Classification_Decision_tree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "79e807d5-0028-4eaf-9417-f213bf1ae1c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wrangling data\n",
    "from scipy.io import arff\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import re\n",
    "import time\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact\n",
    "\n",
    "# Visualizing the data \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "\n",
    "# Some aggregation to statisticize data \n",
    "from scipy.stats.mstats import trimmed_var\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "# Build model\n",
    "from imblearn.pipeline import make_pipeline\n",
    "#from sklearn.pipeline import make_pipeline\n",
    "from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier\n",
    "\n",
    "# Evaluate model\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, classification_report, confusion_matrix\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split\n",
    "\n",
    "# Save model \n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c8e67a94-dd1a-4590-b52b-eb6a095eb3cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "warnings.filterwarnings(action='ignore')\n",
    "pd.options.display.float_format = '{:.3f}'.format"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8f1d5e8-55b7-4622-95f3-1dc4b13da128",
   "metadata": {},
   "source": [
    "# Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "39da00ab-d2ce-49b1-babf-c3b8aa9998df",
   "metadata": {},
   "outputs": [],
   "source": [
    "def wrangle(filename):\n",
    "    \n",
    "    # Open compressed file, load into dictionary\n",
    "    data = arff.loadarff(filename)[0]\n",
    "        \n",
    "    # Load dictionary into DataFrame, set index\n",
    "    df = pd.DataFrame(data=data)\n",
    "    \n",
    "    # Rename columns\n",
    "    df.columns = [re.sub(pattern='Attr',repl='feat_',string=col) for col in df.columns]\n",
    "    df.rename(columns={'class':'bankrupt'},inplace=True)\n",
    "    \n",
    "    # Change dtype of the Labels columns\n",
    "    df['bankrupt'] = df['bankrupt'].astype(np.int64)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2adf75f2-51fc-4dc0-aae5-4fa6c2dd8fcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of DataFrame: (10503, 65)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feat_1</th>\n",
       "      <th>feat_2</th>\n",
       "      <th>feat_3</th>\n",
       "      <th>feat_4</th>\n",
       "      <th>feat_5</th>\n",
       "      <th>feat_6</th>\n",
       "      <th>feat_7</th>\n",
       "      <th>feat_8</th>\n",
       "      <th>feat_9</th>\n",
       "      <th>feat_10</th>\n",
       "      <th>...</th>\n",
       "      <th>feat_56</th>\n",
       "      <th>feat_57</th>\n",
       "      <th>feat_58</th>\n",
       "      <th>feat_59</th>\n",
       "      <th>feat_60</th>\n",
       "      <th>feat_61</th>\n",
       "      <th>feat_62</th>\n",
       "      <th>feat_63</th>\n",
       "      <th>feat_64</th>\n",
       "      <th>bankrupt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.174</td>\n",
       "      <td>0.413</td>\n",
       "      <td>0.144</td>\n",
       "      <td>1.348</td>\n",
       "      <td>-28.982</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.219</td>\n",
       "      <td>1.123</td>\n",
       "      <td>1.196</td>\n",
       "      <td>0.464</td>\n",
       "      <td>...</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.376</td>\n",
       "      <td>0.836</td>\n",
       "      <td>0.000</td>\n",
       "      <td>9.714</td>\n",
       "      <td>6.281</td>\n",
       "      <td>84.291</td>\n",
       "      <td>4.330</td>\n",
       "      <td>4.034</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.146</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.282</td>\n",
       "      <td>1.629</td>\n",
       "      <td>2.595</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.172</td>\n",
       "      <td>1.172</td>\n",
       "      <td>1.602</td>\n",
       "      <td>0.540</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.901</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.988</td>\n",
       "      <td>4.110</td>\n",
       "      <td>102.190</td>\n",
       "      <td>3.572</td>\n",
       "      <td>5.950</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.488</td>\n",
       "      <td>3.160</td>\n",
       "      <td>84.874</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.005</td>\n",
       "      <td>2.988</td>\n",
       "      <td>1.008</td>\n",
       "      <td>0.676</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.774</td>\n",
       "      <td>3.792</td>\n",
       "      <td>64.846</td>\n",
       "      <td>5.629</td>\n",
       "      <td>4.458</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.025</td>\n",
       "      <td>0.432</td>\n",
       "      <td>0.275</td>\n",
       "      <td>1.783</td>\n",
       "      <td>-10.105</td>\n",
       "      <td>0.569</td>\n",
       "      <td>0.025</td>\n",
       "      <td>1.306</td>\n",
       "      <td>1.051</td>\n",
       "      <td>0.565</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.952</td>\n",
       "      <td>0.143</td>\n",
       "      <td>4.229</td>\n",
       "      <td>5.053</td>\n",
       "      <td>98.783</td>\n",
       "      <td>3.695</td>\n",
       "      <td>3.484</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.188</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.342</td>\n",
       "      <td>1.928</td>\n",
       "      <td>-58.274</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.234</td>\n",
       "      <td>1.409</td>\n",
       "      <td>1.339</td>\n",
       "      <td>0.585</td>\n",
       "      <td>...</td>\n",
       "      <td>0.176</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.073</td>\n",
       "      <td>2.591</td>\n",
       "      <td>7.076</td>\n",
       "      <td>100.540</td>\n",
       "      <td>3.630</td>\n",
       "      <td>4.638</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   feat_1  feat_2  feat_3  feat_4  feat_5  feat_6  feat_7  feat_8  feat_9  \\\n",
       "0   0.174   0.413   0.144   1.348 -28.982   0.604   0.219   1.123   1.196   \n",
       "1   0.146   0.460   0.282   1.629   2.595   0.000   0.172   1.172   1.602   \n",
       "2   0.001   0.226   0.488   3.160  84.874   0.191   0.005   2.988   1.008   \n",
       "3   0.025   0.432   0.275   1.783 -10.105   0.569   0.025   1.306   1.051   \n",
       "4   0.188   0.415   0.342   1.928 -58.274   0.000   0.234   1.409   1.339   \n",
       "\n",
       "   feat_10  ...  feat_56  feat_57  feat_58  feat_59  feat_60  feat_61  \\\n",
       "0    0.464  ...    0.164    0.376    0.836    0.000    9.714    6.281   \n",
       "1    0.540  ...    0.028    0.271    0.901    0.000    5.988    4.110   \n",
       "2    0.676  ...    0.008    0.001    0.992    0.000    6.774    3.792   \n",
       "3    0.565  ...    0.048    0.043    0.952    0.143    4.229    5.053   \n",
       "4    0.585  ...    0.176    0.322    0.826    0.073    2.591    7.076   \n",
       "\n",
       "   feat_62  feat_63  feat_64  bankrupt  \n",
       "0   84.291    4.330    4.034         0  \n",
       "1  102.190    3.572    5.950         0  \n",
       "2   64.846    5.629    4.458         0  \n",
       "3   98.783    3.695    3.484         0  \n",
       "4  100.540    3.630    4.638         0  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = 'dataset/3year.arff'\n",
    "df = wrangle(file_path)\n",
    "bankrupt_df = df.copy()\n",
    "print(\"Shape of DataFrame:\",bankrupt_df.shape)\n",
    "bankrupt_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b868d0f-343f-487a-9feb-16f6b64a9ae7",
   "metadata": {},
   "source": [
    "# Explore data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27ab094f-87fd-4447-af30-1aa22392b2e6",
   "metadata": {},
   "source": [
    "## Overview data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f2861a16-2586-4af7-b165-8937f77a7c06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10503 entries, 0 to 10502\n",
      "Data columns (total 65 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   feat_1    10503 non-null  float64\n",
      " 1   feat_2    10503 non-null  float64\n",
      " 2   feat_3    10503 non-null  float64\n",
      " 3   feat_4    10485 non-null  float64\n",
      " 4   feat_5    10478 non-null  float64\n",
      " 5   feat_6    10503 non-null  float64\n",
      " 6   feat_7    10503 non-null  float64\n",
      " 7   feat_8    10489 non-null  float64\n",
      " 8   feat_9    10500 non-null  float64\n",
      " 9   feat_10   10503 non-null  float64\n",
      " 10  feat_11   10503 non-null  float64\n",
      " 11  feat_12   10485 non-null  float64\n",
      " 12  feat_13   10460 non-null  float64\n",
