{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c5ba4fe",
   "metadata": {},
   "source": [
    "# Decision Trees - Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07494ffb",
   "metadata": {},
   "source": [
    "Decision Trees are supervised machine learning algorithms that are used for both regression and classification tasks. Trees are powerful algorithms that can handle complex datasets. \n",
    "\n",
    "In the previous notebook, we learnt to predict the continous values with decision trees (regression). This notebook is for classification. \n",
    "\n",
    "Different to other algorithms, here are two things that trees are very particular about:\n",
    "\n",
    "Here are 7 interesting facts about decision trees:\n",
    "\n",
    "* They do not need the numerical input data to be scaled. Whatever the numerical values are, decision trees don't care. \n",
    "\n",
    "* Decision trees handle categorical features in the raw text format (Scikit-Learn doesn't support this, TensorFlow's trees implementation does).\n",
    "\n",
    "* Different to other complex learning algorithms, the results of decision trees can be interpreted. It's fair to say that decision trees are not blackbox type models. \n",
    "* While most models will suffer from missing values, decision trees are okay with them.\n",
    "* Trees can handle imbalanced datasets. You will only have to adjust the weights of the classes.\n",
    "* Trees can provide the feature importances or how much each feature contributed to the model training results.\n",
    "* Trees are the basic building blocks of ensemble methods such as random forests and gradient boosting machines.\n",
    "\n",
    "The way decision trees works is like the series of if/else questions. Let's say that you want to make a decision of the car to buy. In order to get the right car to buy, you could go on and evaluate the level of the safety, the number of sits and doors by asking series of if like questions. \n",
    "\n",
    "Here is the structure of the decision trees.\n",
    "\n",
    "![Decision Trees.png](../../../../images/ml-fundamentals/4XijLSXib.png)\n",
    "\n",
    "\n",
    "A well-known downside of decision trees is that they tend to overfit the data easily(pretty much assumed they will always overfit at first). One way to overcome overfitting is to reduce the maximum depth of the decision tree (refered to as `max_depth`hyperparameter) in decision trees. We will see other techniques to avoid overfitting. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4e2c5a6",
   "metadata": {},
   "source": [
    "## Decision Trees for Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae5e8372",
   "metadata": {},
   "source": [
    "### Contents\n",
    "\n",
    "* [1 - Imports]\n",
    "* [2 - Loading the data]\n",
    "* [3 - Exploratory Analysis]\n",
    "* [4 - Preprocessing the data]\n",
    "* [5 - Training Decision Trees]\n",
    "* [6 - Evaluating Decision Trees]\n",
    "* [7 - Improving Decision Trees]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99058d12",
   "metadata": {},
   "source": [
    "## 1 - Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "df278c5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35699c38",
   "metadata": {},
   "source": [
    "## 2 - Loading the data\n",
    "\n",
    "In this classification task with decision trees, we will use a car dataset that is avilable at [OpenML](https://www.openml.org/d/21) to predict the car acceptability given the information about the car. We will load it with Sklearn `fetch_openml` function. \n",
    "\n",
    "The version of the data we are fetching is 2. In the version 1 of the dataset, the target class had 4 classes (unacc, acc, good, vgood) but in the second version, the majority class is Positive(P) whereas the rest are Negative(P). If you want to see version 1, you can change version parameter in a cell below. \n",
    "\n",
    "\n",
    "Here are the informations about the features:\n",
    "\n",
    "* *buying*: The buying price of the car(vhigh, high, med, low)\n",
    "* maint: The maintainance price of the car(high, high, med, low)\n",
    "* *doors*: The number of doors (2,3,4,5more)\n",
    "* *persons*: The number of persons that can be carried the car. They are 2, 4, and more. \n",
    "* *lug_boot*: The size of the luggage boot (small, med, big)\n",
    "* *safety*: Estimated safefy of the car(low, med, high)\n",
    "* *BinaryClass(target feature)*: The car acceptability class. Either positive(P) or negative(N).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d3083946",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "\n",
    "car_data = fetch_openml(name='car', version=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "46c09852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils._bunch.Bunch"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(car_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "154ed67e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': '991',\n",
       " 'name': 'car',\n",
       " 'version': '2',\n",
       " 'description_version': '1',\n",
       " 'format': 'ARFF',\n",
       " 'upload_date': '2014-10-04T22:44:31',\n",
       " 'licence': 'Public',\n",
       " 'url': 'https://api.openml.org/data/v1/download/53525/car.arff',\n",
       " 'parquet_url': 'http://openml1.win.tue.nl/dataset991/dataset_991.pq',\n",
       " 'file_id': '53525',\n",
       " 'default_target_attribute': 'binaryClass',\n",
       " 'tag': ['derived',\n",
       "  'mythbusting_1',\n",
       "  'study_1',\n",
       "  'study_15',\n",
       "  'study_20',\n",
       "  'study_41',\n",
       "  'study_7'],\n",
       " 'visibility': 'public',\n",
       " 'minio_url': 'http://openml1.win.tue.nl/dataset991/dataset_991.pq',\n",
       " 'status': 'active',\n",
       " 'processing_date': '2020-11-20 20:17:54',\n",
       " 'md5_checksum': '49c57b793eef1b8e55f297e5e019fdbf'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data.details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d93da4ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data.details['version'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "69e438fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Author**:   \n",
      "**Source**: Unknown - Date unknown  \n",
      "**Please cite**:   \n",
      "\n",
      "Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converted by Quan Sun.\n",
      "\n",
      "Downloaded from openml.org.\n"
     ]
    }
   ],
   "source": [
    "# Data description \n",
    "\n",
    "print(car_data.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d7180991",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying feature names\n",
    "\n",
    "car_data.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e3ba092e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buying</th>\n",
       "      <th>maint</th>\n",
       "      <th>doors</th>\n",
       "      <th>persons</th>\n",
