MMA_823_Assignment_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Zb6R7CfyFLZf"
   },
   "source": [
    "# MMA 823 Assignment 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tBxpYe8xFTot"
   },
   "source": [
    "## load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uZaGt_QX-8Rg"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.metrics import silhouette_score, silhouette_samples\n",
    "import sklearn.metrics\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n",
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "from yellowbrick.cluster import (\n",
    "    SilhouetteVisualizer,\n",
    "    InterclusterDistance,\n",
    "    KElbowVisualizer,\n",
    ")\n",
    "\n",
    "import itertools\n",
    "\n",
    "import scipy\n",
    "\n",
    "\n",
    "# The following is just to change the display settings.\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "pd.set_option(\"display.max_colwidth\", 200)\n",
    "pd.options.display.max_columns = None\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SoU3paMMFY2g"
   },
   "source": [
    "## Load data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UENz7r8y_cKH"
   },
   "outputs": [],
   "source": [
    "df=pd.read_excel('https://github.com/Wangmiaobeng/MMA_Share/releases/download/MMA823/Bankruptcy_data_Final.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CnMwAfa0NOqD"
   },
   "source": [
    "## Clean data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "p9gE_SlsFnr4"
   },
   "source": [
    "#### check the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 561
    },
    "colab_type": "code",
    "id": "OLySfUYx_gJ4",
    "outputId": "59f89c79-bcb1-489d-be47-142ef0fa6787"
   },
   "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>Data Year - Fiscal</th>\n",
       "      <th>Tobin's Q</th>\n",
       "      <th>EPS</th>\n",
       "      <th>Liquidity</th>\n",
       "      <th>Profitability</th>\n",
       "      <th>Productivity</th>\n",
       "      <th>Leverage Ratio</th>\n",
       "      <th>Asset Turnover</th>\n",
       "      <th>Operational Margin</th>\n",
       "      <th>Return on Equity</th>\n",
       "      <th>Market Book Ratio</th>\n",
       "      <th>Assets Growth</th>\n",
       "      <th>Sales Growth</th>\n",
       "      <th>Employee Growth</th>\n",
       "      <th>BK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1979</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.77</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.126319</td>\n",
       "      <td>0.014278</td>\n",
       "      <td>0.040179</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1981</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.56</td>\n",
       "      <td>0.368077</td>\n",
       "      <td>0.327909</td>\n",
       "      <td>0.566524</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1982</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.05</td>\n",
       "      <td>5.28</td>\n",
       "      <td>-0.020809</td>\n",
       "      <td>-0.118904</td>\n",
       "      <td>-0.095890</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1983</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.04</td>\n",
       "      <td>8.68</td>\n",
       "      <td>0.233089</td>\n",
       "      <td>0.146807</td>\n",
       "      <td>0.053030</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Data Year - Fiscal  Tobin's Q   EPS  Liquidity  Profitability  \\\n",
       "0                1979       0.98  1.58       0.36           0.18   \n",
       "1                1980       0.98  1.41       0.36           0.19   \n",
       "2                1981       0.87  0.31       0.32           0.13   \n",
       "3                1982       1.13  0.71       0.28           0.14   \n",
       "4                1983       1.26  0.75       0.41           0.13   \n",
       "\n",
       "   Productivity  Leverage Ratio  Asset Turnover  Operational Margin  \\\n",
       "0          0.13            1.33            1.77                0.07   \n",
       "1          0.12            1.31            1.59                0.07   \n",
       "2          0.08            1.03            1.55                0.05   \n",
       "3          0.08            0.80            1.39                0.06   \n",
       "4          0.08            0.20            1.30                0.06   \n",
       "\n",
       "   Return on Equity  Market Book Ratio  Assets Growth  Sales Growth  \\\n",
       "0              0.15               2.22            NaN           NaN   \n",
       "1              0.13               2.41       0.126319      0.014278   \n",
       "2              0.04               2.56       0.368077      0.327909   \n",
       "3              0.05               5.28      -0.020809     -0.118904   \n",
       "4              0.04               8.68       0.233089      0.146807   \n",
       "\n",
       "   Employee Growth  BK  \n",
       "0              NaN   0  \n",
       "1         0.040179   0  \n",
       "2         0.566524   0  \n",
       "3        -0.095890   0  \n",
       "4         0.053030   0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 92872 entries, 0 to 92871\n",
      "Data columns (total 15 columns):\n",
      "Data Year - Fiscal    92872 non-null int64\n",
      "Tobin's Q             92620 non-null float64\n",
      "EPS                   92867 non-null float64\n",
      "Liquidity             92625 non-null float64\n",
      "Profitability         92625 non-null float64\n",
