Weight              Weight\n",
    "\n",
    "# Structure\n",
    "Rather than being a tree that is heavily leaned, using bins leads to a tree that appears relatively balanced.\n",
    "\n",
    "# Size\n",
    "Using bins leads to a tree that is significantly smaller.\n",
    "\n",
    "# Explain why \n",
    "Using bins reduces the number of variables. Since price is now sorted into a finite number of categorical bins rather than a continuous value, the number of variables can be greatly reduced to fit the outcome prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.60344828]\n",
      "[1]\n",
      "(0       (5.7, 6.65]\n",
      "1       (5.7, 6.65]\n",
      "2       (5.7, 6.65]\n",
      "3       (6.65, 7.6]\n",
      "4       (5.7, 6.65]\n",
      "           ...     \n",
      "1431    (1.9, 2.85]\n",
      "1432    (3.8, 4.75]\n",
      "1433    (1.9, 2.85]\n",
      "1434    (1.9, 2.85]\n",
      "1435    (0.95, 1.9]\n",
      "Name: Price, Length: 1436, dtype: category\n",
      "Categories (20, interval[float64, right]): [(-0.019, 0.95] < (0.95, 1.9] < (1.9, 2.85] < (2.85, 3.8] ... (15.2, 16.15] < (16.15, 17.1] < (17.1, 18.05] < (18.05, 19.0]], array([-0.019,  0.95 ,  1.9  ,  2.85 ,  3.8  ,  4.75 ,  5.7  ,  6.65 ,\n",
      "        7.6  ,  8.55 ,  9.5  , 10.45 , 11.4  , 12.35 , 13.3  , 14.25 ,\n",
      "       15.2  , 16.15 , 17.1  , 18.05 , 19.   ]))\n"
     ]
    }
   ],
   "source": [
    "# ii. Predict the price, using the smaller RT and CT, of a used Toyota Corolla with the specifications listed in Table 9.10.\n",
    "sample_car = pd.DataFrame(columns=X.columns)\n",
    "# sample_car.loc[0] = [77, 117000, 'Petrol', 110, 'No', 5, 100, 'No', 3, 'Yes', 'No' 'No', 'No', 'No', 'Yes']\n",
    "sample_car.loc[0] = [77, 117000, 110, 0, 0, 1, 5, 100, 50, 0, 1]\n",
    "\n",
    "fullClassTree = DecisionTreeClassifier(random_state=1, min_samples_leaf=50, max_depth=7)\n",
    "fullClassTree.fit(train_X, train_y)\n",
    "\n",
    "RT_pred = regTree.predict(sample_car)\n",
    "CT_pred = fullClassTree.predict(sample_car)\n",
    "\n",
    "print(RT_pred)\n",
    "print(CT_pred)\n",
    "\n",
    "tmp_df = toyotaCorolla_df\n",
    "print(pd.cut(tmp_df.Price, bins=20, retbins=True))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# iii Compare the predictions in terms of the predictors that were used, the magnitude of the difference between the two predictions, and the advantages and disadvantages of the two methods.\n",
    "\n",
    "Regression Tree with bins: $9,120\n",
    "Classification Tree with bins: $6,650\n",
    "\n",
    "In this instance, the Regression Tree performed better since it was better trained. Our regression model made use of GridSearchCV to find parameters that functioned well for this given set. This is opposed to the Classification Tree, which did not have any additional tuning.\n",
    "\n",
    "Each tree functions in different ways, and the application and underlying data will determine which tree is best for which situation.\n"
   ]
  }
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