       "      <th>lug_boot</th>\n",
       "      <th>safety</th>\n",
       "      <th>binaryClass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>low</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>med</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>high</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>med</td>\n",
       "      <td>low</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>med</td>\n",
       "      <td>med</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  buying  maint doors persons lug_boot safety binaryClass\n",
       "0  vhigh  vhigh     2       2    small    low           P\n",
       "1  vhigh  vhigh     2       2    small    med           P\n",
       "2  vhigh  vhigh     2       2    small   high           P\n",
       "3  vhigh  vhigh     2       2      med    low           P\n",
       "4  vhigh  vhigh     2       2      med    med           P"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the whole dataframe\n",
    "\n",
    "car_data = car_data.frame\n",
    "car_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2f05b049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(car_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c492fd93",
   "metadata": {},
   "source": [
    "## 3 - Exploratory Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9906083",
   "metadata": {},
   "source": [
    "Before doing exploratory analysis, let's get the training and test data. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dff2de1d",
   "metadata": {},
   "source": [
    "### Splitting Data into Training and Test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "98d74144",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of training data is: 1555 \n",
      "The size of testing data is: 173\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_data, test_data = train_test_split(car_data, test_size=0.1,random_state=20)\n",
    "\n",
    "print('The size of training data is: {} \\nThe size of testing data is: {}'.format(len(train_data), len(test_data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab1127e7",
   "metadata": {},
   "source": [
    "### Checking Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "753bb539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buying</th>\n",
       "      <th>maint</th>\n",
       "      <th>doors</th>\n",
       "      <th>persons</th>\n",
       "      <th>lug_boot</th>\n",
       "      <th>safety</th>\n",
       "      <th>binaryClass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1555</td>\n",
       "      <td>1555</td>\n",
       "      <td>1555</td>\n",
       "      <td>1555</td>\n",
       "      <td>1555</td>\n",
       "      <td>1555</td>\n",
       "      <td>1555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>med</td>\n",
       "      <td>low</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>med</td>\n",
       "      <td>low</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>402</td>\n",
       "      <td>392</td>\n",
       "      <td>393</td>\n",
       "      <td>521</td>\n",
       "      <td>522</td>\n",
       "      <td>528</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       buying maint doors persons lug_boot safety binaryClass\n",
       "count    1555  1555  1555    1555     1555   1555        1555\n",
       "unique      4     4     4       3        3      3           2\n",
       "top       med   low     2       2      med    low           P\n",
       "freq      402   392   393     521      522    528        1097"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking summary statistics\n",
    "\n",
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd6e747c",
   "metadata": {},
   "source": [
    "### Checking Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f0e4fb67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "buying         0\n",
       "maint          0\n",
       "doors          0\n",
       "persons        0\n",
       "lug_boot       0\n",
       "safety         0\n",
       "binaryClass    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking missing values\n",
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4977a1a1",
   "metadata": {},
   "source": [
    "We don't have any missing values. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5da8fe66",
   "metadata": {},
   "source": [
    "### Checking Categorical Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff90081a",
   "metadata": {},
   "source": [
    "Let's inspect some categorical features that are in the dataset, almost all re. Let's see that!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7c5e465c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "med      402\n",
       "vhigh    387\n",
       "high     385\n",
       "low      381\n",
       "Name: buying, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['buying'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d88aefd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='buying', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='buying')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "83d9f295",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='buying', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='buying', hue='binaryClass')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a986c09",
   "metadata": {},
   "source": [
    "As you can see above, the majority class in the buying price is median(`med`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dc1eadb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low      392\n",
       "med      390\n",
       "high     387\n",
       "vhigh    386\n",
       "Name: maint, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['maint'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "433c25f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='maint', ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='maint')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "110b9abd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='maint', ylabel='count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='maint', hue='binaryClass')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1b2c63b",
   "metadata": {},
   "source": [
    "Let's also check what's in doors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4298a48f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2        393\n",
       "4        393\n",
       "5more    389\n",
       "3        380\n",
       "Name: doors, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['doors'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "477a5113",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='doors', ylabel='count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='doors')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f77d35bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='doors', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='doors', hue='binaryClass')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a9ae2a9",