      "Productivity          92625 non-null float64\n",
      "Leverage Ratio        92846 non-null float64\n",
      "Asset Turnover        92625 non-null float64\n",
      "Operational Margin    87315 non-null float64\n",
      "Return on Equity      92864 non-null float64\n",
      "Market Book Ratio     92815 non-null float64\n",
      "Assets Growth         86171 non-null float64\n",
      "Sales Growth          86171 non-null float64\n",
      "Employee Growth       85862 non-null float64\n",
      "BK                    92872 non-null int64\n",
      "dtypes: float64(13), int64(2)\n",
      "memory usage: 10.6 MB\n"
     ]
    }
   ],
   "source": [
    "df.head()\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QkK_1pJ4ObRE"
   },
   "source": [
    "#### Set the year to string type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kmSCZMmXK1tG"
   },
   "outputs": [],
   "source": [
    "df['Data Year - Fiscal'] = df['Data Year - Fiscal'].apply(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JJ-ry1gqloRl"
   },
   "source": [
    "#### check the correlation\n",
    "* There is one group of features correlation coefficient large than 0.5.\n",
    "\n",
    "* We will keep these features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 111
    },
    "colab_type": "code",
    "id": "nVZEE_7eO602",
    "outputId": "ae9694eb-9142-45f0-8e87-c5636bf5f73d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Pearson Correlation Coefficient</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Liquidity</th>\n",
       "      <th>Return on Equity</th>\n",
       "      <td>0.582335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Pearson Correlation Coefficient\n",
       "Col1      Col2                                             \n",
       "Liquidity Return on Equity                         0.582335"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr=df.corr()\n",
    "corr_triu = corr.where(~np.tril(np.ones(corr.shape)).astype(np.bool))\n",
    "corr_triu = corr_triu.stack()\n",
    "corr_triu.name = 'Pearson Correlation Coefficient'\n",
    "corr_triu.index.names = ['Col1', 'Col2']\n",
    "corr_triu[(corr_triu > 0.5) | (corr_triu < -0.5) ].to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NbhJ9tH1mcwS"
   },
   "source": [
    "#### Check the missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZT9IAx3pHV9e"
   },
   "outputs": [],
   "source": [
    "percent_missing = df.isnull().sum() * 100 / len(df)\n",
    "missing_value_df = pd.DataFrame({'column_name': df.columns,\n",
    "                                 'percent_missing': percent_missing})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 514
    },
    "colab_type": "code",
    "id": "v32tLIy8IDDI",
    "outputId": "1ea5cbd0-6177-4d18-d9ab-11698be73faf"
   },
   "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>column_name</th>\n",
       "      <th>percent_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Data Year - Fiscal</th>\n",
       "      <td>Data Year - Fiscal</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tobin's Q</th>\n",
       "      <td>Tobin's Q</td>\n",
       "      <td>0.271341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPS</th>\n",
       "      <td>EPS</td>\n",
       "      <td>0.005384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liquidity</th>\n",
       "      <td>Liquidity</td>\n",
       "      <td>0.265957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profitability</th>\n",
       "      <td>Profitability</td>\n",
       "      <td>0.265957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Productivity</th>\n",
       "      <td>Productivity</td>\n",
       "      <td>0.265957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leverage Ratio</th>\n",
       "      <td>Leverage Ratio</td>\n",
       "      <td>0.027996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asset Turnover</th>\n",
       "      <td>Asset Turnover</td>\n",
       "      <td>0.265957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Operational Margin</th>\n",
       "      <td>Operational Margin</td>\n",
       "      <td>5.983504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Return on Equity</th>\n",
       "      <td>Return on Equity</td>\n",
       "      <td>0.008614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Market Book Ratio</th>\n",
       "      <td>Market Book Ratio</td>\n",
       "      <td>0.061375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assets Growth</th>\n",
       "      <td>Assets Growth</td>\n",
       "      <td>7.215307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales Growth</th>\n",
       "      <td>Sales Growth</td>\n",
       "      <td>7.215307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Employee Growth</th>\n",
       "      <td>Employee Growth</td>\n",
       "      <td>7.548023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BK</th>\n",
       "      <td>BK</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           column_name  percent_missing\n",
       "Data Year - Fiscal  Data Year - Fiscal         0.000000\n",
       "Tobin's Q                    Tobin's Q         0.271341\n",
       "EPS                                EPS         0.005384\n",
       "Liquidity                    Liquidity         0.265957\n",
       "Profitability            Profitability         0.265957\n",
       "Productivity              Productivity         0.265957\n",