      " 13  feat_14   10503 non-null  float64\n",
      " 14  feat_15   10495 non-null  float64\n",
      " 15  feat_16   10489 non-null  float64\n",
      " 16  feat_17   10489 non-null  float64\n",
      " 17  feat_18   10503 non-null  float64\n",
      " 18  feat_19   10460 non-null  float64\n",
      " 19  feat_20   10460 non-null  float64\n",
      " 20  feat_21   9696 non-null   float64\n",
      " 21  feat_22   10503 non-null  float64\n",
      " 22  feat_23   10460 non-null  float64\n",
      " 23  feat_24   10276 non-null  float64\n",
      " 24  feat_25   10503 non-null  float64\n",
      " 25  feat_26   10489 non-null  float64\n",
      " 26  feat_27   9788 non-null   float64\n",
      " 27  feat_28   10275 non-null  float64\n",
      " 28  feat_29   10503 non-null  float64\n",
      " 29  feat_30   10460 non-null  float64\n",
      " 30  feat_31   10460 non-null  float64\n",
      " 31  feat_32   10402 non-null  float64\n",
      " 32  feat_33   10485 non-null  float64\n",
      " 33  feat_34   10489 non-null  float64\n",
      " 34  feat_35   10503 non-null  float64\n",
      " 35  feat_36   10503 non-null  float64\n",
      " 36  feat_37   5767 non-null   float64\n",
      " 37  feat_38   10503 non-null  float64\n",
      " 38  feat_39   10460 non-null  float64\n",
      " 39  feat_40   10485 non-null  float64\n",
      " 40  feat_41   10301 non-null  float64\n",
      " 41  feat_42   10460 non-null  float64\n",
      " 42  feat_43   10460 non-null  float64\n",
      " 43  feat_44   10460 non-null  float64\n",
      " 44  feat_45   9912 non-null   float64\n",
      " 45  feat_46   10485 non-null  float64\n",
      " 46  feat_47   10417 non-null  float64\n",
      " 47  feat_48   10503 non-null  float64\n",
      " 48  feat_49   10460 non-null  float64\n",
      " 49  feat_50   10489 non-null  float64\n",
      " 50  feat_51   10503 non-null  float64\n",
      " 51  feat_52   10417 non-null  float64\n",
      " 52  feat_53   10275 non-null  float64\n",
      " 53  feat_54   10275 non-null  float64\n",
      " 54  feat_55   10503 non-null  float64\n",
      " 55  feat_56   10460 non-null  float64\n",
      " 56  feat_57   10503 non-null  float64\n",
      " 57  feat_58   10474 non-null  float64\n",
      " 58  feat_59   10503 non-null  float64\n",
      " 59  feat_60   9911 non-null   float64\n",
      " 60  feat_61   10486 non-null  float64\n",
      " 61  feat_62   10460 non-null  float64\n",
      " 62  feat_63   10485 non-null  float64\n",
      " 63  feat_64   10275 non-null  float64\n",
      " 64  bankrupt  10503 non-null  int64  \n",
      "dtypes: float64(64), int64(1)\n",
      "memory usage: 5.2 MB\n"
     ]
    }
   ],
   "source": [
    "bankrupt_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a4d32e63-d8ac-43a0-90db-64bde4797339",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "feat_37   0.451\n",
       "feat_21   0.077\n",
       "feat_27   0.068\n",
       "feat_60   0.056\n",
       "feat_45   0.056\n",
       "feat_28   0.022\n",
       "feat_53   0.022\n",
       "feat_54   0.022\n",
       "feat_64   0.022\n",
       "feat_24   0.022\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(bankrupt_df.isna().sum()/bankrupt_df.shape[0]).sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5bca6901-566a-4bac-8466-bcb854e99a5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the feat 37 because the missing propotion is more than 40%\n",
    "bankrupt_df.drop(columns='feat_37',inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "564827d1-e86c-4b23-b87b-1674745d6f0d",
   "metadata": {},
   "source": [
    "# Preprocess"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47d4a424-35fe-4d9b-8e27-4e3881c04fbc",
   "metadata": {},
   "source": [
    "## Split data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "813046d5-8071-40db-8c04-18d24cd6698a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape: (10503, 63)\n",
      "y shape: (10503,)\n"
     ]
    }
   ],
   "source": [
    "target = \"bankrupt\"\n",
    "X = bankrupt_df.drop(columns=target)\n",
    "y = bankrupt_df[target]\n",
    "\n",
    "print(\"X shape:\", X.shape)\n",
    "print(\"y shape:\", y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8d76408-6637-4fb1-9ab2-cf956692fd48",
   "metadata": {},
   "source": [
    "In order to make sure that our model can generalize, we need to put aside a test set that we'll use to evaluate our model once it's trained. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "482d8253-a00f-40f8-a0f2-9ea1e408f22a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (8402, 63)\n",
      "y_train shape: (8402,)\n",
      "X_test shape: (2101, 63)\n",
      "y_test shape: (2101,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=42)\n",
    "\n",
    "print(\"X_train shape:\", X_train.shape)\n",
    "print(\"y_train shape:\", y_train.shape)\n",
    "print(\"X_test shape:\", X_test.shape)\n",
    "print(\"y_test shape:\", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "057939df-2a48-478a-8261-c1504ce8f9fd",
   "metadata": {},
   "source": [
    "## Resample "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5ddf1c2e-0290-4bc9-bd97-a230bcbb0d20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train_over shape: (16028, 63)\n"
     ]
    },
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feat_1</th>\n",
       "      <th>feat_2</th>\n",
       "      <th>feat_3</th>\n",
       "      <th>feat_4</th>\n",
       "      <th>feat_5</th>\n",
       "      <th>feat_6</th>\n",
       "      <th>feat_7</th>\n",
       "      <th>feat_8</th>\n",
       "      <th>feat_9</th>\n",
       "      <th>feat_10</th>\n",
       "      <th>...</th>\n",
       "      <th>feat_55</th>\n",
       "      <th>feat_56</th>\n",
       "      <th>feat_57</th>\n",
       "      <th>feat_58</th>\n",
       "      <th>feat_59</th>\n",
       "      <th>feat_60</th>\n",
       "      <th>feat_61</th>\n",
       "      <th>feat_62</th>\n",
       "      <th>feat_63</th>\n",
       "      <th>feat_64</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.014</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.336</td>\n",
       "      <td>3.284</td>\n",
       "      <td>28.116</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.017</td>\n",
       "      <td>1.065</td>\n",
       "      <td>1.363</td>\n",
       "      <td>0.516</td>\n",
       "      <td>...</td>\n",
       "      <td>5550.800</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.881</td>\n",
       "      <td>5.766</td>\n",
       "      <td>39.379</td>\n",
       "      <td>9.269</td>\n",
       "      <td>2.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.056</td>\n",
       "      <td>0.287</td>\n",
       "      <td>0.340</td>\n",
       "      <td>2.212</td>\n",
       "      <td>41.783</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.066</td>\n",
       "      <td>2.179</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.626</td>\n",
       "      <td>...</td>\n",
       "      <td>5034.800</td>\n",
       "      <td>-0.076</td>\n",
       "      <td>0.089</td>\n",
       "      <td>1.076</td>\n",
       "      <td>0.011</td>\n",
       "      <td>11.784</td>\n",
       "      <td>5.463</td>\n",
       "      <td>62.549</td>\n",
       "      <td>5.835</td>\n",
       "      <td>4.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.125</td>\n",
       "      <td>0.212</td>\n",
       "      <td>-0.044</td>\n",
       "      <td>0.767</td>\n",
       "      <td>-26.932</td>\n",
       "      <td>0.427</td>\n",
       "      <td>0.155</td>\n",
       "      <td>3.714</td>\n",
       "      <td>1.176</td>\n",
       "      <td>0.788</td>\n",
       "      <td>...</td>\n",
       "      <td>-5127.200</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.032</td>\n",
       "      <td>68.975</td>\n",
       "      <td>10.712</td>\n",
       "      <td>63.477</td>\n",
       "      <td>5.750</td>\n",
       "      <td>1.255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.271</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.419</td>\n",
       "      <td>2.243</td>\n",
       "      <td>300.000</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.303</td>\n",
       "      <td>1.877</td>\n",
       "      <td>8.682</td>\n",
       "      <td>0.652</td>\n",
       "      <td>...</td>\n",
       "      <td>1322.000</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.416</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.000</td>\n",
       "      <td>147.960</td>\n",
       "      <td>149.580</td>\n",
       "      <td>14.188</td>\n",
       "      <td>25.727</td>\n",
       "      <td>35.688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.485</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.619</td>\n",
       "      <td>2.622</td>\n",