   "metadata": {},
   "source": [
    "Nothing stunning in the maintenance cost. All cars share the same maintenance costs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7dc9bb1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2       521\n",
       "more    521\n",
       "4       513\n",
       "Name: persons, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['persons'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0965bc7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='persons', ylabel='count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='persons', hue='binaryClass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f8fbe08a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "med      522\n",
       "small    517\n",
       "big      516\n",
       "Name: lug_boot, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['lug_boot'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d7e678ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='lug_boot', ylabel='count'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABNYAAANBCAYAAAAssTPKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAABKkElEQVR4nO3de7SWdZ3//9fmfHKDIOwNiXhCFAVp0HSnOYySiMZkMZVGIuboaNiElDFMiooVZXlIh2zsgPZNhpkmtfRrniixFBAxEsUYdSzsKxuaFLaH5Lh/f7S8f7PzyEfk3sDjsda91r6v67qv+33t1mLXs+tQ09zc3BwAAAAAYIu0qfYAAAAAALA9EtYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAu2qPUBrsHnz5jzzzDPZZZddUlNTU+1xAAAAAKiS5ubmPP/88+nXr1/atHnjc9KEtSTPPPNM+vfvX+0xAAAAAGglnn766ey+++5vuI2wlmSXXXZJ8udfWG1tbZWnAQAAAKBampqa0r9//0oveiPCWlK5/LO2tlZYAwAAAOAt3S7MwwsAAAAAoICwBgAAAAAFhDUAAAAAKOAeawAAAACtRHNzczZu3JhNmzZVe5QdVtu2bdOuXbu3dA+1NyOsAQAAALQC69evz8qVK/PSSy9Ve5QdXpcuXdK3b9906NDhbe1HWAMAAACoss2bN+epp55K27Zt069fv3To0GGrnFFFS83NzVm/fn3+8Ic/5KmnnsrAgQPTpk35ndKENQAAAIAqW79+fTZv3pz+/funS5cu1R5nh9a5c+e0b98+v/vd77J+/fp06tSpeF8eXgAAAADQSryds6d467bW79l/WgAAAABQQFgDAAAAgALCGgAAAEArN2LEiEyaNOl11++555658sort9k8W8P2OPNfEtYAAAAAtnOLFi3KmWeeWe0xKpqamvKFL3wh+++/fzp16pT6+vqMHDkyN954Y5qbm6s93lbjqaAAAAAA27nevXu/49+xfv36dOjQ4U23W7NmTY488sisXbs2X/ziF3PooYemXbt2mTdvXj7/+c/n6KOPTo8ePd7xebcFZ6wBAAAAbAc2btyYc845J927d89uu+2WCy64oHL2119eVllTU5PvfOc7+dCHPpQuXbpk4MCB+clPflJZv2nTppx++unZa6+90rlz5wwaNCjf+MY3WnzfhAkTcuKJJ+ZLX/pS+vXrl0GDBmX69Ok56KCDXjXbsGHDcsEFFyRJ/vmf/zm//e1vs3Dhwpx66qkZPHhw9ttvv5xxxhlZsmRJunXr9prHd/nll2fIkCHp2rVr+vfvn0996lN54YUXKut/97vfZcyYMdl1113TtWvXHHjggbntttuSJM8991zGjRuX3r17p3Pnzhk4cGBmzZpV9oveAs5YAwAAANgOXH/99Tn99NPzwAMP5MEHH8yZZ56ZPfbYI2ecccZrbn/xxRfn0ksvzde+9rVcffXVGTduXH73u9+lZ8+e2bx5c3bffff88Ic/TK9evXL//ffnzDPPTN++ffPRj360so+5c+emtrY2d911V5Kke/fuufjii7No0aIceuihSZJf/epXefjhh3PjjTdm8+bNmTNnTsaNG5d+/fq9aqbXi2pJ0qZNm1x11VXZa6+98t///d/51Kc+lc9//vP55je/mSSZOHFi1q9fn3vvvTddu3bNsmXLKvu74IILsmzZsvz0pz/NbrvtlieeeCJ/+tOfyn7RW0BYAwAAANgO9O/fP1dccUVqamoyaNCgLF26NFdcccXrhrUJEybk5JNPTpJ8+ctfzlVXXZUHHnggxx13XNq3b5+LL764su1ee+2V+fPn5z/+4z9ahLWuXbvmO9/5TotLQEeNGpVZs2ZVwtqsWbPy13/919l7772zevXqPPfcc9l///23+Pj+98MZ9txzz3zxi1/MWWedVQlrK1asyNixYzNkyJAkyd57713ZfsWKFXn3u9+dQw45pPL5bcGloAAAAADbgcMPPzw1NTWV9w0NDXn88cezadOm19x+6NChlZ+7du2a2trarF69urJs5syZGT58eHr37p1u3brl2muvzYoVK1rsY8iQIa+6r9oZZ5yRf/u3f8vLL7+c9evXZ/bs2fnkJz+ZJG/rwQR33313jjnmmLzrXe/KLrvsklNOOSV//OMf89JLLyVJ/vEf/zFf/OIXc8QRR+TCCy/Mww8/XPns2WefnTlz5mTYsGH5/Oc/n/vvv794ji0hrAEAAADsgNq3b9/ifU1NTTZv3pwkmTNnTj73uc/l9NNPz5133pklS5bktNNOy/r161t8pmvXrq/a75gxY9KxY8fcdNNNueWWW7Jhw4b83d/9XZI/P0ShR48e+c1vfrNFs/72t7/NBz7wgQwdOjQ/+tGPsnjx4sycOTNJKjP9/d//ff77v/87p5xySpYuXZpDDjkkV199dZJk9OjR+d3vfpdzzz03zzzzTI455ph87nOf26IZSghrAAAAANuBhQsXtni/YMGCDBw4MG3btt3ifd13331573vfm0996lN597vfnX333TdPPvnkW/psu3btcuqpp2bWrFmZNWtWTjrppHTu3DnJn++TdtJJJ+WGG27IM88886rPvvDCC9m4ceOrli9evDibN2/OZZddlsMPPzz77bffa36+f//+Oeuss3LjjTfms5/9bL797W9X1vXu3TunnnpqfvCDH+TKK6/Mtdde+1Z/HcXcYw0AAABgO7BixYpMnjw5//AP/5CHHnooV199dS677LKifQ0cODDf//73c8cdd2SvvfbK//k//yeLFi3KXnvt9ZY+//d///c54IADkvw50v1vX/rSl3LPPffksMMOy5e+9KUccsghad++fX7xi19kxowZWbRoUXr06NHiM/vuu282bNiQq6++OmPGjMl9992Xb33rWy22mTRpUkaPHp399tsvzz33XH7+859XZpg2bVqGDx+eAw88MOvWrcutt95aWfdOEtYAAAAAtgPjx4/Pn/70p7znPe9J27Zt85nPfCZnnnlm0b7+4R/+Ib/61a/ysY99LDU1NTn55JPzqU99Kj/96U/f0ucHDhyY9773vXn22Wdz2GGHtVjXs2fPLFiwIF/5ylfyxS9+Mb/73e+y6667ZsiQIfna176W7t27v2p/Bx98cC6//PJ89atfzdSpU3PUUUdlxowZGT9+fGWbTZs2ZeLEifn973+f2traHHfccbniiiuSJB06dMjUqVPz29/+Np07d8773ve+zJkzp+h3syVqmt/OXeV2EE1NTenevXvWrl2b2traao8DAAAA7GRefvnlPPXUU9lrr73SqVOnao/zppqbmzNw4MB86lOfyuTJk6s9zhZ7o9/3lnQiZ6wBAAAA8Jb94Q9/yJw5c9LY2JjTTjut2uNUlbAGAAAAwFvWp0+f7Lbbbrn22muz6667VnucqhLWAAAAAHjL3FXs/9em2gMAAAAAwPZIWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAo0K7aAwAAAADwzhl+3ve32Xct/tr4Lf7MhAkTcv311ydJ2rdvnz322CPjx4/PP//zP6ddu9adrlr3dAAAAADs8I477rjMmjUr69aty2233ZaJEyemffv2mTp1arVHe0PCWiu3LasyO7eS/1cBAAAAtoaOHTumvr4+SXL22Wfnpptuyk9+8pNWH9bcYw0AAACAVqVz585Zv359tcd4U8IaAAAAAK1Cc3Nz7r777txxxx05+uijqz3Om3IpKAAAAABVdeutt6Zbt27ZsGFDNm/enI9//OO56KKLqj3WmxLWAAAAAKiqv/mbv8k111yTDh06pF+/fq3+aaCv2D6mBAAAAGCH1bVr1+y7777VHmOLuccaAAAAABQQ1gAAAACggEtBAQAAAHZgi782vtojvKHrrruu2iMUc8YaAAAAABQQ1gAAAACgQKsJa1/5yldSU1OTSZMmVZa9/PLLmThxYnr16pVu3bpl7NixWbVqVYvPrVixIieccEK6dOmSPn365LzzzsvGjRu38fQAAAAA7GxaRVhbtGhR/vVf/zVDhw5tsfzcc8/NLbfckh/+8IeZN29ennnmmXz4wx+urN+0aVNOOOGErF+/Pvfff3+uv/76XHfddZk2bdq2PgQAAAAAdjJVD2svvPBCxo0bl29/+9vZddddK8vXrl2b7373u7n88stz9NFHZ/jw4Zk1a1buv//+LFiwIEly5513ZtmyZfnBD36QYcOGZfTo0bnkkksyc+bMrF+/vlqHBAAAAMBOoOphbeLEiTnhhBMycuTIFssXL16cDRs2tFi+//77Z4899sj8+fOTJPPnz8+QIUNSV1dX2WbUqFFpamrKo48++rrfuW7dujQ1NbV4AQAAAMCWaFfNL58zZ04eeuihLFq06FXrGhsb06FDh/To0aPF8rq6ujQ2Nla2+d9R7ZX1r6x7PTNmzMjFF1/8NqcHAAAAYGdWtTPWnn766XzmM5/JDTfckE6dOm3T7546dWrWrl1beT399NPb9PsBAAAA2P5VLawtXrw4q1evzl/91V+lXbt2adeuXebNm5errroq7dq1S11dXdavX581a9a0+NyqVatSX1+fJKmvr3/VU0Jfef/KNq+lY8eOqa2tbfECAAAAgC1RtbB2zDHHZOnSpVmyZEnldcghh2TcuHGVn9u3b5+5c+dWPrN8+fKsWLEiDQ0NSZKGhoYsXbo0q1evrmxz1113pba2NoMHD97mxwQAAADAzqNq91jbZZddctBBB7VY1rVr1/Tq1auy/PTTT8/kyZPTs2fP1NbW5tOf/nQaGhpy+OGHJ0mOPfbYDB48OKecckouvfTSNDY25vzzz8/EiRPTsWPHbX5MAAAAAK3NiulDttl37TFt6RZ/ZsKECbn++uszY8aM/NM//VNl+c0335wPfehDaW5u3pojblVVfyroG7niiivygQ98IGPHjs1RRx2V+vr63HjjjZX1bdu2za233pq2bdumoaEhn/jEJzJ+/PhMnz69ilMDAAAAsCU6deqUr371q3nuueeqPcoWqepTQf/SPffc0+J9p06dMnPmzMycOfN1PzNgwIDcdttt7/BkAAAAALxTRo4cmSeeeCIzZszIpZdeWu1x3rJWfcYaAAAAADu+tm3b5stf/nKuvvrq/P73v6/2OG+ZsAYAAABA1X3oQx/KsGHDcuGFF1Z7lLdMWAMAAACgVfjqV7+a66+/Po899li1R3lLhDUAAAAAWoWjjjoqo0aNytSpU6s9ylvSqh5eAAAAAMDO7Stf+UqGDRuWQYMGVXuUN+WMNQAAAABajSFDhmTcuHG56qqrqj3KmxLWAAAAAGhVpk+fns2bN1d7jDflUlAAAACAHdge05ZWe4Q3dN11171q2Z577pl169Zt+2G2kDPWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAACtRHNzc7VH2Clsrd+zsAYAAABQZe3bt0+SvPTSS1WeZOfwyu/5ld97qXZbYxgAAAAAyrVt2zY9evTI6tWrkyRdunRJTU1Nlafa8TQ3N+ell17K6tWr06NHj7Rt2/Zt7U9YAwAAAGgF6uvrk6QS13jn9OjRo/L7fjuENQAAAIBWoKamJn379k2fPn2yYcOGao+zw2rfvv3bPlPtFcIaAAAAQCvStm3brRZ+eGd5eAEAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACgQLtqDwAAvLnh532/2iOwk1j8tfHVHgEAYLvhjDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABaoa1q655poMHTo0tbW1qa2tTUNDQ376059W1o8YMSI1NTUtXmeddVaLfaxYsSInnHBCunTpkj59+uS8887Lxo0bt/WhAAAAALCTaVfNL999993zla98JQMHDkxzc3Ouv/76fPCDH8yvfvWrHHjggUmSM844I9OnT698pkuXLpWfN23alBNOOCH19fW5//77s3LlyowfPz7t27fPl7/85W1+PAAAAADsPKoa1saMGdPi/Ze+9KVcc801WbBgQSWsdenSJfX19a/5+TvvvDPLli3L3Xffnbq6ugwbNiyXXHJJpkyZkosuuigdOnR4x48BAAAAgJ1Tq7nH2qZNmzJnzpy8+OKLaWhoqCy/4YYbsttuu+Wggw7K1KlT89JLL1XWzZ8/P0OGDEldXV1l2ahRo9LU1JRHH330db9r3bp1aWpqavECAAAAgC1R1TPWkmTp0qVpaGjIyy+/nG7duuWmm27K4MGDkyQf//jHM2DAgPTr1y8PP/xwpkyZkuXLl+fGG29MkjQ2NraIakkq7xsbG1/3O2fMmJGLL774HToiAAAAAHYGVQ9rgwYNypIlS7J27dr853/+Z0499dTMmzcvgwcPzplnnlnZbsiQIenbt2+OOeaYPPnkk9lnn32Kv3Pq1KmZPHly5X1TU1P69+//to4DAAAAgJ1L1S8F7dChQ/bdd98MHz48M2bMyMEHH5xvfOMbr7ntYYcdliR54oknkiT19fVZtWpVi21eef9692VLko4dO1aeRPrKCwAAAAC2RNXD2l/avHlz1q1b95rrlixZkiTp27dvkqShoSFLly7N6tWrK9vcddddqa2trVxOCgAAAADvhKpeCjp16tSMHj06e+yxR55//vnMnj0799xzT+644448+eSTmT17do4//vj06tUrDz/8cM4999wcddRRGTp0aJLk2GOPzeDBg3PKKafk0ksvTWNjY84///xMnDgxHTt2rOahAQAAALCDq2pYW716dcaPH5+VK1eme/fuGTp0aO644468//3vz9NPP5277747V155ZV588cX0798/Y8eOzfnnn1/5fNu2bXPrrbfm7LPPTkNDQ7p27ZpTTz0106dPr+JRAQAAALAzqGpY++53v/u66/r375958+a96T4GDBiQ2267bWuOBQAAAABvqtXdYw0AAAAAtgfCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACgQLtqDwAAAMCbG37e96s9AjuJxV8bX+0RYLvhjDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAApUNaxdc801GTp0aGpra1NbW5uGhob89Kc/rax/+eWXM3HixPTq1SvdunXL2LFjs2rVqhb7WLFiRU444YR06dIlffr0yXnnnZeNGzdu60MBAAAAYCdT1bC2++675ytf+UoWL16cBx98MEcffXQ++MEP5tFHH02SnHvuubnlllvywx/+MPPmzcszzzyTD3/4w5XPb9q0KSeccELWr1+f+++/P9dff32uu+66TJs2rVqHBAAAAMBOol01v3zMmDEt3n/pS1/KNddckwULFmT33XfPd7/73cyePTtHH310kmTWrFk54IADsmDBghx++OG58847s2zZstx9992pq6vLsGHDcskll2TKlCm56KKL0qFDh2ocFgAAAAA7gVZzj7VNmzZlzpw5efHFF9PQ0JDFixdnw4YNGTlyZGWb/fffP3vssUfmz5+fJJk/f36GDBmSurq6yjajRo1KU1NT5ay317Ju3bo0NTW1eAEAAADAlqh6WFu6dGm6deuWjh075qyzzspNN92UwYMHp7GxMR06dEiPHj1abF9XV5fGxsYkSWNjY4uo9sr6V9a9nhkzZqR79+6VV//+/bfuQQEAAACww6t6WBs0aFCWLFmShQsX5uyzz86pp56aZcuWvaPfOXXq1Kxdu7byevrpp9/R7wMAAABgx1PVe6wlSYcOHbLvvvsmSYYPH55FixblG9/4Rj72sY9l/fr1WbNmTYuz1latWpX6+vokSX19fR544IEW+3vlqaGvbPNaOnbsmI4dO27lIwEAAABgZ1L1M9b+0ubNm7Nu3boMHz487du3z9y5cyvrli9fnhUrVqShoSFJ0tDQkKVLl2b16tWVbe66667U1tZm8ODB23x2AAAAAHYeVT1jberUqRk9enT22GOPPP/885k9e3buueee3HHHHenevXtOP/30TJ48OT179kxtbW0+/elPp6GhIYcffniS5Nhjj83gwYNzyimn5NJLL01jY2POP//8TJw40RlpAAAAALyjqhrWVq9enfHjx2flypXp3r17hg4dmjvuuCPvf//7kyRXXHFF2rRpk7Fjx2bdunUZNWpUvvnNb1Y+37Zt29x66605++yz09DQkK5du+bUU0/N9OnTq3VIAAAAAOwkqhrWvvvd777h+k6dOmXmzJmZOXPm624zYMCA3HbbbVt7NAAAAAB4Q63uHmsAAAAAsD0Q1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUKCqYW3GjBk59NBDs8suu6RPnz458cQTs3z58hbbjBgxIjU1NS1eZ511VottVqxYkRNOOCFdunRJnz59ct5552Xjxo3b8lAAAAAA2Mm0q+aXz5s3LxMnTsyhhx6ajRs35p//+Z9z7LHHZtmyZenatWtluzPOOCPTp0+vvO/SpUvl502bNuWEE05IfX197r///qxcuTLjx49P+/bt8+Uvf3mbHg8AAAAAO4+qhrXbb7+9xfvrrrsuffr0yeLFi3PUUUdVlnfp0iX19fWvuY8777wzy5Yty9133526uroMGzYsl1xySaZMmZKLLrooHTp0eEePAQAAAICdU6u6x9ratWuTJD179myx/IYbbshuu+2Wgw46KFOnTs1LL71UWTd//vwMGTIkdXV1lWWjRo1KU1NTHn300df8nnXr1qWpqanFCwAAAAC2RFXPWPvfNm/enEmTJuWII47IQQcdVFn+8Y9/PAMGDEi/fv3y8MMPZ8qUKVm+fHluvPHGJEljY2OLqJak8r6xsfE1v2vGjBm5+OKL36EjAQAAAGBn0GrC2sSJE/PII4/kl7/8ZYvlZ555ZuXnIUOGpG/fvjnmmGPy5JNPZp999in6rqlTp2by5MmV901NTenfv3/Z4AAAAADslFrFpaDnnHNObr311vz85z/P7rvv/obbHnbYYUmSJ554IklSX1+fVatWtdjmlfevd1+2jh07pra2tsULAAAAALZEVcNac3NzzjnnnNx000352c9+lr322utNP7NkyZIkSd++fZMkDQ0NWbp0aVavXl3Z5q677kptbW0GDx78jswNAAAAAFW9FHTixImZPXt2fvzjH2eXXXap3BOte/fu6dy5c5588snMnj07xx9/fHr16pWHH3445557bo466qgMHTo0SXLsscdm8ODBOeWUU3LppZemsbEx559/fiZOnJiOHTtW8/AAAAAA2IFV9Yy1a665JmvXrs2IESPSt2/fyuvf//3fkyQdOnTI3XffnWOPPTb7779/PvvZz2bs2LG55ZZbKvto27Ztbr311rRt2zYNDQ35xCc+kfHjx2f69OnVOiwAAAAAdgJVPWOtubn5Ddf3798/8+bNe9P9DBgwILfddtvWGgsAAAAA3lSreHgBAAAAAGxvhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYrC2tFHH501a9a8anlTU1OOPvrotzsTAAAAALR6RWHtnnvuyfr161+1/OWXX84vfvGLtz0UAAAAALR27bZk44cffrjy87Jly9LY2Fh5v2nTptx+++1517vetfWmAwAAAIBWaovC2rBhw1JTU5OamprXvOSzc+fOufrqq7facAAAAADQWm1RWHvqqafS3NycvffeOw888EB69+5dWdehQ4f06dMnbdu23epDAgAAAEBrs0VhbcCAAUmSzZs3vyPDAAAAAMD2YovC2v/2+OOP5+c//3lWr179qtA2bdq0tz0YAAAAALRmRWHt29/+ds4+++zstttuqa+vT01NTWVdTU2NsAYAAADADq8orH3xi1/Ml770pUyZMmVrzwMAAAAA24U2JR967rnn8pGPfGRrzwIAAAAA242isPaRj3wkd95559aeBQAAAAC2G0WXgu6777654IILsmDBggwZMiTt27dvsf4f//Eft8pwAAAAANBaFYW1a6+9Nt26dcu8efMyb968FutqamqENQAAAAB2eEVh7amnntracwAAAADAdqXoHmsAAAAAsLMrOmPtk5/85Buu/973vlc0DAAAAABsL4rC2nPPPdfi/YYNG/LII49kzZo1Ofroo7fKYAAAAADQmhWFtZtuuulVyzZv3pyzzz47++yzz9seCgAAAABau612j7U2bdpk8uTJueKKK7bWLgEAAACg1dqqDy948skns3Hjxq25SwAAAABolYouBZ08eXKL983NzVm5cmX+7//9vzn11FO3ymAAAAAA0JoVhbVf/epXLd63adMmvXv3zmWXXfamTwwFAAAAgB1BUVj7+c9/vrXnAAAAAIDtSlFYe8Uf/vCHLF++PEkyaNCg9O7de6sMBQAAAACtXdHDC1588cV88pOfTN++fXPUUUflqKOOSr9+/XL66afnpZde2tozAgAAAECrUxTWJk+enHnz5uWWW27JmjVrsmbNmvz4xz/OvHnz8tnPfnZrzwgAAAAArU7RpaA/+tGP8p//+Z8ZMWJEZdnxxx+fzp0756Mf/WiuueaarTUfAAAAALRKRWesvfTSS6mrq3vV8j59+rgUFAAAAICdQlFYa2hoyIUXXpiXX365suxPf/pTLr744jQ0NGy14QAAAACgtSq6FPTKK6/Mcccdl9133z0HH3xwkuTXv/51OnbsmDvvvHOrDggAAAAArVFRWBsyZEgef/zx3HDDDfnNb36TJDn55JMzbty4dO7ceasOCAAAAACtUVFYmzFjRurq6nLGGWe0WP69730vf/jDHzJlypStMhwAAAAAtFZF91j713/91+y///6vWn7ggQfmW9/61tseCgAAAABau6Kw1tjYmL59+75qee/evbNy5cq3PRQAAAAAtHZFYa1///657777XrX8vvvuS79+/d72UAAAAADQ2hXdY+2MM87IpEmTsmHDhhx99NFJkrlz5+bzn/98PvvZz27VAQEAAACgNSoKa+edd17++Mc/5lOf+lTWr1+fJOnUqVOmTJmSqVOnbtUBAQAAAKA1KgprNTU1+epXv5oLLrggjz32WDp37pyBAwemY8eOW3s+AAAAAGiViu6x9opu3brl0EMPzUEHHVQU1WbMmJFDDz00u+yyS/r06ZMTTzwxy5cvb7HNyy+/nIkTJ6ZXr17p1q1bxo4dm1WrVrXYZsWKFTnhhBPSpUuX9OnTJ+edd142btz4dg4NAAAAAN7Q2wprb9e8efMyceLELFiwIHfddVc2bNiQY489Ni+++GJlm3PPPTe33HJLfvjDH2bevHl55pln8uEPf7iyftOmTTnhhBOyfv363H///bn++utz3XXXZdq0adU4JAAAAAB2EkWXgm4tt99+e4v31113Xfr06ZPFixfnqKOOytq1a/Pd7343s2fPrjwkYdasWTnggAOyYMGCHH744bnzzjuzbNmy3H333amrq8uwYcNyySWXZMqUKbnooovSoUOHahwaAAAAADu4qp6x9pfWrl2bJOnZs2eSZPHixdmwYUNGjhxZ2Wb//ffPHnvskfnz5ydJ5s+fnyFDhqSurq6yzahRo9LU1JRHH330Nb9n3bp1aWpqavECAAAAgC3RasLa5s2bM2nSpBxxxBE56KCDkiSNjY3p0KFDevTo0WLburq6NDY2Vrb531HtlfWvrHstM2bMSPfu3Suv/v37b+WjAQAAAGBH12rC2sSJE/PII49kzpw57/h3TZ06NWvXrq28nn766Xf8OwEAAADYsVT1HmuvOOecc3Lrrbfm3nvvze67715ZXl9fn/Xr12fNmjUtzlpbtWpV6uvrK9s88MADLfb3ylNDX9nmL3Xs2LHoKaYAAAAA8IqqnrHW3Nycc845JzfddFN+9rOfZa+99mqxfvjw4Wnfvn3mzp1bWbZ8+fKsWLEiDQ0NSZKGhoYsXbo0q1evrmxz1113pba2NoMHD942BwIAAADATqeqZ6xNnDgxs2fPzo9//OPssssulXuide/ePZ07d0737t1z+umnZ/LkyenZs2dqa2vz6U9/Og0NDTn88MOTJMcee2wGDx6cU045JZdeemkaGxtz/vnnZ+LEic5KAwAAAOAdU9Wwds011yRJRowY0WL5rFmzMmHChCTJFVdckTZt2mTs2LFZt25dRo0alW9+85uVbdu2bZtbb701Z599dhoaGtK1a9eceuqpmT59+rY6DAAAAAB2QlUNa83NzW+6TadOnTJz5szMnDnzdbcZMGBAbrvttq05GgAAAAC8oVbzVFAAAAAA2J4IawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACgQLtqDwAAAAC0HiumD6n2COwk9pi2tNojvG3OWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACgQLtqDwAAQOuxYvqQao/ATmKPaUurPQIAvG3OWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAAChQ1bB27733ZsyYMenXr19qampy8803t1g/YcKE1NTUtHgdd9xxLbZ59tlnM27cuNTW1qZHjx45/fTT88ILL2zDowAAAABgZ1TVsPbiiy/m4IMPzsyZM193m+OOOy4rV66svP7t3/6txfpx48bl0UcfzV133ZVbb7019957b84888x3enQAAAAAdnLtqvnlo0ePzujRo99wm44dO6a+vv411z322GO5/fbbs2jRohxyyCFJkquvvjrHH398vv71r6dfv35bfWYAAAAASLaDe6zdc8896dOnTwYNGpSzzz47f/zjHyvr5s+fnx49elSiWpKMHDkybdq0ycKFC193n+vWrUtTU1OLFwAAAABsiVYd1o477rh8//vfz9y5c/PVr3418+bNy+jRo7Np06YkSWNjY/r06dPiM+3atUvPnj3T2Nj4uvudMWNGunfvXnn179//HT0OAAAAAHY8Vb0U9M2cdNJJlZ+HDBmSoUOHZp999sk999yTY445pni/U6dOzeTJkyvvm5qaxDUAAAAAtkirPmPtL+29997Zbbfd8sQTTyRJ6uvrs3r16hbbbNy4Mc8+++zr3pct+fN922pra1u8AAAAAGBLbFdh7fe//33++Mc/pm/fvkmShoaGrFmzJosXL65s87Of/SybN2/OYYcdVq0xAQAAANgJVPVS0BdeeKFy9lmSPPXUU1myZEl69uyZnj175uKLL87YsWNTX1+fJ598Mp///Oez7777ZtSoUUmSAw44IMcdd1zOOOOMfOtb38qGDRtyzjnn5KSTTvJEUAAAAADeUVU9Y+3BBx/Mu9/97rz73e9OkkyePDnvfve7M23atLRt2zYPP/xw/vZv/zb77bdfTj/99AwfPjy/+MUv0rFjx8o+brjhhuy///455phjcvzxx+fII4/MtddeW61DAgAAAGAnUdUz1kaMGJHm5ubXXX/HHXe86T569uyZ2bNnb82xAAAAAOBNbVf3WAMAAACA1kJYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABRoV+0BgNZhxfQh1R6BncQe05ZWewQAAICtwhlrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFKhqWLv33nszZsyY9OvXLzU1Nbn55ptbrG9ubs60adPSt2/fdO7cOSNHjszjjz/eYptnn30248aNS21tbXr06JHTTz89L7zwwjY8CgAAAAB2RlUNay+++GIOPvjgzJw58zXXX3rppbnqqqvyrW99KwsXLkzXrl0zatSovPzyy5Vtxo0bl0cffTR33XVXbr311tx7770588wzt9UhAAAAALCTalfNLx89enRGjx79muuam5tz5ZVX5vzzz88HP/jBJMn3v//91NXV5eabb85JJ52Uxx57LLfffnsWLVqUQw45JEly9dVX5/jjj8/Xv/719OvXb5sdCwAAAAA7l1Z7j7WnnnoqjY2NGTlyZGVZ9+7dc9hhh2X+/PlJkvnz56dHjx6VqJYkI0eOTJs2bbJw4cLX3fe6devS1NTU4gUAAAAAW6LVhrXGxsYkSV1dXYvldXV1lXWNjY3p06dPi/Xt2rVLz549K9u8lhkzZqR79+6VV//+/bfy9AAAAADs6FptWHsnTZ06NWvXrq28nn766WqPBAAAAMB2ptWGtfr6+iTJqlWrWixftWpVZV19fX1Wr17dYv3GjRvz7LPPVrZ5LR07dkxtbW2LFwAAAABsiVYb1vbaa6/U19dn7ty5lWVNTU1ZuHBhGhoakiQNDQ1Zs2ZNFi9eXNnmZz/7WTZv3pzDDjtsm88MAAAAwM6jqk8FfeGFF/LEE09U3j/11FNZsmRJevbsmT322COTJk3KF7/4xQwcODB77bVXLrjggvTr1y8nnnhikuSAAw7IcccdlzPOOCPf+ta3smHDhpxzzjk56aSTPBEUAAAAgHdUVcPagw8+mL/5m7+pvJ88eXKS5NRTT811112Xz3/+83nxxRdz5plnZs2aNTnyyCNz++23p1OnTpXP3HDDDTnnnHNyzDHHpE2bNhk7dmyuuuqqbX4sAAAAAOxcqhrWRowYkebm5tddX1NTk+nTp2f69Omvu03Pnj0ze/bsd2I8AAAAAHhdrfYeawAAAADQmglrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAo0KrD2kUXXZSampoWr/3337+y/uWXX87EiRPTq1evdOvWLWPHjs2qVauqODEAAAAAO4tWHdaS5MADD8zKlSsrr1/+8peVdeeee25uueWW/PCHP8y8efPyzDPP5MMf/nAVpwUAAABgZ9Gu2gO8mXbt2qW+vv5Vy9euXZvvfve7mT17do4++ugkyaxZs3LAAQdkwYIFOfzww7f1qAAAAADsRFr9GWuPP/54+vXrl7333jvjxo3LihUrkiSLFy/Ohg0bMnLkyMq2+++/f/bYY4/Mnz//Dfe5bt26NDU1tXgBAAAAwJZo1WHtsMMOy3XXXZfbb78911xzTZ566qm8733vy/PPP5/GxsZ06NAhPXr0aPGZurq6NDY2vuF+Z8yYke7du1de/fv3fwePAgAAAIAdUau+FHT06NGVn4cOHZrDDjssAwYMyH/8x3+kc+fOxfudOnVqJk+eXHnf1NQkrgEAAACwRVr1GWt/qUePHtlvv/3yxBNPpL6+PuvXr8+aNWtabLNq1arXvCfb/9axY8fU1ta2eAEAAADAltiuwtoLL7yQJ598Mn379s3w4cPTvn37zJ07t7J++fLlWbFiRRoaGqo4JQAAAAA7g1Z9KejnPve5jBkzJgMGDMgzzzyTCy+8MG3bts3JJ5+c7t275/TTT8/kyZPTs2fP1NbW5tOf/nQaGho8ERQAAACAd1yrDmu///3vc/LJJ+ePf/xjevfunSOPPDILFixI7969kyRXXHFF2rRpk7Fjx2bdunUZNWpUvvnNb1Z5agAAAAB2Bq06rM2ZM+cN13fq1CkzZ87MzJkzt9FEAAAAAPBn29U91gAAAACgtRDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQQFgDAAAAgALCGgAAAAAUENYAAAAAoICwBgAAAAAFhDUAAAAAKCCsAQAAAEABYQ0AAAAACghrAAAAAFBAWAMAAACAAsIaAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABYQ1AAAAACggrAEAAABAAWENAAAAAAoIawAAAABQYIcJazNnzsyee+6ZTp065bDDDssDDzxQ7ZEAAAAA2IHtEGHt3//93zN58uRceOGFeeihh3LwwQdn1KhRWb16dbVHAwAAAGAHtUOEtcsvvzxnnHFGTjvttAwePDjf+ta30qVLl3zve9+r9mgAAAAA7KDaVXuAt2v9+vVZvHhxpk6dWlnWpk2bjBw5MvPnz3/Nz6xbty7r1q2rvF+7dm2SpKmp6Z0dtsCmdX+q9gjsJJ5vv6naI7CTaI3/1m4P/D1gW/H3gG3F34Mt528B24q/BWwrrfVvwStzNTc3v+m2231Y+5//+Z9s2rQpdXV1LZbX1dXlN7/5zWt+ZsaMGbn44otftbx///7vyIywPTio2gOw85jRvdoTAG/A3wO2GX8PoNXyt4BtppX/LXj++efTvfsbz7jdh7USU6dOzeTJkyvvN2/enGeffTa9evVKTU1NFSeD6mhqakr//v3z9NNPp7a2ttrjAFAl/h4A4G8B/PlMteeffz79+vV70223+7C22267pW3btlm1alWL5atWrUp9ff1rfqZjx47p2LFji2U9evR4p0aE7UZtba0/ngD4ewCAvwXs9N7sTLVXbPcPL+jQoUOGDx+euXPnVpZt3rw5c+fOTUNDQxUnAwAAAGBHtt2fsZYkkydPzqmnnppDDjkk73nPe3LllVfmxRdfzGmnnVbt0QAAAADYQe0QYe1jH/tY/vCHP2TatGlpbGzMsGHDcvvtt7/qgQbAa+vYsWMuvPDCV10iDcDOxd8DAPwtgC1T0/xWnh0KAAAAALSw3d9jDQAAAACqQVgDAAAAgALCGgAAAAAUENaAIhdddFGGDRtWeT9hwoSceOKJVZsHgOoYMWJEJk2aVO0xAHiL3uzf7T333DNXXnnlNpsHtnc7xFNBAQAAgLdv0aJF6dq1a7XHgO2GsAYAAAAkSXr37l3tEWC74lJQ2EH853/+Z4YMGZLOnTunV69eGTlyZF588cXKJZpf/vKXU1dXlx49emT69OnZuHFjzjvvvPTs2TO77757Zs2a1WJ/U6ZMyX777ZcuXbpk7733zgUXXJANGzZU6egA2FIjRozIpz/96UyaNCm77rpr6urq8u1vfzsvvvhiTjvttOyyyy7Zd99989Of/rTymUceeSSjR49Ot27dUldXl1NOOSX/8z//U1n/4osvZvz48enWrVv69u2byy67rBqHBsDbtHHjxpxzzjnp3r17dtttt1xwwQVpbm5O8upLQX/zm9/kyCOPTKdOnTJ48ODcfffdqampyc0331yd4aGVEdZgB7By5cqcfPLJ+eQnP5nHHnss99xzTz784Q9X/jj+7Gc/yzPPPJN77703l19+eS688MJ84AMfyK677pqFCxfmrLPOyj/8wz/k97//fWWfu+yyS6677rosW7Ys3/jGN/Ltb387V1xxRbUOEYAC119/fXbbbbc88MAD+fSnP52zzz47H/nIR/Le9743Dz30UI499ticcsopeemll7JmzZocffTRefe7350HH3wwt99+e1atWpWPfvSjlf2dd955mTdvXn784x/nzjvvzD333JOHHnqoikcIQInrr78+7dq1ywMPPJBvfOMbufzyy/Od73znVdtt2rQpJ554Yrp06ZKFCxfm2muvzRe+8IUqTAytV03zK//LG9huPfTQQxk+fHh++9vfZsCAAS3WTZgwIffcc0/++7//O23a/Lml77///unTp0/uvffeJH/+g9m9e/d85zvfyUknnfSa3/H1r389c+bMyYMPPpjkzw8vuPnmm7NkyZLK96xZs8b/cwXQSowYMSKbNm3KL37xiyT//7/1H/7wh/P9738/SdLY2Ji+fftm/vz5ufvuu/OLX/wid9xxR2Ufv//979O/f/8sX748/fr1S69evfKDH/wgH/nIR5Ikzz77bHbfffeceeaZbnQNsJ0YMWJEVq9enUcffTQ1NTVJkn/6p3/KT37ykyxbtix77rlnJk2alEmTJuX222/PmDFj8vTTT6e+vj5Jcvfdd+f9739/brrpJg8vg7jHGuwQDj744BxzzDEZMmRIRo0alWOPPTZ/93d/l1133TVJcuCBB1aiWpLU1dXloIMOqrxv27ZtevXqldWrV1eW/fu//3uuuuqqPPnkk3nhhReycePG1NbWbruDAuBtGzp0aOXnV/6tHzJkSGVZXV1dkmT16tX59a9/nZ///Ofp1q3bq/bz5JNP5k9/+lPWr1+fww47rLK8Z8+eGTRo0Dt4BAC8Ew4//PBKVEuShoaGXHbZZdm0aVOL7ZYvX57+/ftXolqSvOc979lmc8L2wKWgsANo27Zt7rrrrvz0pz/N4MGDc/XVV2fQoEF56qmnkiTt27dvsX1NTc1rLtu8eXOSZP78+Rk3blyOP/743HrrrfnVr36VL3zhC1m/fv22OSAAtoo3+/f/lf9RtXnz5rzwwgsZM2ZMlixZ0uL1+OOP56ijjtqmcwMAbC+csQY7iJqamhxxxBE54ogjMm3atAwYMCA33XRT0b7uv//+DBgwoMX9E373u99trVEBaIX+6q/+Kj/60Y+y5557pl27V/9XxH322Sft27fPwoULs8ceeyRJnnvuufzXf/1X/vqv/3pbjwvA27Bw4cIW7xcsWJCBAwembdu2LZYPGjQoTz/9dFatWlU5y3nRokXbbE7YHjhjDXYACxcuzJe//OU8+OCDWbFiRW688cb84Q9/yAEHHFC0v4EDB2bFihWZM2dOnnzyyVx11VXFkQ6A7cPEiRPz7LPP5uSTT86iRYvy5JNP5o477shpp52WTZs2pVu3bjn99NNz3nnn5Wc/+1keeeSRTJgwocWtBgDYPqxYsSKTJ0/O8uXL82//9m+5+uqr85nPfOZV273//e/PPvvsk1NPPTUPP/xw7rvvvpx//vlJ0uJSUtiZ+W9CsAOora3Nvffem+OPPz777bdfzj///Fx22WUZPXp00f7+9m//Nueee27OOeecDBs2LPfff38uuOCCrTw1AK1Jv379ct9992XTpk059thjM2TIkEyaNCk9evSoxLOvfe1red/73pcxY8Zk5MiROfLIIzN8+PAqTw7Alho/fnz+9Kc/5T3veU8mTpyYz3zmMznzzDNftV3btm1z880354UXXsihhx6av//7v69c1dKpU6dtPTa0Sp4KCgAAALwl9913X4488sg88cQT2Weffao9DlSdsAYAAAC8pptuuindunXLwIED88QTT+Qzn/lMdt111/zyl7+s9mjQKnh4AQAAAPCann/++UyZMiUrVqzIbrvtlpEjR+ayyy6r9ljQajhjDQAAAAAKeHgBAAAAABQQ1gAAAACggLAGAAAAAAWENQAAAAAoIKwBALRSI0aMyKRJk7bJd91zzz2pqanJmjVrtsn3AQDsCIQ1AAC2meuuuy49evSo9hgAAFuFsAYAAAAABYQ1AIDtQE1NTW6++eYWy3r06JHrrruu8v7+++/PsGHD0qlTpxxyyCG5+eabU1NTkyVLlrzl77nvvvsydOjQdOrUKYcffngeeeSRFut/9KMf5cADD0zHjh2z55575rLLLmux/rnnnsv48eOz6667pkuXLhk9enQef/zxJH++3PS0007L2rVrU1NTk5qamlx00UVb8msAAGhVhDUAgB1AU1NTxowZkyFDhuShhx7KJZdckilTpmzxfs4777xcdtllWbRoUXr37p0xY8Zkw4YNSZLFixfnox/9aE466aQsXbo0F110US644IIWcW/ChAl58MEH85Of/CTz589Pc3Nzjj/++GzYsCHvfe97c+WVV6a2tjYrV67MypUr87nPfW5r/QoAALa5dtUeAACAt2/27NmpqanJt7/97XTq1CmDBw/O//t//y9nnHHGFu3nwgsvzPvf//4kyfXXX5/dd989N910Uz760Y/m8ssvzzHHHJMLLrggSbLffvtl2bJl+drXvpYJEybk8ccfz09+8pPcd999ee9735skueGGG9K/f//cfPPN+chHPpLu3bunpqYm9fX1W/cXAABQBc5YAwDYASxfvrxyCecr3vOe92zxfhoaGio/9+zZM4MGDcpjjz2WJHnsscdyxBFHtNj+iCOOyOOPP55NmzblscceS7t27XLYYYdV1vfq1avFPgAAdiTCGgDAdqCmpibNzc0tlr1yiSYAANUhrAEAbAd69+6dlStXVt4//vjjeemllyrvBw0alKVLl2bdunWVZYsWLdri71mwYEHl5+eeey7/9V//lQMOOCBJcsABB+S+++5rsf19992X/fbbL23bts0BBxyQjRs3ZuHChZX1f/zjH7N8+fIMHjw4SdKhQ4ds2rRpi+cCAGiNhDUAgO3A0UcfnX/5l3/Jr371qzz44IM566yz0r59+8r6j3/849m8eXPOPPPMPPbYY7njjjvy9a9/Pcmfz3Z7q6ZPn565c+fmkUceyYQJE7LbbrvlxBNPTJJ89rOfzdy5c3PJJZfkv/7rv3L99dfnX/7lXyoPIBg4cGA++MEP5owzzsgvf/nL/PrXv84nPvGJvOtd78oHP/jBJMmee+6ZF154IXPnzs3//M//tIiDAADbG2ENAGA7cNlll6V///553/vel49//OP53Oc+ly5dulTW19bW5pZbbsmSJUsybNiwfOELX8i0adOSpMV9197MV77ylXzmM5/J8OHD09jYmFtuuSUdOnRIkvzVX/1V/uM//iNz5szJQQcdlGnTpmX69OmZMGFC5fOzZs3K8OHD84EPfCANDQ1pbm7ObbfdVomA733ve3PWWWflYx/7WHr37p1LL710K/x2AACqo6b5L2/WAQDADuGGG27IaaedlrVr16Zz587VHgcAYIfTrtoDAACwdXz/+9/P3nvvnXe961359a9/nSlTpuSjH/2oqAYA8A5xKSgAwA6isbExn/jEJ3LAAQfk3HPPzUc+8pFce+21SZKzzjor3bp1e83XWWedVeXJAQC2Ty4FBQDYCaxevTpNTU2vua62tjZ9+vTZxhMBAGz/hDUAAAAAKOBSUAAAAAAoIKwBAAAAQAFhDQAAAAAKCGsAAAAAUEBYAwAAAIACwhoAAAAAFBDWAAAAAKCAsAYAAAAABf4/9PAXzME7BRAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='lug_boot', hue='binaryClass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b645aa70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low     528\n",
       "high    514\n",
       "med     513\n",
       "Name: safety, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['safety'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0bf059e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='safety', ylabel='count'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='safety', hue='binaryClass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f978b550",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "P    1097\n",
       "N     458\n",
       "Name: binaryClass, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['binaryClass'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7c7b7048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='binaryClass', ylabel='count'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='binaryClass')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "341e0ea8",
   "metadata": {},
   "source": [
    "As you can see, our data is completely skewed/imbalanced. The positive examples are 2x more than negative examples. \n",
    "\n",
    "So we will remember during the model evaluation that accuracy is not the right metric in this case. Real world datasets comes with their unique blends, dataset can be imbalanced. Missing values can be present. We just have to find the effective way to deal with those issues. So again for evaluation, we will not rely on accuracy. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6733727",
   "metadata": {},
   "source": [
    "## 4 - Data Preprocessing \n",
    "\n",
    "It is here that we prepare the data to be in the proper format for the machine learning model. \n",
    "\n",
    "### Handling Categorical Features\n",
    "\n",
    "Decision trees don't care if the features are scaled or not, and they can handle the categorical features. There is a [note on documentation](https://scikit-learn.org/stable/modules/tree.html#tree) that the sklearn tree implementation doesn't support categorical features, so I will go ahead and handle them. It's fun anyways :)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9517b46f",
   "metadata": {},
   "source": [
    "Before handling categorical features, let's create a training input data and labels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0766b45b",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_train = train_data.drop('binaryClass', axis=1)\n",
    "car_labels = train_data[['binaryClass']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0768adae",
   "metadata": {},
   "source": [
    "Let's create a pipeline to encode all features in the training input data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b7ee6d06",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('ord_enc', OrdinalEncoder())\n",
    "    \n",
    "])\n",
    "\n",
    "car_train_prepared = pipe.fit_transform(car_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc414f79",
   "metadata": {},
   "source": [
    "Let's also handle labels. Labels contain P and N, so we want to convert those into numbers. Here instead of using Ordinary Encoder, we will use Label Encoder. Sklearn is explicity that it is used to encode target features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b691edf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\16111\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:115: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "\n",
    "label_enc = LabelEncoder()\n",
    "\n",
    "car_labels_prepared = label_enc.fit_transform(car_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd496de6",
   "metadata": {},
   "source": [
    "## 5 - Training Decision Tree Classifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8311b87a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree_clf = DecisionTreeClassifier()\n",
    "\n",
    "tree_clf.fit(car_train_prepared, car_labels_prepared)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "757c0a01",
   "metadata": {},
   "source": [
    "Let's train the same model on the scaled data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ea28474e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if you run this, it will be error. \n",
    "# SKlearn tree implementation doesn't support categorical features\n",
    "\n",
    "#from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "#tree_clf = DecisionTreeClassifier()\n",
    "\n",
    "#tree_clf.fit(car_train, car_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9afb9bde",
   "metadata": {},
   "source": [
    "## 6 - Evaluating Decision Trees\n",
    "\n",
    "Let's build 3 functions to display accuracy, confusion matrix, and classification report. Classification report contains all useful metrics such as precision, recall, and f1 score. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6962359e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "def accuracy(input_data,model,labels):\n",
    "    \"\"\"\n",
    "    Take the input data, model and labels and return accuracy\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    preds = model.predict(input_data)\n",
    "    acc = accuracy_score(labels,preds)\n",
    "    \n",
    "    return acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5321beb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def conf_matrix(input_data,model,labels):\n",
    "    \"\"\"\n",
    "    Take the input data, model and labels and return confusion matrix\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    preds = model.predict(input_data)\n",
    "    cm = confusion_matrix(labels,preds)\n",
    "    \n",
    "    return cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "12b6e547",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "def class_report(input_data,model,labels):\n",