       "Leverage Ratio          Leverage Ratio         0.027996\n",
       "Asset Turnover          Asset Turnover         0.265957\n",
       "Operational Margin  Operational Margin         5.983504\n",
       "Return on Equity      Return on Equity         0.008614\n",
       "Market Book Ratio    Market Book Ratio         0.061375\n",
       "Assets Growth            Assets Growth         7.215307\n",
       "Sales Growth              Sales Growth         7.215307\n",
       "Employee Growth        Employee Growth         7.548023\n",
       "BK                                  BK         0.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_value_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3YlYY0NGmyHF"
   },
   "source": [
    "##### Check the ratio of target\n",
    "\n",
    "* This is an imbanlanced data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "wrYf0V4MIEhZ",
    "outputId": "f3589d62-2d6d-4a16-f053-21f9f8a4dff7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9939917305538806\n",
      "0.00600826944611939\n"
     ]
    }
   ],
   "source": [
    "BK0_ratio=len(df[df.BK==0])/len(df)\n",
    "BK1_ratio=len(df[df.BK==1])/len(df)\n",
    "print(BK0_ratio)\n",
    "print(BK1_ratio)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zLNmvPwPnXxD"
   },
   "source": [
    "##### Check the ratio of target which has missing value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vJxRNZtAL58J"
   },
   "outputs": [],
   "source": [
    "null_data = df[df.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "au2uS2ukMXf2",
    "outputId": "c1eca923-1a37-4fa1-aada-c8bc652a930b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9927151182721975\n",
      "0.007284881727802537\n"
     ]
    }
   ],
   "source": [
    "print(len(null_data[null_data.BK==0])/len(null_data))\n",
    "print(len(null_data[null_data.BK==1])/len(null_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HNR3wkoGnjn_"
   },
   "source": [
    "##### Check the target ratio relative ratio of each missing feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "G3Oo0YG_Mv46"
   },
   "outputs": [],
   "source": [
    "BK_ratio_dict={}\n",
    "BK_ratio_dict['variable']=[]\n",
    "BK_ratio_dict['total_missing']=[]\n",
    "BK_ratio_dict['BK1_ratio']=[]\n",
    "BK_ratio_dict['BK1_ratio_relative']=[]\n",
    "\n",
    "for i in list(df.columns)[1:-1]:\n",
    "  temp_df=df[df[i].isnull()]\n",
    "  BK_ratio_dict['variable'].append(i)\n",
    "  temp_ratio=len(temp_df[temp_df.BK==1])/len(temp_df)\n",
    "  BK_ratio_dict['BK1_ratio'].append(temp_ratio)\n",
    "  BK_ratio_dict['BK1_ratio_relative'].append( abs((temp_ratio-BK1_ratio)/BK1_ratio))\n",
    "  BK_ratio_dict['total_missing'].append(len(temp_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "colab_type": "code",
    "id": "apwxLiw8QBcJ",
    "outputId": "4809f918-6792-47aa-c38e-4695bcf61d58"
   },
   "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>variable</th>\n",
       "      <th>total_missing</th>\n",
       "      <th>BK1_ratio</th>\n",
       "      <th>BK1_ratio_relative</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EPS</td>\n",
       "      <td>5</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>132.149821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Return on Equity</td>\n",
       "      <td>8</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>82.218638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Market Book Ratio</td>\n",
       "      <td>57</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>25.279570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Leverage Ratio</td>\n",
       "      <td>26</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>5.401434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Liquidity</td>\n",
       "      <td>247</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Profitability</td>\n",
       "      <td>247</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Productivity</td>\n",
       "      <td>247</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Asset Turnover</td>\n",
       "      <td>247</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Assets Growth</td>\n",
       "      <td>6701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Sales Growth</td>\n",
       "      <td>6701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tobin's Q</td>\n",
       "      <td>252</td>\n",
       "      <td>0.011905</td>\n",
       "      <td>0.981396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Employee Growth</td>\n",
       "      <td>7010</td>\n",
       "      <td>0.010271</td>\n",
       "      <td>0.709484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Operational Margin</td>\n",
       "      <td>5557</td>\n",
       "      <td>0.003059</td>\n",
       "      <td>0.490834</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              variable  total_missing  BK1_ratio  BK1_ratio_relative\n",
       "1                  EPS              5   0.800000          132.149821\n",
       "8     Return on Equity              8   0.500000           82.218638\n",
       "9    Market Book Ratio             57   0.157895           25.279570\n",
       "5       Leverage Ratio             26   0.038462            5.401434\n",
       "2            Liquidity            247   0.000000            1.000000\n",
       "3        Profitability            247   0.000000            1.000000\n",
       "4         Productivity            247   0.000000            1.000000\n",