       "      <td>372.120</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.596</td>\n",
       "      <td>1.622</td>\n",
       "      <td>2.967</td>\n",
       "      <td>0.619</td>\n",
       "      <td>...</td>\n",
       "      <td>490.720</td>\n",
       "      <td>0.201</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.288</td>\n",
       "      <td>46.919</td>\n",
       "      <td>7.779</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   feat_1  feat_2  feat_3  feat_4  feat_5  feat_6  feat_7  feat_8  feat_9  \\\n",
       "0   0.014   0.484   0.336   3.284  28.116   0.000   0.017   1.065   1.363   \n",
       "1   0.056   0.287   0.340   2.212  41.783   0.152   0.066   2.179   0.930   \n",
       "2   0.125   0.212  -0.044   0.767 -26.932   0.427   0.155   3.714   1.176   \n",
       "3   0.271   0.348   0.419   2.243 300.000   0.129   0.303   1.877   8.682   \n",
       "4   0.485   0.381   0.619   2.622 372.120   0.000   0.596   1.622   2.967   \n",
       "\n",
       "   feat_10  ...   feat_55  feat_56  feat_57  feat_58  feat_59  feat_60  \\\n",
       "0    0.516  ...  5550.800    0.015    0.027    0.987    0.000    5.881   \n",
       "1    0.626  ...  5034.800   -0.076    0.089    1.076    0.011   11.784   \n",
       "2    0.788  ... -5127.200    0.150    0.159    0.850    0.032   68.975   \n",
       "3    0.652  ...  1322.000    0.039    0.416    0.981    0.000  147.960   \n",
       "4    0.619  ...   490.720    0.201    0.785    0.816    0.000      NaN   \n",
       "\n",
       "   feat_61  feat_62  feat_63  feat_64  \n",
       "0    5.766   39.379    9.269    2.635  \n",
       "1    5.463   62.549    5.835    4.328  \n",
       "2   10.712   63.477    5.750    1.255  \n",
       "3  149.580   14.188   25.727   35.688  \n",
       "4    4.288   46.919    7.779      NaN  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "over_sampler = RandomOverSampler(random_state=42)\n",
    "X_train_over, y_train_over = over_sampler.fit_resample(X_train,y_train)\n",
    "print(\"X_train_over shape:\", X_train_over.shape)\n",
    "X_train_over.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33f87cc8-f4b6-4f52-b219-085bf381442a",
   "metadata": {},
   "source": [
    "# Build model "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8d81ca3-4918-4d95-8469-cefa235bd0dc",
   "metadata": {},
   "source": [
    "## Baseline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b1d82829-4d59-4342-8314-6087c6e84ea5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Accuracy: 0.9529\n"
     ]
    }
   ],
   "source": [
    "acc_baseline = df['bankrupt'].value_counts(normalize=True).max()\n",
    "print(\"Baseline Accuracy:\", round(acc_baseline, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8bc3fa7-b5ff-4523-8162-ff950cfcc469",
   "metadata": {},
   "source": [
    "Create the baseline to compare with the accuracy score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a57e2b79-27b6-4e4f-a9e0-245562d50e2e",
   "metadata": {},
   "source": [
    "## Iterate "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51cf724a-1373-4a49-a843-072e6065a323",
   "metadata": {},
   "source": [
    "### Create Pipeline: RandomForest vs Gradient Booster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5cfa6dd2-ec23-4c4b-98f2-4a347485e260",
   "metadata": {},
   "outputs": [],
   "source": [
    "sampling=RandomOverSampler(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a08779db-40c8-4c5e-b6e1-a2fe375ed575",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('simpleimputer', SimpleImputer()),\n",
       "                ('randomoversampler', RandomOverSampler(random_state=42)),\n",
       "                ('randomforestclassifier',\n",
       "                 RandomForestClassifier(random_state=42))])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create 2 pipeline: random forest and gradient booster\n",
    "# random forest\n",
    "clf_rf = make_pipeline(\n",
    "    SimpleImputer(),\n",
    "    RandomOverSampler(random_state=42),\n",
    "    RandomForestClassifier(random_state=42)\n",
    ")\n",
    "clf_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "48a3049d-ec2f-4bc5-8bd4-405cc19c5a81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('simpleimputer', SimpleImputer()),\n",
       "                ('randomoversampler', RandomOverSampler(random_state=42)),\n",
       "                ('gradientboostingclassifier',\n",
       "                 GradientBoostingClassifier(random_state=42))])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gradient booster\n",
    "clf_gb = make_pipeline(\n",
    "    SimpleImputer(),\n",
    "    RandomOverSampler(random_state=42),\n",
    "    GradientBoostingClassifier(random_state=42)\n",
    ")\n",
    "clf_gb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "421cb537-07dc-4b14-96b6-3657a6adc384",
   "metadata": {},
   "source": [
    "In order to get the best performance from our model, we will implement to create params anf tune its hyperparameter."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09d87407-5b2e-40cd-ae22-58d7187aeb02",
   "metadata": {},
   "source": [
    "### Create the params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a76b5e48-8935-47d1-9aec-1f57fc4479b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'simpleimputer__strategy': ['mean', 'median'],\n",
       " 'randomforestclassifier__n_estimators': range(25, 100, 25),\n",
       " 'randomforestclassifier__max_depth': range(10, 50, 10)}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest Params\n",
    "params_rf = {\n",
    "    'simpleimputer__strategy': ['mean','median'],\n",
    "    'randomforestclassifier__n_estimators': range(25,100,25),\n",
    "    'randomforestclassifier__max_depth': range(10,50,10)\n",
    "}\n",
    "params_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "84443279-dc85-4276-8232-f6ec61aad066",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'simpleimputer__strategy': ['mean', 'median'],\n",
       " 'gradientboostingclassifier__n_estimators': range(20, 31, 5),\n",
       " 'gradientboostingclassifier__max_depth': range(2, 5)}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gradient Booster Params\n",
    "params_gb = {\n",
    "    'simpleimputer__strategy': ['mean','median'],\n",
    "    'gradientboostingclassifier__n_estimators': range(20,31,5),\n",
    "    'gradientboostingclassifier__max_depth': range(2,5)\n",
    "}\n",
    "params_gb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d600bf-332f-4bc2-83b8-d520e0a851af",
   "metadata": {},
   "source": [
    "### Use Cross validation to find the best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0f03aba8-c56d-4bef-a178-d16e0a7ffb05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('simpleimputer', SimpleImputer()),\n",
       "                                       ('randomoversampler',\n",
       "                                        RandomOverSampler(random_state=42)),\n",
       "                                       ('randomforestclassifier',\n",
       "                                        RandomForestClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'randomforestclassifier__max_depth': range(10, 50, 10),\n",
       "                         'randomforestclassifier__n_estimators': range(25, 100, 25),\n",
       "                         'simpleimputer__strategy': ['mean', 'median']},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rf = GridSearchCV(estimator=clf_rf,param_grid=params_rf,cv=5,n_jobs=-1,verbose=1)\n",
    "model_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4f18b4df-dbad-43ff-bed9-9f071499ba6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('simpleimputer', SimpleImputer()),\n",
       "                                       ('randomoversampler',\n",
       "                                        RandomOverSampler(random_state=42)),\n",
       "                                       ('gradientboostingclassifier',\n",
       "                                        GradientBoostingClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'gradientboostingclassifier__max_depth': range(2, 5),\n",
       "                         'gradientboostingclassifier__n_estimators': range(20, 31, 5),\n",
       "                         'simpleimputer__strategy': ['mean', 'median']},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_gb = GridSearchCV(estimator=clf_gb,param_grid=params_gb,cv=5,n_jobs=-1,verbose=1)\n",
    "model_gb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe86b161-2bd3-439f-b4bc-7520893ca158",
   "metadata": {},
   "source": [
    "### Train model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c10dea9e-2276-47ba-8350-c49da35f31af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n",
      "GridSearchCV(cv=5,\n",
      "             estimator=Pipeline(steps=[('simpleimputer', SimpleImputer()),\n",