    "    \"\"\"\n",
    "    Take the input data, model and labels and return confusion matrix\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    preds = model.predict(input_data)\n",
    "    report = classification_report(labels,preds)\n",
    "    report = print(report)\n",
    "    \n",
    "    return report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a052ed6",
   "metadata": {},
   "source": [
    "Let's find the accuracy on the training set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a17fed16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy(car_train_prepared, tree_clf, car_labels_prepared)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbeda399",
   "metadata": {},
   "source": [
    "Ohh, see! The decision trees overfitted the dataset. Also if we remember well, our data is not balanced. We have many positive examples than negative examples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b7b71a96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 458,    0],\n",
       "       [   0, 1097]], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_matrix(car_train_prepared, tree_clf, car_labels_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1e7d0797",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       458\n",
      "           1       1.00      1.00      1.00      1097\n",
      "\n",
      "    accuracy                           1.00      1555\n",
      "   macro avg       1.00      1.00      1.00      1555\n",
      "weighted avg       1.00      1.00      1.00      1555\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class_report(car_train_prepared, tree_clf, car_labels_prepared)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54f6022d",
   "metadata": {},
   "source": [
    "The model clearly overfitted the data. Let's see how we can regularize it. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66a574a5",
   "metadata": {},
   "source": [
    "## 7 - Improving Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0f92566d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'random_state': None,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af110f75",
   "metadata": {},
   "source": [
    "One way to avoid overfitting is reduce number maximum depth of the tree, set by the hyperparameter `max_depth`. Similarly, we can attempt to reduce all hyperparameters with `max`term while also increasing the `min_` term parameters. \n",
    "\n",
    "Also, I set the `class_weight` to `balanced` because our dataset is imbalanced. By setting it to balanced, the model will automatically adjust the class weight based on the number of available samples in all classes. \n",
    "\n",
    "\n",
    "Let's use GridSearch to find best values of these hyperparameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "18f52052",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's hide warnings returned by grid search\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "056818ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 5400 candidates, totalling 16200 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n",
       "             estimator=DecisionTreeClassifier(class_weight=&#x27;balanced&#x27;,\n",
       "                                              random_state=42),\n",
       "             param_grid={&#x27;max_depth&#x27;: [0, 1, 2, 3, 4, 5],\n",
       "                         &#x27;max_features&#x27;: [0, 1, 2, 3, 4, 5],\n",
       "                         &#x27;max_leaf_nodes&#x27;: [0, 1, 2, 3, 4, 5],\n",
       "                         &#x27;min_samples_leaf&#x27;: [0, 1, 2, 3, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [0, 1, 2, 3, 4]},\n",
       "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3,\n",
       "             estimator=DecisionTreeClassifier(class_weight=&#x27;balanced&#x27;,\n",
       "                                              random_state=42),\n",
       "             param_grid={&#x27;max_depth&#x27;: [0, 1, 2, 3, 4, 5],\n",
       "                         &#x27;max_features&#x27;: [0, 1, 2, 3, 4, 5],\n",
       "                         &#x27;max_leaf_nodes&#x27;: [0, 1, 2, 3, 4, 5],\n",
       "                         &#x27;min_samples_leaf&#x27;: [0, 1, 2, 3, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [0, 1, 2, 3, 4]},\n",
       "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(class_weight=&#x27;balanced&#x27;, random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(class_weight=&#x27;balanced&#x27;, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3,\n",
       "             estimator=DecisionTreeClassifier(class_weight='balanced',\n",
       "                                              random_state=42),\n",
       "             param_grid={'max_depth': [0, 1, 2, 3, 4, 5],\n",
       "                         'max_features': [0, 1, 2, 3, 4, 5],\n",
       "                         'max_leaf_nodes': [0, 1, 2, 3, 4, 5],\n",
       "                         'min_samples_leaf': [0, 1, 2, 3, 4],\n",
       "                         'min_samples_split': [0, 1, 2, 3, 4]},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "params_grid = {'max_leaf_nodes': list(range(0, 10)), \n",
    "               'min_samples_split': [0,1,2, 3, 4], \n",
    "               'min_samples_leaf': [0,1,2, 3, 4], \n",
    "               'max_depth':[0,1,2,3,4,5],\n",
    "               'max_features':[0,1,2,3,4,5],\n",
    "              'max_leaf_nodes':[0,1,2,3,4,5]}\n",
    "\n",
    "#refit is true by default. The best estimator is trained on the whole dataset \n",
    "\n",
    "grid_search = GridSearchCV(DecisionTreeClassifier(random_state=42, class_weight='balanced'), params_grid, verbose=1, cv=3, refit=True)\n",
    "\n",
    "grid_search.fit(car_train_prepared, car_labels_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "55a5d7f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 4,\n",
       " 'max_features': 5,\n",
       " 'max_leaf_nodes': 5,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c4a1488d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(class_weight=&#x27;balanced&#x27;, max_depth=4, max_features=5,\n",
       "                       max_leaf_nodes=5, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(class_weight=&#x27;balanced&#x27;, max_depth=4, max_features=5,\n",
       "                       max_leaf_nodes=5, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(class_weight='balanced', max_depth=4, max_features=5,\n",
       "                       max_leaf_nodes=5, random_state=42)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4995ab2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_best = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0631c68d",
   "metadata": {},
   "source": [
    "We can also plot the tree of the improved model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bb3a6292",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety',\n",