       "6       Asset Turnover            247   0.000000            1.000000\n",
       "10       Assets Growth           6701   0.000000            1.000000\n",
       "11        Sales Growth           6701   0.000000            1.000000\n",
       "0            Tobin's Q            252   0.011905            0.981396\n",
       "12     Employee Growth           7010   0.010271            0.709484\n",
       "7   Operational Margin           5557   0.003059            0.490834"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(BK_ratio_dict).sort_values('BK1_ratio_relative',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BJVk8HJhQVGp"
   },
   "source": [
    "* After checking all the missing values, I decided to impute the missing value with 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EyNANgAYV9eK"
   },
   "source": [
    "##### define a function to create dummy feature to catch the missing value before imputing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GvwhLWihNRLq"
   },
   "outputs": [],
   "source": [
    "def create_surrogate(df_data):\n",
    "  data_frame=df_data.copy()\n",
    "  for i in list(data_frame.columns):\n",
    "    if sum(data_frame[i].isnull())>=1:\n",
    "      data_frame[i+'_surrogate']=np.where(pd.isnull(data_frame[i]),1,0)\n",
    "  return data_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nW9FnEhnWR06"
   },
   "source": [
    "##### apple the function to create the dummy feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8nBgBshsWaOR"
   },
   "outputs": [],
   "source": [
    "df_with_surrogate=create_surrogate(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-PLmllG6Wlep"
   },
   "source": [
    "##### impute with 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aLTqueNe-0lH"
   },
   "outputs": [],
   "source": [
    "df_cleaned=df_with_surrogate.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 819
    },
    "colab_type": "code",
    "id": "pNGoUHcB_fjR",
    "outputId": "da2d6201-d742-4180-dc61-b9d6bf58e396"
   },
   "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>Data Year - Fiscal</th>\n",
       "      <th>Tobin's Q</th>\n",
       "      <th>EPS</th>\n",
       "      <th>Liquidity</th>\n",
       "      <th>Profitability</th>\n",
       "      <th>Productivity</th>\n",
       "      <th>Leverage Ratio</th>\n",
       "      <th>Asset Turnover</th>\n",
       "      <th>Operational Margin</th>\n",
       "      <th>Return on Equity</th>\n",
       "      <th>Market Book Ratio</th>\n",
       "      <th>Assets Growth</th>\n",
       "      <th>Sales Growth</th>\n",
       "      <th>Employee Growth</th>\n",
       "      <th>BK</th>\n",
       "      <th>Tobin's Q_surrogate</th>\n",
       "      <th>EPS_surrogate</th>\n",
       "      <th>Liquidity_surrogate</th>\n",
       "      <th>Profitability_surrogate</th>\n",
       "      <th>Productivity_surrogate</th>\n",
       "      <th>Leverage Ratio_surrogate</th>\n",
       "      <th>Asset Turnover_surrogate</th>\n",
       "      <th>Operational Margin_surrogate</th>\n",
       "      <th>Return on Equity_surrogate</th>\n",
       "      <th>Market Book Ratio_surrogate</th>\n",
       "      <th>Assets Growth_surrogate</th>\n",
       "      <th>Sales Growth_surrogate</th>\n",
       "      <th>Employee Growth_surrogate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1979</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.77</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2.22</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.126319</td>\n",
       "      <td>0.014278</td>\n",
       "      <td>0.040179</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1981</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.56</td>\n",
       "      <td>0.368077</td>\n",
       "      <td>0.327909</td>\n",
       "      <td>0.566524</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1982</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.05</td>\n",
       "      <td>5.28</td>\n",
       "      <td>-0.020809</td>\n",
       "      <td>-0.118904</td>\n",
       "      <td>-0.095890</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1983</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.04</td>\n",
       "      <td>8.68</td>\n",
       "      <td>0.233089</td>\n",
       "      <td>0.146807</td>\n",
       "      <td>0.053030</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Data Year - Fiscal  Tobin's Q   EPS  Liquidity  Profitability  Productivity  \\\n",
       "0               1979       0.98  1.58       0.36           0.18          0.13   \n",
       "1               1980       0.98  1.41       0.36           0.19          0.12   \n",
       "2               1981       0.87  0.31       0.32           0.13          0.08   \n",
       "3               1982       1.13  0.71       0.28           0.14          0.08   \n",
       "4               1983       1.26  0.75       0.41           0.13          0.08   \n",
       "\n",
       "   Leverage Ratio  Asset Turnover  Operational Margin  Return on Equity  \\\n",
       "0            1.33            1.77                0.07              0.15   \n",
       "1            1.31            1.59                0.07              0.13   \n",
       "2            1.03            1.55                0.05              0.04   \n",
       "3            0.80            1.39                0.06              0.05   \n",