      "                                       ('randomoversampler',\n",
      "                                        RandomOverSampler(random_state=42)),\n",
      "                                       ('randomforestclassifier',\n",
      "                                        RandomForestClassifier(random_state=42))]),\n",
      "             n_jobs=-1,\n",
      "             param_grid={'randomforestclassifier__max_depth': range(10, 50, 10),\n",
      "                         'randomforestclassifier__n_estimators': range(25, 100, 25),\n",
      "                         'simpleimputer__strategy': ['mean', 'median']},\n",
      "             verbose=1)\n",
      "Total diff time: 126.13560771942139 second\n"
     ]
    }
   ],
   "source": [
    "# Train model\n",
    "start = time.time()\n",
    "print(model_rf.fit(X_train,y_train))\n",
    "diff = time.time() - start\n",
    "print(f'Total diff time: {diff} second')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "778370aa-f4fc-414e-850f-aeb01a2ef24b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 18 candidates, totalling 90 fits\n",
      "GridSearchCV(cv=5,\n",
      "             estimator=Pipeline(steps=[('simpleimputer', SimpleImputer()),\n",
      "                                       ('randomoversampler',\n",
      "                                        RandomOverSampler(random_state=42)),\n",
      "                                       ('gradientboostingclassifier',\n",
      "                                        GradientBoostingClassifier(random_state=42))]),\n",
      "             n_jobs=-1,\n",
      "             param_grid={'gradientboostingclassifier__max_depth': range(2, 5),\n",
      "                         'gradientboostingclassifier__n_estimators': range(20, 31, 5),\n",
      "                         'simpleimputer__strategy': ['mean', 'median']},\n",
      "             verbose=1)\n",
      "Total time: 222.08275508880615 sec\n"
     ]
    }
   ],
   "source": [
    "# Fit model to over-sampled training data\n",
    "start_time = time.time()\n",
    "print(model_gb.fit(X_train,y_train))\n",
    "diff = time.time() - start_time\n",
    "print(f\"Total time: {diff} sec\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d39ba5b2-22e9-48d3-9081-2cd3478ab8d2",
   "metadata": {},
   "source": [
    "### CV results Assessment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15ec7909-86fb-41df-b446-04fc33b29bfd",
   "metadata": {},
   "source": [
    "- Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b8aba88e-45b1-4b1b-8250-935101f4f1fe",
   "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>rank_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>params</th>\n",
       "      <th>param_simpleimputer__strategy</th>\n",
       "      <th>param_randomforestclassifier__n_estimators</th>\n",
       "      <th>param_randomforestclassifier__max_depth</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_fit_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.962</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.960</td>\n",
       "      <td>{'randomforestclassifier__max_depth': 30, 'ran...</td>\n",
       "      <td>mean</td>\n",
       "      <td>75</td>\n",
       "      <td>30</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.141</td>\n",
       "      <td>11.691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.959</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.958</td>\n",
       "      <td>{'randomforestclassifier__max_depth': 40, 'ran...</td>\n",
       "      <td>mean</td>\n",
       "      <td>75</td>\n",
       "      <td>40</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.641</td>\n",
       "      <td>11.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.959</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.957</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.957</td>\n",
       "      <td>{'randomforestclassifier__max_depth': 30, 'ran...</td>\n",
       "      <td>mean</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.134</td>\n",
       "      <td>4.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.957</td>\n",
       "      <td>0.957</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.957</td>\n",
       "      <td>{'randomforestclassifier__max_depth': 30, 'ran...</td>\n",
       "      <td>mean</td>\n",
       "      <td>50</td>\n",
       "      <td>30</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.083</td>\n",
       "      <td>7.847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.957</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.957</td>\n",
       "      <td>{'randomforestclassifier__max_depth': 40, 'ran...</td>\n",
       "      <td>mean</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.175</td>\n",
       "      <td>3.621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank_test_score  std_test_score  mean_test_score  split4_test_score  \\\n",
       "16                1           0.001            0.960              0.962   \n",
       "22                2           0.001            0.959              0.960   \n",
       "12                3           0.001            0.959              0.960   \n",
       "14                4           0.001            0.958              0.960   \n",
       "18                5           0.001            0.958              0.958   \n",
       "\n",
       "    split3_test_score  split2_test_score  split1_test_score  \\\n",
       "16              0.958              0.958              0.960   \n",
       "22              0.958              0.958              0.960   \n",
       "12              0.957              0.958              0.961   \n",
       "14              0.957              0.957              0.960   \n",
       "18              0.957              0.958              0.960   \n",
       "\n",
       "    split0_test_score                                             params  \\\n",
       "16              0.960  {'randomforestclassifier__max_depth': 30, 'ran...   \n",
       "22              0.958  {'randomforestclassifier__max_depth': 40, 'ran...   \n",
       "12              0.957  {'randomforestclassifier__max_depth': 30, 'ran...   \n",
       "14              0.957  {'randomforestclassifier__max_depth': 30, 'ran...   \n",
       "18              0.957  {'randomforestclassifier__max_depth': 40, 'ran...   \n",
       "\n",
       "   param_simpleimputer__strategy param_randomforestclassifier__n_estimators  \\\n",
       "16                          mean                                         75   \n",
       "22                          mean                                         75   \n",
       "12                          mean                                         25   \n",
       "14                          mean                                         50   \n",
       "18                          mean                                         25   \n",
       "\n",
       "   param_randomforestclassifier__max_depth  std_score_time  mean_score_time  \\\n",
       "16                                      30           0.015            0.108   \n",
       "22                                      40           0.018            0.098   \n",
       "12                                      30           0.004            0.043   \n",
       "14                                      30           0.014            0.067   \n",
       "18                                      40           0.009            0.033   \n",
       "\n",
       "    std_fit_time  mean_fit_time  \n",
       "16         0.141         11.691  \n",
       "22         0.641         11.109  \n",
       "12         0.134          4.109  \n",
       "14         0.083          7.847  \n",
       "18         0.175          3.621  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_rf = pd.DataFrame(model_rf.cv_results_)\n",
    "results_rf.iloc[:,::-1].sort_values(\"rank_test_score\").head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b41d791f-9252-4d50-9754-983053c0362c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest result:\n",
      "The best param: {'randomforestclassifier__max_depth': 30, 'randomforestclassifier__n_estimators': 75, 'simpleimputer__strategy': 'mean'}\n",
      "The best score: 0.9604950023798191\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"Random Forest result:\n",
    "The best param: {model_rf.best_params_}\n",
    "The best score: {model_rf.score(X_test,y_test)}\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2425cd5e-c9a3-42ca-980f-924ca1c3dc21",
   "metadata": {},
   "source": [
    "- Gradient Booster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ecd93258-7939-4d70-8c6b-e6a452c90d10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>params</th>\n",
       "      <th>param_simpleimputer__strategy</th>\n",
       "      <th>param_gradientboostingclassifier__n_estimators</th>\n",
       "      <th>param_gradientboostingclassifier__max_depth</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_fit_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.887</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.898</td>\n",
       "      <td>{'gradientboostingclassifier__max_depth': 4, '...</td>\n",
       "      <td>mean</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.472</td>\n",
       "      <td>26.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.882</td>\n",
       "      <td>{'gradientboostingclassifier__max_depth': 4, '...</td>\n",
       "      <td>mean</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.458</td>\n",
       "      <td>21.789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.854</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.890</td>\n",
       "      <td>{'gradientboostingclassifier__max_depth': 4, '...</td>\n",
       "      <td>median</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.018</td>\n",
       "      <td>2.248</td>\n",
       "      <td>24.397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.864</td>\n",
       "      <td>0.863</td>\n",
       "      <td>0.869</td>\n",
       "      <td>0.859</td>\n",
       "      <td>0.864</td>\n",
       "      <td>0.863</td>\n",
       "      <td>{'gradientboostingclassifier__max_depth': 4, '...</td>\n",
       "      <td>mean</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.497</td>\n",
       "      <td>17.594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.858</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.863</td>\n",
       "      <td>0.880</td>\n",
       "      <td>{'gradientboostingclassifier__max_depth': 4, '...</td>\n",
       "      <td>median</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.173</td>\n",
       "      <td>22.224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank_test_score  std_test_score  mean_test_score  split4_test_score  \\\n",
       "16                1           0.005            0.891              0.883   \n",
       "14                2           0.005            0.879              0.872   \n",
       "17                3           0.012            0.873              0.868   \n",
       "12                4           0.003            0.864              0.863   \n",
       "15                5           0.013            0.858              0.852   \n",
       "\n",
       "    split3_test_score  split2_test_score  split1_test_score  \\\n",
       "16              0.895              0.887              0.891   \n",
       "14              0.886              0.874              0.881   \n",
       "17              0.875              0.854              0.876   \n",
       "12              0.869              0.859              0.864   \n",
       "15              0.857              0.839              0.863   \n",
       "\n",
       "    split0_test_score                                             params  \\\n",
       "16              0.898  {'gradientboostingclassifier__max_depth': 4, '...   \n",
       "14              0.882  {'gradientboostingclassifier__max_depth': 4, '...   \n",
       "17              0.890  {'gradientboostingclassifier__max_depth': 4, '...   \n",
       "12              0.863  {'gradientboostingclassifier__max_depth': 4, '...   \n",
       "15              0.880  {'gradientboostingclassifier__max_depth': 4, '...   \n",
       "\n",
       "   param_simpleimputer__strategy  \\\n",
       "16                          mean   \n",
       "14                          mean   \n",
       "17                        median   \n",
       "12                          mean   \n",
       "15                        median   \n",
       "\n",
       "   param_gradientboostingclassifier__n_estimators  \\\n",
       "16                                             30   \n",
       "14                                             25   \n",
       "17                                             30   \n",
       "12                                             20   \n",
       "15                                             25   \n",
       "\n",
       "   param_gradientboostingclassifier__max_depth  std_score_time  \\\n",
       "16                                           4           0.003   \n",
       "14                                           4           0.005   \n",
       "17                                           4           0.003   \n",
       "12                                           4           0.003   \n",
       "15                                           4           0.003   \n",
       "\n",
       "    mean_score_time  std_fit_time  mean_fit_time  \n",
       "16            0.015         0.472         26.119  \n",
       "14            0.016         0.458         21.789  \n",
       "17            0.018         2.248         24.397  \n",
       "12            0.015         0.497         17.594  \n",
       "15            0.017         0.173         22.224  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_gb = pd.DataFrame(model_gb.cv_results_)\n",
    "results_gb.iloc[:,::-1].sort_values(\"rank_test_score\").head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d3267ecd-d3d4-47a5-9dd6-70ca9347a578",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Booster result:\n",
      "The best param: {'gradientboostingclassifier__max_depth': 4, 'gradientboostingclassifier__n_estimators': 30, 'simpleimputer__strategy': 'mean'}\n",
      "The best score: 0.8895763921941933\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"Gradient Booster result:\n",
    "The best param: {model_gb.best_params_}\n",
    "The best score: {model_gb.score(X_test,y_test)}\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1be9e625-43d5-4157-96a4-7f39307a9ab8",
   "metadata": {},
   "source": [
    "As we see, Although Gradient Booster take more time than Random Forest, it is scored less than Random Forest. \n",
    "\n",
    "For this reason, we recommend to use Randomforest for this model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cdac6af-961d-44a5-91bd-63f72d8159b0",
   "metadata": {},
   "source": [
    "Beside that, we should not choose the model with the best score. Instead, we could select the most proper model (running time, the score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e1d982a9-e52f-4bf7-b136-3540c6a7d0eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param_randomforestclassifier__max_depth</th>\n",
       "      <th>param_randomforestclassifier__n_estimators</th>\n",
       "      <th>param_simpleimputer__strategy</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>mean_fit_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.926</td>\n",
       "      <td>1.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.930</td>\n",
       "      <td>2.274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>25</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.958</td>\n",
       "      <td>3.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.958</td>\n",
       "      <td>3.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.959</td>\n",
       "      <td>4.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.958</td>\n",
       "      <td>7.307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.958</td>\n",
       "      <td>7.847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30</td>\n",
       "      <td>50</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.958</td>\n",
       "      <td>7.847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>75</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.931</td>\n",
       "      <td>9.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>40</td>\n",
       "      <td>75</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.959</td>\n",
       "      <td>11.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20</td>\n",
       "      <td>75</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.958</td>\n",
       "      <td>11.487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>75</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.960</td>\n",
       "      <td>11.691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   param_randomforestclassifier__max_depth  \\\n",
       "0                                       10   \n",
       "1                                       10   \n",
       "2                                       40   \n",
       "3                                       20   \n",
       "4                                       30   \n",
       "5                                       40   \n",
       "6                                       20   \n",
       "7                                       30   \n",
       "8                                       10   \n",