       "       'binaryClass'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bc930bc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "\n",
    "plt.figure(figsize=(20,15))\n",
    "plot_tree(tree_best, feature_names=car_train.columns, class_names=['N','P']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44fc64ef",
   "metadata": {},
   "source": [
    "Let's make prediction on the training data again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "85b1b211",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8926045016077171"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy(car_train_prepared, tree_best, car_labels_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d6e03505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[430,  28],\n",
       "       [139, 958]], dtype=int64)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_matrix(car_train_prepared, tree_best, car_labels_prepared)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fd20f6c",
   "metadata": {},
   "source": [
    "In confusion matrix, each row represent an actual class and each column represents predicted class. \n",
    "\n",
    "So, from the results above:\n",
    "\n",
    "* 430 negative examples(N) were correcty predicted as negatives(`true negatives`).\n",
    "* 28 negatives examples(N) were incorrectly classified as positive examples when they are in fact negatives(`false positives`).\n",
    "* 139 positive examples were incorrectly classified as negative(N) when in fact they are positives(P) (`false negatives`).\n",
    "* 958 were correctly classified as positive examples(`true positives`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bfe10646",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.94      0.84       458\n",
      "           1       0.97      0.87      0.92      1097\n",
      "\n",
      "    accuracy                           0.89      1555\n",
      "   macro avg       0.86      0.91      0.88      1555\n",
      "weighted avg       0.91      0.89      0.90      1555\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class_report(car_train_prepared, tree_best, car_labels_prepared)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c62a574a",
   "metadata": {},
   "source": [
    "Wow, this is much better. By only setting the class weight to `balanced` and finding the best values of the hyperparameters, we were able to improve our model. \n",
    "\n",
    "If you remember, the negative class has fewer examples than the positive classes. You can see them in `support` in classification report. But our model is able to identify them correctly at 76%, and also is able to identify the positive examples at 97% without overfitting. That is precision. \n",
    "\n",
    "A few notes about Precison/Recall/F1 score:\n",
    "\n",
    "* Precision is the model accuracy on predicting positive examples correctly. \n",
    "* Recall is the ratio of the positive examples that are correctly identified by the model. \n",
    "\n",
    "* F1 score is the harmonic mean of precision and recall. \n",
    "\n",
    "The higher the precision and recall are, the higher the F1 score. But there is a tradeoff between them. Increasing precision will reduce recall, and vice versa. So it's fair to say that it depends on the problem you're trying to solve and the metrics you want to optimize for. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d96d0b2f",
   "metadata": {},
   "source": [
    "### Evaluating the model on the test set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21d18db2",
   "metadata": {},
   "source": [
    "It is only after we have improved the model that we can feed it to the test set. If we try to show the test set to the model while we are still training, we may cause a potential leak thus producing misleading predictions. \n",
    "\n",
    "Also, we will apply the same processing functions that we applied to the training set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3e4f6abb",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_test = test_data.drop('binaryClass', axis=1)\n",
    "car_test_labels = test_data['binaryClass']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "18686a6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling the categorical features with the pipeline that we defined early\n",
    "# We don't fit on the test data. Only transform\n",
    "\n",
    "car_test_prepared = pipe.transform(car_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4e320e33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handle labels too\n",
    "\n",
    "car_test_labels_prepared = label_enc.transform(car_test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f12a3457",
   "metadata": {},
   "source": [
    "Now we can make predictions on the test set. We will only have to call the metrics functions we created previously. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b0ead3bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8554913294797688"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy(car_test_prepared, tree_best, car_test_labels_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8d5414f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[53,  7],\n",
       "       [18, 95]], dtype=int64)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_matrix(car_test_prepared, tree_best, car_test_labels_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0c63170a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.88      0.81        60\n",
      "           1       0.93      0.84      0.88       113\n",
      "\n",
      "    accuracy                           0.86       173\n",
      "   macro avg       0.84      0.86      0.85       173\n",
      "weighted avg       0.87      0.86      0.86       173\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class_report(car_test_prepared, tree_best, car_test_labels_prepared)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c7eda1f",
   "metadata": {},
   "source": [
    "The model never saw the test set but as you can see, the results are truly remarkable. The model can generalize well on the test set and that is because we improved it in the right ways. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7e86c6d",
   "metadata": {},
   "source": [
    "This is the end of the notebook. We have learned how to build and how to regularize( or handle overfitting) the decion trees classifier. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a059a83f",
   "metadata": {},
   "source": [
    "## Acknowledgments\n",
    "\n",
    "Thanks to Jake VanderPlas for creating the open-source course [ Python Data Science Handbook ](https://github.com/jakevdp/PythonDataScienceHandbook). It inspires the majority of the content in this chapter."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.6 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "e7e062254315b6c9f92abc9bc2bdcaf487fbb633b3956fd7c9b16e81ab674fff"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}