       "9                                       40   \n",
       "10                                      20   \n",
       "11                                      30   \n",
       "\n",
       "   param_randomforestclassifier__n_estimators param_simpleimputer__strategy  \\\n",
       "0                                          25                          mean   \n",
       "1                                          50                          mean   \n",
       "2                                          25                          mean   \n",
       "3                                          25                          mean   \n",
       "4                                          25                          mean   \n",
       "5                                          50                          mean   \n",
       "6                                          50                          mean   \n",
       "7                                          50                          mean   \n",
       "8                                          75                          mean   \n",
       "9                                          75                          mean   \n",
       "10                                         75                          mean   \n",
       "11                                         75                          mean   \n",
       "\n",
       "    mean_test_score  mean_fit_time  \n",
       "0             0.926          1.114  \n",
       "1             0.930          2.274  \n",
       "2             0.958          3.621  \n",
       "3             0.958          3.708  \n",
       "4             0.959          4.109  \n",
       "5             0.958          7.307  \n",
       "6             0.958          7.847  \n",
       "7             0.958          7.847  \n",
       "8             0.931          9.750  \n",
       "9             0.959         11.109  \n",
       "10            0.958         11.487  \n",
       "11            0.960         11.691  "
      ]
     },
     "execution_count": 25,
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    "mask_1 = (results_rf.param_simpleimputer__strategy == 'mean')\n",
    "visual_ = results_rf[mask_1].groupby(['param_randomforestclassifier__max_depth',\n",
    "                 'param_randomforestclassifier__n_estimators',\n",
    "                 'param_simpleimputer__strategy'],as_index=False).agg({'mean_test_score':'mean',\n",
    "                                                        'mean_fit_time':'mean'}).sort_values('mean_fit_time').reset_index(drop=True)\n",
    "visual_"
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          "text": "Mean Fit Time [seconds]"
         },
         "type": "linear"
        }
       }
      },
      "image/png": "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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "fig = px.bar(data_frame=visual_,\n",
    "             x='max depth-estimators',\n",
    "             y='mean_fit_time',\n",
    "             color='mean_test_score',\n",
    "             color_continuous_scale= 'burgyl',\n",
    "             title='Measure the proper model according to mean time and mean score',\n",
    "             hover_data=['param_simpleimputer__strategy'],\n",
    "             labels={'param_simpleimputer__strategy':'Imputer Strategy'}\n",
    "            )\n",
    "fig.update_layout(xaxis_title='Max Depth - Number of Estimators',yaxis_title='Mean Fit Time [seconds]')\n",
    "fig.show()"
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  },
  {
   "cell_type": "markdown",
   "id": "4503e79f-5894-4f21-a3c3-3b878f14e07b",
   "metadata": {},
   "source": [
    "## Evaluate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba4d358f-8524-45da-8dd5-27c0098d48c0",
   "metadata": {},
   "source": [
    "Calculate the training and test accuracy scores for `model`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9f1c5f97-e90d-4211-a7e3-5d5c6f5295db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 1.0\n",
      "Validation Accuracy: 0.9605\n"
     ]
    }
   ],
   "source": [
    "acc_train = model_rf.score(X_train,y_train)\n",
    "acc_test = model_rf.score(X_test,y_test)\n",
    "\n",
    "print(\"Training Accuracy:\", round(acc_train, 4))\n",
    "print(\"Validation Accuracy:\", round(acc_test, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82d96309-0271-44d7-8777-33a198ab47a3",
   "metadata": {},
   "source": [
    "Next, we're going to use a confusion matrix to see how our model performs. To better understand the values we'll see in the matrix, let's first count how many observations in our test set belong to the positive and negative classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2a50d27c-2e19-4101-ac53-626cd42c1aab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1994\n",
       "1     107\n",
       "Name: bankrupt, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1e1802df-7cd2-4a6c-bfb8-932b5b9bc189",
   "metadata": {},
   "outputs": [
    {
     "data": {
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rV64m4T+6BhFoAO6akq8P3o7Q119Z1aSpXb1vPqGuvy7Rw0Pb+zs0+FBDf2rhkSNH9O2336pVq1aSpJSUFJ04cUI7d+5UUlKSJOntt9+Ww+FQcnKyc8zDDz+syspKNW7cWJKUmZmpxMTEWrcIJD8nA1arVUlJSdq0aZMGDRokSXI4HNq0aZPGjh3rz9AC1mcfhenB33Vwrj+TcbEk6fpbijRxYZ6KChvrmYyLdeKbRoqOqVKf3xdp6B9dk7UjB0K08vtJhrHxFbrt/kINHv21y5gXH2+lzJeinev39U2UJM375351+zVXHKBhadaySpMW5yk6pkqnTwXr4L5QPTy0vXZlRfg7NFzASkpKnN/yJengwYPKyclRdHS0oqOjNWPGDA0ZMkRxcXE6cOCAHnzwQXXo0EGpqamSpE6dOqlfv34aNWqUli1bpsrKSo0dO1a33nqrbDabJGno0KGaMWOGRowYocmTJ2vPnj1atGiRnnzySY9itRiG4dc62Lp165SWlqZnnnlGv/zlL7Vw4UK99NJL+vTTT2vMJfip4uJiRUVF6fhn7RUZcUF1PIBaS7V193cIQJ2pMir1jv6lkydP+qQPfzbVnxU3Z96lxuHWcx/gRmVphV69fmWtY33nnXfUu3fvGtvT0tK0dOlSDRo0SB9++KFOnDghm82mvn37atasWS6ffUVFRRo7dqxee+01BQUFaciQIVq8eLGaNv3hvi67d+9Wenq6PvjgA7Vs2VLjxo3T5MmTPXpvfr8W7A9/+IO+/vprTZs2TQUFBerevbvefPPNcyYCAAB4or7bBL169dLPfd/+73//e85zREdHa82aNT87pmvXrtq6datHsf2U35MBSRo7dixtAQAA/KRBJAMAANS183m+wE+PD1QkAwAAU2joVxP4E7PuAAAwOSoDAABToDLgHskAAMAUSAbco00AAIDJURkAAJgClQH3SAYAAKZgyLvLAwP5sVUkAwAAU6Ay4B5zBgAAMDkqAwAAU6Ay4B7JAADAFEgG3KNNAACAyVEZAACYApUB90gGAACmYBgWGV58oHtzbENHmwAAAJOjMgAAMAWHLF7ddMibYxs6kgEAgCkwZ8A92gQAAJgclQEAgCkwgdA9kgEAgCnQJnCPZAAAYApUBtxjzgAAACZHZQAAYAqGl22CQK4MkAwAAEzBkGQY3h0fqGgTAABgclQGAACm4JBFFu5AeFYkAwAAU+BqAvdoEwAAYHJUBgAApuAwLLJw06GzIhkAAJiCYXh5NUEAX05AmwAAAJOjMgAAMAUmELpHMgAAMAWSAfdoEwAATKH6qYXeLJ7IysrSwIEDZbPZZLFYtH79eue+yspKTZ48WV26dFF4eLhsNpuGDx+uo0ePupyjbdu2slgsLsvcuXNdxuzevVs9evRQaGio4uPjNW/ePI9/NyQDAADUgdLSUnXr1k1Lliypse/06dPatWuXpk6dql27dumVV15Rbm6ubrzxxhpjZ86cqfz8fOcybtw4577i4mL17dtXCQkJ2rlzp+bPn6+MjAw9++yzHsVKmwAAYAr1fTVB//791b9//7Pui4qKUmZmpsu2p59+Wr/85S+Vl5enNm3aOLdHREQoLi7urOdZvXq1KioqtGLFClmtVl122WXKycnRggULNHr06FrHSmUAAGAKZ5IBixfLmfMUFxe7LOXl5T6J7+TJk7JYLGrWrJnL9rlz56pFixa64oorNH/+fFVVVTn3ZWdnq2fPnrJarc5tqampys3N1fHjx2v92iQDAAB4ID4+XlFRUc5lzpw5Xp+zrKxMkydP1m233abIyEjn9vvvv19r167V5s2bdc8992j27Nl68MEHnfsLCgoUGxvrcq7q9YKCglq/Pm0CAIAp+OpqgsOHD7t8YIeEhHgVV2VlpW655RYZhqGlS5e67JswYYLz565du8pqteqee+7RnDlzvH7dHyMZAACYgvH94s3xkhQZGemSDHijOhH48ssv9fbbb5/zvMnJyaqqqtKhQ4eUmJiouLg4FRYWuoypXnc3z+BsaBMAAOAH1YnA559/ro0bN6pFixbnPCYnJ0dBQUGKiYmRJKWkpCgrK0uVlZXOMZmZmUpMTFTz5s1rHQuVAQCAKdT3TYdKSkq0f/9+5/rBgweVk5Oj6OhotWrVSr/73e+0a9cubdiwQXa73dnjj46OltVqVXZ2trZv367evXsrIiJC2dnZGj9+vG6//XbnB/3QoUM1Y8YMjRgxQpMnT9aePXu0aNEiPfnkkx7FSjIAADAHX/UJamnHjh3q3bu3c726/5+WlqaMjAz9+9//liR1797d5bjNmzerV69eCgkJ0dq1a5WRkaHy8nK1a9dO48ePd5lHEBUVpbfeekvp6elKSkpSy5YtNW3aNI8uK5RIBgAAZuFlZUAeHturVy8ZP3Nzgp/bJ0lXXnml3nvvvXO+TteuXbV161aPYvsp5gwAAGByVAYAAKZQ33cgvJCQDAAATIGnFrpHmwAAAJOjMgAAMAfD4vEkwBrHByiSAQCAKTBnwD3aBAAAmByVAQCAOdTzTYcuJCQDAABT4GoC92qVDFTfMrE2brzxxvMOBgAA1L9aJQODBg2q1cksFovsdrs38QAAUHcCuNTvjVolAw6Ho67jAACgTtEmcM+rqwnKysp8FQcAAHXL8MESoDxOBux2u2bNmqWLL75YTZs21RdffCFJmjp1qpYvX+7zAAEAQN3yOBl47LHHtGrVKs2bN09Wq9W5/fLLL9fzzz/v0+AAAPAdiw+WwORxMvDiiy/q2Wef1bBhwxQcHOzc3q1bN3366ac+DQ4AAJ+hTeCWx8nAV199pQ4dOtTY7nA4VFlZ6ZOgAABA/fE4GejcubO2bt1aY/s///lPXXHFFT4JCgAAn6My4JbHdyCcNm2a0tLS9NVXX8nhcOiVV15Rbm6uXnzxRW3YsKEuYgQAwHs8tdAtjysDN910k1577TVt3LhR4eHhmjZtmvbt26fXXntN119/fV3ECAAA6tB5PZugR48eyszM9HUsAADUGR5h7N55P6hox44d2rdvn6Qz8wiSkpJ8FhQAAD7HUwvd8jgZOHLkiG677Tb973//U7NmzSRJJ06c0K9//WutXbtWrVu39nWMAACgDnk8Z2DkyJGqrKzUvn37VFRUpKKiIu3bt08Oh0MjR46sixgBAPBe9QRCb5YA5XFlYMuWLdq2bZsSExOd2xITE/XUU0+pR48ePg0OAABfsRhnFm+OD1QeJwPx8fFnvbmQ3W6XzWbzSVAAAPgccwbc8rhNMH/+fI0bN047duxwbtuxY4ceeOABPf744z4NDgAA1L1aVQaaN28ui+WHXklpaamSk5PVqNGZw6uqqtSoUSPdfffdGjRoUJ0ECgCAV7jpkFu1SgYWLlxYx2EAAFDHaBO4VatkIC0tra7jAAAAfnLeNx2SpLKyMlVUVLhsi4yM9CogAADqBJUBtzyeQFhaWqqxY8cqJiZG4eHhat68ucsCAECDxFML3fI4GXjwwQf19ttva+nSpQoJCdHzzz+vGTNmyGaz6cUXX6yLGAEAQB3yuE3w2muv6cUXX1SvXr101113qUePHurQoYMSEhK0evVqDRs2rC7iBADAO1xN4JbHlYGioiK1b99e0pn5AUVFRZKk3/72t8rKyvJtdAAA+Ej1HQi9WQKVx8lA+/btdfDgQUnSpZdeqpdeeknSmYpB9YOLAADAhcPjZOCuu+7SRx99JEl66KGHtGTJEoWGhmr8+PGaNGmSzwMEAMAn6nkCYVZWlgYOHCibzSaLxaL169e7hmMYmjZtmlq1aqUmTZqoT58++vzzz13GFBUVadiwYYqMjFSzZs00YsQIlZSUuIzZvXu3evToodDQUMXHx2vevHmeBarzSAbGjx+v+++/X5LUp08fffrpp1qzZo0+/PBDPfDAAx4HAABAICotLVW3bt20ZMmSs+6fN2+eFi9erGXLlmn79u0KDw9XamqqysrKnGOGDRumvXv3KjMzUxs2bFBWVpZGjx7t3F9cXKy+ffsqISFBO3fu1Pz585WRkaFnn33Wo1i9us+AJCUkJCghIcHb0wAAUKcs8vKphd//b3Fxscv2kJAQhYSE1Bjfv39/9e/f/6znMgxDCxcu1COPPKKbbrpJkvTiiy8qNjZW69ev16233qp9+/bpzTff1AcffKCrrrpKkvTUU0/phhtu0OOPPy6bzabVq1eroqJCK1askNVq1WWXXaacnBwtWLDAJWk4l1olA4sXL671CaurBgAABKL4+HiX9enTpysjI8Ojcxw8eFAFBQXq06ePc1tUVJSSk5OVnZ2tW2+9VdnZ2WrWrJkzEZDOVOSDgoK0fft23XzzzcrOzlbPnj1ltVqdY1JTU/WXv/xFx48fr/X9f2qVDDz55JO1OpnFYvFLMjAkKUWNLNZzDwQuQJZG3/k7BKDOWAxDqqqnF/PRpYWHDx92udvu2aoC51JQUCBJio2NddkeGxvr3FdQUKCYmBiX/Y0aNVJ0dLTLmHbt2tU4R/U+nyYD1VcPAABwwfLR7YgjIyMD7tb7Hk8gBAAA3omLi5MkFRYWumwvLCx07ouLi9OxY8dc9ldVVamoqMhlzNnO8ePXqA2SAQCAOTSgZxO0a9dOcXFx2rRpk3NbcXGxtm/frpSUFElSSkqKTpw4oZ07dzrHvP3223I4HEpOTnaOycrKUmVlpXNMZmamEhMTPXpeEMkAAMAU6vsOhCUlJcrJyVFOTo6kMy33nJwc5eXlyWKx6I9//KMeffRR/fvf/9bHH3+s4cOHy2azadCgQZKkTp06qV+/fho1apTef/99/e9//9PYsWN16623ymazSZKGDh0qq9WqESNGaO/evVq3bp0WLVqkCRMmeBSr15cWAgCAmnbs2KHevXs716s/oNPS0rRq1So9+OCDKi0t1ejRo3XixAn99re/1ZtvvqnQ0FDnMatXr9bYsWN13XXXKSgoSEOGDHG5wi8qKkpvvfWW0tPTlZSUpJYtW2ratGkeXVYoSRbDMC7Yuy0XFxcrKipK10YM42oCBCzjO64mQOCqMiq1ueplnTx5ss4m5VV/VrR99DEF/eiD1lOOsjIdeuThOo3VX86rTbB161bdfvvtSklJ0VdffSVJ+tvf/qZ3333Xp8EBAOAzDWjOQEPjcTLw8ssvKzU1VU2aNNGHH36o8vJySdLJkyc1e/ZsnwcIAADqlsfJwKOPPqply5bpueeeU+PGjZ3bf/Ob32jXrl0+DQ4AAF/hEcbueTyBMDc3Vz179qyxPSoqSidOnPBFTAAA+J6P7kAYiDyuDMTFxWn//v01tr/77rtq3769T4ICAMDnmDPglsfJwKhRo/TAAw9o+/btslgsOnr0qFavXq2JEydqzJgxdREjAACoQx63CR566CE5HA5dd911On36tHr27KmQkBBNnDhR48aNq4sYAQDwmrd9f+YM/IjFYtHDDz+sSZMmaf/+/SopKVHnzp3VtGnTuogPAADf8NGDigLRed+B0Gq1qnPnzr6MBQAA+IHHyUDv3r1lsbifUfn22297FRAAAHXC28sDqQz8oHv37i7rlZWVysnJ0Z49e5SWluaruAAA8C3aBG55nAw8+eSTZ92ekZGhkpISrwMCAAD1y2ePML799tu1YsUKX50OAADf4j4DbvnsEcbZ2dkuj10EAKAh4dJC9zxOBgYPHuyybhiG8vPztWPHDk2dOtVngQEAgPrhcTIQFRXlsh4UFKTExETNnDlTffv29VlgAACgfniUDNjtdt11113q0qWLmjdvXlcxAQDge1xN4JZHEwiDg4PVt29fnk4IALjg8Ahj9zy+muDyyy/XF198URexAAAAP/A4GXj00Uc1ceJEbdiwQfn5+SouLnZZAABosLis8KxqPWdg5syZ+tOf/qQbbrhBknTjjTe63JbYMAxZLBbZ7XbfRwkAgLeYM+BWrZOBGTNm6N5779XmzZvrMh4AAFDPap0MGMaZlOiaa66ps2AAAKgr3HTIPY8uLfy5pxUCANCg0SZwy6NkoGPHjudMCIqKirwKCAAA1C+PkoEZM2bUuAMhAAAXAtoE7nmUDNx6662KiYmpq1gAAKg7tAncqvV9BpgvAABAYPL4agIAAC5IVAbcqnUy4HA46jIOAADqFHMG3PP4EcYAAFyQqAy45fGzCQAAQGChMgAAMAcqA26RDAAATIE5A+7RJgAAwORIBgAA5mD4YPFA27ZtZbFYaizp6emSpF69etXYd++997qcIy8vTwMGDFBYWJhiYmI0adIkVVVVne9vwC3aBAAAU6jvNsEHH3wgu93uXN+zZ4+uv/56/f73v3duGzVqlGbOnOlcDwsLc/5st9s1YMAAxcXFadu2bcrPz9fw4cPVuHFjzZ49+/zfyFmQDAAAUAcuuugil/W5c+fqkksu0TXXXOPcFhYWpri4uLMe/9Zbb+mTTz7Rxo0bFRsbq+7du2vWrFmaPHmyMjIyZLVafRYrbQIAgDn4qE1QXFzsspSXl5/zpSsqKvR///d/uvvuu11u77969Wq1bNlSl19+uaZMmaLTp08792VnZ6tLly6KjY11bktNTVVxcbH27t17/r+Hs6AyAAAwBx9dWhgfH++yefr06crIyPjZQ9evX68TJ07ozjvvdG4bOnSoEhISZLPZtHv3bk2ePFm5ubl65ZVXJEkFBQUuiYAk53pBQYEXb6QmkgEAADxw+PBhRUZGOtdDQkLOeczy5cvVv39/2Ww257bRo0c7f+7SpYtatWql6667TgcOHNAll1zi26DPgTYBAMAULD5YJCkyMtJlOVcy8OWXX2rjxo0aOXLkz45LTk6WJO3fv1+SFBcXp8LCQpcx1evu5hmcL5IBAIA51POlhdVWrlypmJgYDRgw4GfH5eTkSJJatWolSUpJSdHHH3+sY8eOOcdkZmYqMjJSnTt3Pr9g3KBNAAAwBX/cgdDhcGjlypVKS0tTo0Y/fOQeOHBAa9as0Q033KAWLVpo9+7dGj9+vHr27KmuXbtKkvr27avOnTvrjjvu0Lx581RQUKBHHnlE6enptWpNeIJkAACAOrJx40bl5eXp7rvvdtlutVq1ceNGLVy4UKWlpYqPj9eQIUP0yCOPOMcEBwdrw4YNGjNmjFJSUhQeHq60tDSX+xL4CskAAMAc/PCgor59+8owah4YHx+vLVu2nPP4hIQEvfHGG56/sIdIBgAA5hHADxvyBhMIAQAwOSoDAABT4BHG7pEMAADMwQ9zBi4UtAkAADA5KgMAAFOgTeAeyQAAwBxoE7hFmwAAAJOjMgAAMAXaBO6RDAAAzIE2gVskAwAAcyAZcIs5AwAAmByVAQCAKTBnwD2SAQCAOdAmcIs2AQAAJkdlAABgChbDkMU4/6/33hzb0JEMAADMgTaBW7QJAAAwOSoDAABT4GoC90gGAADmQJvALdoEAACYHJUBAIAp0CZwj2QAAGAOtAncIhkAAJgClQH3mDMAAIDJURkAAJgDbQK3SAYAAKYRyKV+b9AmAADA5KgMAADMwTDOLN4cH6BIBgAApsDVBO7RJgAAwOSoDAAAzIGrCdwiGQAAmILFcWbx5vhARZsAAACTozKAGoKCDA0bl6drbzym5i0rVXTMqsxXY/T3v8ZLsnw/ytAd9+ep3+8LFB5p1ye7IvR0Rgcd/bKJP0MHauUP6fn6Tb8Tan1JmSrKgvTJznCtmNNaR74IlSTFti7XC9v2nPXYx8a019bXm9dnuPAV2gRukQyght+POqIBt+Xrickd9eX+MHW8vETj53yu0lON9O+/2b4f85VuvOOonnioowqOhGr4A1/q0eV7dM8NSaqsoOCEhq1Lcolee+EifbY7XEHBhu568Cs99n+fa/R1nVX+XbC+PmrVbUldXY7pP/Rr/e6eQn2wOdJPUcNbXE3gnl//amdlZWngwIGy2WyyWCxav369P8PB9zpdUaz3NrXQB1uideyrUL3735ba9W4zJXY99f0IQ4OGf6W1S+P13qYWOpQbrscf7KgWMRX6dZ9v/Ro7UBuPDP+FMv/ZUl9+1kQH94XpiT+1VWzrCv2iy2lJksNh0fGvG7ssv049oa0bmqvsdLCfo8d5q77PgDeLBzIyMmSxWFyWSy+91Lm/rKxM6enpatGihZo2baohQ4aosLDQ5Rx5eXkaMGCAwsLCFBMTo0mTJqmqqsonv44f82syUFpaqm7dumnJkiX+DAM/se/DSHX/1Qld3PY7SVK7xBJdllSsHVlnSqNxrcsVHVOpD7c1cx5zuqSRcj+K0KVXFPsjZMArYRF2SdKpE2cvlnboUqoOl3+nN9e1rM+wEAAuu+wy5efnO5d3333XuW/8+PF67bXX9I9//ENbtmzR0aNHNXjwYOd+u92uAQMGqKKiQtu2bdMLL7ygVatWadq0aT6P069tgv79+6t///61Hl9eXq7y8nLnenExHzx14aVnWyusqV3P/menHHaLgoINvfBkgja/FiNJan5RhSTp+LdWl+OOf2tV85aV9R4v4A2LxdC9GUe094NwffnZ2ee8pP7hW335eaj27Wxaz9HBl3zVJvjpZ09ISIhCQkLOekyjRo0UFxdXY/vJkye1fPlyrVmzRtdee60kaeXKlerUqZPee+89/epXv9Jbb72lTz75RBs3blRsbKy6d++uWbNmafLkycrIyJDVaq1x3vN1QTV358yZo6ioKOcSHx/v75ACUs/+36j3wGOa96dEjRvcXU881FFD7v5KfQYVnvtg4AKT/mie2nb8TnPS2591vzXEod43Fem/a6kKXPAMHyyS4uPjXT6L5syZ4/YlP//8c9lsNrVv317Dhg1TXl6eJGnnzp2qrKxUnz59nGMvvfRStWnTRtnZ2ZKk7OxsdenSRbGxsc4xqampKi4u1t69e33wC/nBBTWBcMqUKZowYYJzvbi4mISgDox48KBeera1trxxkSTp0GfhirGV6ZZ7jmjj+lgd//pMNtq8RYXz5+r1A5+G+yVm4HzcNzNPyded1MTfJ+qbgrN/y+ox4LhCmji06eXoeo4ODdXhw4cVGfnDRFJ3VYHk5GStWrVKiYmJys/P14wZM9SjRw/t2bNHBQUFslqtatasmcsxsbGxKigokCQVFBS4JALV+6v3+dIFlQz8XCkGvhMS6pBhWFy2OewWWb6vkRUcCVHRscbqnnJCX3x6pmwaFl6lxG6n9PrfW9V7vIDnDN0387B+3e+EHrylowoPu/+7kvqHb/TexiidLGpcj/GhLviqTRAZGemSDLjz4zZ4165dlZycrISEBL300ktq0qRhXYZ9QbUJUD+2b47Wrfce1tXXFCnm4jL9us83GnzXV8re2OL7ERatf/Fi3TrmsJKv/VZtO5bqT/M+07fHrNrmHAM0XOmPHta1NxfpL+Pa6bvSYDW/qFLNL6qUNcT1FnOtEsp0eXKJ3vw7LYKAUM9XE/xUs2bN1LFjR+3fv19xcXGqqKjQiRMnXMYUFhY65xjExcXVuLqgev1s8xC8cUFVBlA/lj7aXsMfyFP69ANq1uLMTYfeWNdKa5b80JL5x3MXK7SJXffP3K+mkVXauzNSU0dezj0GcEEYOPxrSdL8f3zmsv2JCQnK/OcPH/ypf/hW3+Q31q4s7i0A75WUlOjAgQO64447lJSUpMaNG2vTpk0aMmSIJCk3N1d5eXlKSUmRJKWkpOixxx7TsWPHFBNzZgJ3ZmamIiMj1blzZ5/GZjEM/z2guaSkRPv375ckXXHFFVqwYIF69+6t6OhotWnT5pzHFxcXKyoqStdGDFMji+9mVQINifHdd/4OAagzVUalNle9rJMnT9aq9H4+qj8rUvrPVKPGoed9nqrKMmX/Z1qtY504caIGDhyohIQEHT16VNOnT1dOTo4++eQTXXTRRRozZozeeOMNrVq1SpGRkRo3bpwkadu2bZLOXFrYvXt32Ww2zZs3TwUFBbrjjjs0cuRIzZ49+7zfx9n4tTKwY8cO9e7d27lePTkwLS1Nq1at8lNUAICAVM+3Iz5y5Ihuu+02ffvtt7rooov029/+Vu+9954uuujM5Ownn3xSQUFBGjJkiMrLy5Wamqq//vWvzuODg4O1YcMGjRkzRikpKQoPD1daWppmzpzpxZs4O79WBrxFZQBmQGUAgaxeKwP9fFAZeLP2lYELCXMGAACmwLMJ3CMZAACYg8M4s3hzfIAiGQAAmAOPMHaL68AAADA5KgMAAFOwyMs5Az6LpOEhGQAAmIO3dxG8cC++OyfaBAAAmByVAQCAKXBpoXskAwAAc+BqArdoEwAAYHJUBgAApmAxDFm8mATozbENHckAAMAcHN8v3hwfoGgTAABgclQGAACmQJvAPZIBAIA5cDWBWyQDAABz4A6EbjFnAAAAk6MyAAAwBe5A6B7JAADAHGgTuEWbAAAAk6MyAAAwBYvjzOLN8YGKZAAAYA60CdyiTQAAgMlRGQAAmAM3HXKLZAAAYArcjtg92gQAAJgclQEAgDkwgdAtkgEAgDkYkry5PDBwcwGSAQCAOTBnwD3mDAAAYHJUBgAA5mDIyzkDPoukwSEZAACYAxMI3aJNAACAyVEZAACYg0OSxcvjAxTJAADAFLiawD3aBAAA1IE5c+bo6quvVkREhGJiYjRo0CDl5ua6jOnVq5csFovLcu+997qMycvL04ABAxQWFqaYmBhNmjRJVVVVPo2VygAAwBzqeQLhli1blJ6erquvvlpVVVX685//rL59++qTTz5ReHi4c9yoUaM0c+ZM53pYWJjzZ7vdrgEDBiguLk7btm1Tfn6+hg8frsaNG2v27Nnn/15+gmQAAGAO9ZwMvPnmmy7rq1atUkxMjHbu3KmePXs6t4eFhSkuLu6s53jrrbf0ySefaOPGjYqNjVX37t01a9YsTZ48WRkZGbJarZ6/j7OgTQAAgAeKi4tdlvLy8lodd/LkSUlSdHS0y/bVq1erZcuWuvzyyzVlyhSdPn3auS87O1tdunRRbGysc1tqaqqKi4u1d+9eH7ybM6gMAADMwUeVgfj4eJfN06dPV0ZGxs8e6nA49Mc//lG/+c1vdPnllzu3Dx06VAkJCbLZbNq9e7cmT56s3NxcvfLKK5KkgoICl0RAknO9oKDg/N/LT5AMAADMwUeXFh4+fFiRkZHOzSEhIec8ND09XXv27NG7777rsn306NHOn7t06aJWrVrpuuuu04EDB3TJJZd4EaxnaBMAAEyh+tJCbxZJioyMdFnOlQyMHTtWGzZs0ObNm9W6deufHZucnCxJ2r9/vyQpLi5OhYWFLmOq193NMzgfJAMAANQBwzA0duxYvfrqq3r77bfVrl27cx6Tk5MjSWrVqpUkKSUlRR9//LGOHTvmHJOZmanIyEh17tzZZ7HSJgAAmEM9X02Qnp6uNWvW6F//+pciIiKcPf6oqCg1adJEBw4c0Jo1a3TDDTeoRYsW2r17t8aPH6+ePXuqa9eukqS+ffuqc+fOuuOOOzRv3jwVFBTokUceUXp6eq3aE7VFMgAAMAeHIVm8SAYcnh27dOlSSWduLPRjK1eu1J133imr1aqNGzdq4cKFKi0tVXx8vIYMGaJHHnnEOTY4OFgbNmzQmDFjlJKSovDwcKWlpbncl8AXSAYAAKgDxjkqCfHx8dqyZcs5z5OQkKA33njDV2GdFckAAMAceISxWyQDAACT8DIZUOAmA1xNAACAyVEZAACYA20Ct0gGAADm4DDkVanfw6sJLiS0CQAAMDkqAwAAczAcZxZvjg9QJAMAAHNgzoBbJAMAAHNgzoBbzBkAAMDkqAwAAMyBNoFbJAMAAHMw5GUy4LNIGhzaBAAAmByVAQCAOdAmcItkAABgDg6HJC/uFeAI3PsM0CYAAMDkqAwAAMyBNoFbJAMAAHMgGXCLNgEAACZHZQAAYA7cjtgtkgEAgCkYhkOGF08e9ObYho5kAABgDobh3bd75gwAAIBARWUAAGAOhpdzBgK4MkAyAAAwB4dDsnjR9w/gOQO0CQAAMDkqAwAAc6BN4BbJAADAFAyHQ4YXbYJAvrSQNgEAACZHZQAAYA60CdwiGQAAmIPDkCwkA2dDmwAAAJOjMgAAMAfDkOTNfQYCtzJAMgAAMAXDYcjwok1gkAwAAHCBMxzyrjLApYUAACBAURkAAJgCbQL3SAYAAOZAm8CtCzoZqM7SqoxKP0cC1B2Df98IYNV/v+vjW3eVKr2651CVAve/xQs6GTh16pQkKavkJT9HAgDwxqlTpxQVFVUn57ZarYqLi9O7BW94fa64uDhZrVYfRNWwWIwLuAnicDh09OhRRUREyGKx+DscUyguLlZ8fLwOHz6syMhIf4cD+BT/vuufYRg6deqUbDabgoLqbk57WVmZKioqvD6P1WpVaGioDyJqWC7oykBQUJBat27t7zBMKTIykj+WCFj8+65fdVUR+LHQ0NCA/BD3FS4tBADA5EgGAAAwOZIBeCQkJETTp09XSEiIv0MBfI5/3zCrC3oCIQAA8B6VAQAATI5kAAAAkyMZAADA5EgGAAAwOZIB1NqSJUvUtm1bhYaGKjk5We+//76/QwJ8IisrSwMHDpTNZpPFYtH69ev9HRJQr0gGUCvr1q3ThAkTNH36dO3atUvdunVTamqqjh075u/QAK+VlpaqW7duWrJkib9DAfyCSwtRK8nJybr66qv19NNPSzrzXIj4+HiNGzdODz30kJ+jA3zHYrHo1Vdf1aBBg/wdClBvqAzgnCoqKrRz50716dPHuS0oKEh9+vRRdna2HyMDAPgCyQDO6ZtvvpHdbldsbKzL9tjYWBUUFPgpKgCAr5AMAABgciQDOKeWLVsqODhYhYWFLtsLCwsVFxfnp6gAAL5CMoBzslqtSkpK0qZNm5zbHA6HNm3apJSUFD9GBgDwhUb+DgAXhgkTJigtLU1XXXWVfvnLX2rhwoUqLS3VXXfd5e/QAK+VlJRo//79zvWDBw8qJydH0dHRatOmjR8jA+oHlxai1p5++mnNnz9fBQUF6t69uxYvXqzk5GR/hwV47Z133lHv3r1rbE9LS9OqVavqPyCgnpEMAABgcswZAADA5EgGAAAwOZIBAABMjmQAAACTIxkAAMDkSAYAADA5kgEAAEyOZAAAAJMjGQC8dOedd2rQoEHO9V69eumPf/xjvcfxzjvvyGKx6MSJE27HWCwWrV+/vtbnzMjIUPfu3b2K69ChQ7JYLMrJyfHqPADqDskAAtKdd94pi8Uii8Uiq9WqDh06aObMmaqqqqrz137llVc0a9asWo2tzQc4ANQ1HlSEgNWvXz+tXLlS5eXleuONN5Senq7GjRtrypQpNcZWVFTIarX65HWjo6N9ch4AqC9UBhCwQkJCFBcXp4SEBI0ZM0Z9+vTRv//9b0k/lPYfe+wx2Ww2JSYmSpIOHz6sW265Rc2aNVN0dLRuuukmHTp0yHlOu92uCRMmqFmzZmrRooUefPBB/fTxHj9tE5SXl2vy5MmKj49XSEiIOnTooOXLl+vQoUPOh+M0b95cFotFd955p6Qzj4ieM2eO2rVrpyZNmqhbt2765z//6fI6b7zxhjp27KgmTZqod+/eLnHW1uTJk9WxY0eFhYWpffv2mjp1qiorK2uMe+aZZxQfH6+wsDDdcsstOnnypMv+559/Xp06dVJoaKguvfRS/fWvf/U4FgD+QzIA02jSpIkqKiqc65s2bVJubq4yMzO1YcMGVVZWKjU1VREREdq6dav+97//qWnTpurXr5/zuCeeeEKrVq3SihUr9O6776qoqEivvvrqz77u8OHD9fe//12LFy/Wvn379Mwzz6hp06aKj4/Xyy+/LEnKzc1Vfn6+Fi1aJEmaM2eOXnzxRS1btkx79+7V+PHjdfvtt2vLli2SziQtgwcP1sCBA5WTk6ORI0fqoYce8vh3EhERoVWrVumTTz7RokWL9Nxzz+nJJ590GbN//3699NJLeu211/Tmm2/qww8/1H333efcv3r1ak2bNk2PPfaY9u3bp9mzZ2vq1Kl64YUXPI4HgJ8YQABKS0szbrrpJsMwDMPhcBiZmZlGSEiIMXHiROf+2NhYo7y83HnM3/72NyMxMdFwOBzObeXl5UaTJk2M//73v4ZhGEarVq2MefPmOfdXVlYarVu3dr6WYRjGNddcYzzwwAOGYRhGbm6uIcnIzMw8a5ybN282JBnHjx93bisrKzPCwsKMbdu2uYwdMWKEcdtttxmGYRhTpkwxOnfu7LJ/8uTJNc71U5KMV1991e3++fPnG0lJSc716dOnG8HBwcaRI0ec2/7zn/8YQUFBRn5+vmEYhnHJJZcYa9ascTnPrFmzjJSUFMMwDOPgwYOGJOPDDz90+7oA/Is5AwhYGzZsUNOmTVVZWSmHw6GhQ4cqIyPDub9Lly4u8wQ++ugj7d+/XxERES7nKSsr04EDB3Ty5Enl5+crOTnZua9Ro0a66qqrarQKquXk5Cg4OFjXXHNNrePev3+/Tp8+reuvv95le0VFha644gpJ0r59+1zikKSUlJRav0a1devWafHixTpw4IBKSkpUVVWlyMhIlzFt2rTRxRdf7PI6DodDubm5ioiI0IEDBzRixAiNGjXKOaaqqkpRUVEexwPAP0gGELB69+6tpUuXymq1ymazqVEj13/u4eHhLuslJSVKSkrS6tWra5zroosuOq8YmjRp4vExJSUlkqTXX3/d5UNYOjMPwleys7M1bNgwzZgxQ6mpqYqKitLatWv1xBNPeBzrc889VyM5CQ4O9lmsAOoWyQACVnh4uDp06FDr8VdeeaXWrVunmJiYGt+Oq7Vq1Urbt29Xz549JZ35Brxz505deeWVZx3fpUsXORwObdmyRX369Kmxv7oyYbfbnds6d+6skJAQ5eXlua0odOrUyTkZstp777137jf5I9u2bVNCQoIefvhh57Yvv/yyxri8vDwdPXpUNpvN+TpBQUFKTExUbGysbDabvvjiCw0bNsyj1wfQcDCBEPjesGHD1LJlS910003aunWrDh48qHfeeUf333+/jhw5Ikl64IEHNHfuXK1fv16ffvqp7rvvvp+9R0Dbtm2Vlpamu+++W+vXr3ee86WXXpIkJSQkyGKxaMOGDfr6669VUlKiiIgITZw4UePHj9cLL7ygAwcOaNeuXXrqqaeck/Luvfdeff7555o0aZJyc3O1Zs0arVq1yqP3+4tf/EJ5eXlau3atDhw4oMWLF591MmRoaKjS0tL00UcfaevWrbr//vt1yy23KC4uTpI0Y8YMzZkzR4sXL9Znn32mjz/+WCtXrtSCBQs8igeA/5AMAN8LCwtTVlaW2rRpo8GDB6tTp04aMWKEysrKnJWCP/3pT7rjjjuUlpamlJQURURE6Oabb/7Z8y5dulS/+93vdN999+nSSy/VqFGjVFpaKkm6+OKLNWPGDD300EOKjY3V2LFjJUmzZs3S1KlTNWfOHHXq1En9+vXT66+/rnbt2kk608d/+eWXtX79enXr1k3Lli3T7NmzPXq/N954o8aPH6+xY8eqe/fu2rZtm6ZOnVpjXIcOHTR48GDdcMMN6tu3r7p27epy6eDIkSP1/PPPa+XKlerSpYuuueYarVq1yhkrgIbPYrib+QQAAEyBygAAACZHMgAAgMmRDAAAYHIkAwAAmBzJAAAAJkcyAACAyZEMAABgciQDAACYHMkAAAAmRzIAAIDJkQwAAGBy/x+qhYWDeJGkigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot confusion matrix\n",
    "ConfusionMatrixDisplay.from_estimator(model_rf,X_test,y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4495665b-b411-483b-9cfe-529b3c1fe678",
   "metadata": {},
   "source": [
    "This matrix is a great reminder of how imbalanced our data is, and of why accuracy isn't always the best metric for judging whether or not a model is giving us what we want. \n",
    "\n",
    "The accuracy score will be amazing, but it won't tell us what we really need to know.\n",
    "\n",
    "Instead, we can evaluate our model using two new metrics: precision and recall. \n",
    " - The precision score is important when we want our model to only predict that a company will go bankrupt if its very confident in its prediction. \n",
    " - The recall score is important if we want to make sure to identify all the companies that will go bankrupt, even if that means being incorrect sometimes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ebef51dc-bf9f-47a6-a6bc-3ac83874e817",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98      1994\n",
      "           1       0.90      0.25      0.39       107\n",
      "\n",
      "    accuracy                           0.96      2101\n",
      "   macro avg       0.93      0.63      0.69      2101\n",
      "weighted avg       0.96      0.96      0.95      2101\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print classification report\n",
    "print(classification_report(y_test,model_rf.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e07c9fb4-ace6-4618-93ea-b94eebccf13d",
   "metadata": {},
   "source": [
    "Because there's a tradeoff between precision (the quality of our prediction) and recall(the true quantity of true label), we should select between the measure of how \"precise\" our model and the measure of our model's ability base on reality. \n",
    "\n",
    "Hence depend on the aim, we could change the threshold to get the appropriate model. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "558eaad3-aca5-4dd6-bd57-0aa09a3fa34e",
   "metadata": {},
   "source": [
    "`Assumption:`\n",
    "\n",
    "Let's say we work at a private equity firm that purchases distressed businesses, improve them, and then sells them for a profit. You want to build a model to predict which companies will go bankrupt so that you can purchase them ahead of your competitors. \n",
    "\n",
    "If the firm purchases a company that is indeed insolvent, it can make a profit of €100 million or more. But if it purchases a company that isn't insolvent and can't be resold at a profit, the firm will lose €250 million.\n",
    "\n",
    "`Solution:`\n",
    "\n",
    "For a model like this, we want to focus on ***precision***. If we're trying to maximize our profit, the quality of our predictions is much more important than the quantity of our predictions. \n",
    "\n",
    "It's not a big deal if we don't catch every single insolvent company, but it's **definitely** a big deal if the companies we catch don't end up becoming insolvent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "77ef326b-210c-44f2-ab4e-df65a8f9195e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_cnf_matrix(threshold):\n",
    "    \n",
    "    # Define prediction \n",
    "    y_pred_proba = model_rf.predict_proba(X_test)[:,-1]\n",
    "    y_pred = y_pred_proba > threshold\n",
    "    conf_matrix = confusion_matrix(y_test,y_pred)\n",
    "    \n",
    "    # Calculate the values in confusion matrx \n",
    "    tn,fp,fn,tp = conf_matrix.ravel()\n",
    "\n",
    "    print(f'Profit: ${round(tp * 0.1,2)} billion')\n",
    "    print(f'Loss: ${round(fp * 0.25,2)} billion')\n",
    "    print(f'Diff between Profit and Loss:  ${round((tp*0.1)-(fp*0.25),2)} billion')\n",
    "    \n",
    "    # Visual the confusion matrix\n",
    "    ConfusionMatrixDisplay.from_predictions(y_test,y_pred, colorbar=False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a4c1e04c-5a2f-40e5-b552-d73c2e42ac5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "627a5a7605844113a44685e1e3014eb6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.5, description='threshold', max=1.0, step=0.05), Output()), _dom_cla…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thresh_widget = widgets.FloatSlider(value=0.5,\n",
    "                                    min=0,\n",
    "                                    max=1,\n",
    "                                    step=0.05)\n",
    "\n",
    "interact(make_cnf_matrix, threshold=thresh_widget);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aff8687-8bfb-4eda-8569-caa136c237a0",
   "metadata": {},
   "source": [
    "# Communication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "be025c81-8fa5-4669-ac3e-38df63d02979",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save model \n",
    "with open('clf-model.pkl',mode='wb') as f:\n",
    "    pickle.dump(model_rf,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "322f18e2-55ae-4360-9573-294bd95c2828",
   "metadata": {},
   "outputs": [],
   "source": [
    "from predictor import make_predictions, wrangle\n",
    "file = r'dataset/2year.arff'\n",
    "model_name = 'clf-model.pkl'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "efd13904-5d6b-4272-be3c-05a072d090ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions shape: (10173,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: bankrupt, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate predictions\n",
    "y_test_pred = make_predictions(\n",
    "    data_filepath= file,\n",
    "    model_filepath= model_name,\n",
    ")\n",
    "\n",
    "print(\"predictions shape:\", y_test_pred.shape)\n",
    "y_test_pred.head()"
   ]
  }
 ],
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