       "4            0.20            1.30                0.06              0.04   \n",
       "\n",
       "   Market Book Ratio  Assets Growth  Sales Growth  Employee Growth  BK  \\\n",
       "0               2.22       0.000000      0.000000         0.000000   0   \n",
       "1               2.41       0.126319      0.014278         0.040179   0   \n",
       "2               2.56       0.368077      0.327909         0.566524   0   \n",
       "3               5.28      -0.020809     -0.118904        -0.095890   0   \n",
       "4               8.68       0.233089      0.146807         0.053030   0   \n",
       "\n",
       "   Tobin's Q_surrogate  EPS_surrogate  Liquidity_surrogate  \\\n",
       "0                    0              0                    0   \n",
       "1                    0              0                    0   \n",
       "2                    0              0                    0   \n",
       "3                    0              0                    0   \n",
       "4                    0              0                    0   \n",
       "\n",
       "   Profitability_surrogate  Productivity_surrogate  Leverage Ratio_surrogate  \\\n",
       "0                        0                       0                         0   \n",
       "1                        0                       0                         0   \n",
       "2                        0                       0                         0   \n",
       "3                        0                       0                         0   \n",
       "4                        0                       0                         0   \n",
       "\n",
       "   Asset Turnover_surrogate  Operational Margin_surrogate  \\\n",
       "0                         0                             0   \n",
       "1                         0                             0   \n",
       "2                         0                             0   \n",
       "3                         0                             0   \n",
       "4                         0                             0   \n",
       "\n",
       "   Return on Equity_surrogate  Market Book Ratio_surrogate  \\\n",
       "0                           0                            0   \n",
       "1                           0                            0   \n",
       "2                           0                            0   \n",
       "3                           0                            0   \n",
       "4                           0                            0   \n",
       "\n",
       "   Assets Growth_surrogate  Sales Growth_surrogate  Employee Growth_surrogate  \n",
       "0                        1                       1                          1  \n",
       "1                        0                       0                          0  \n",
       "2                        0                       0                          0  \n",
       "3                        0                       0                          0  \n",
       "4                        0                       0                          0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 92872 entries, 0 to 92871\n",
      "Data columns (total 28 columns):\n",
      "Data Year - Fiscal              92872 non-null object\n",
      "Tobin's Q                       92872 non-null float64\n",
      "EPS                             92872 non-null float64\n",
      "Liquidity                       92872 non-null float64\n",
      "Profitability                   92872 non-null float64\n",
      "Productivity                    92872 non-null float64\n",
      "Leverage Ratio                  92872 non-null float64\n",
      "Asset Turnover                  92872 non-null float64\n",
      "Operational Margin              92872 non-null float64\n",
      "Return on Equity                92872 non-null float64\n",
      "Market Book Ratio               92872 non-null float64\n",
      "Assets Growth                   92872 non-null float64\n",
      "Sales Growth                    92872 non-null float64\n",
      "Employee Growth                 92872 non-null float64\n",
      "BK                              92872 non-null int64\n",
      "Tobin's Q_surrogate             92872 non-null int64\n",
      "EPS_surrogate                   92872 non-null int64\n",
      "Liquidity_surrogate             92872 non-null int64\n",
      "Profitability_surrogate         92872 non-null int64\n",
      "Productivity_surrogate          92872 non-null int64\n",
      "Leverage Ratio_surrogate        92872 non-null int64\n",
      "Asset Turnover_surrogate        92872 non-null int64\n",
      "Operational Margin_surrogate    92872 non-null int64\n",
      "Return on Equity_surrogate      92872 non-null int64\n",
      "Market Book Ratio_surrogate     92872 non-null int64\n",
      "Assets Growth_surrogate         92872 non-null int64\n",
      "Sales Growth_surrogate          92872 non-null int64\n",
      "Employee Growth_surrogate       92872 non-null int64\n",
      "dtypes: float64(13), int64(14), object(1)\n",
      "memory usage: 19.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_cleaned.head()\n",
    "df_cleaned.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 476
    },
    "colab_type": "code",
    "id": "gopPF6o6W72k",
    "outputId": "70e57187-bba1-40da-c740-5dc30458eb44"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Data Year - Fiscal',\n",
       " \"Tobin's Q\",\n",
       " 'EPS',\n",
       " 'Liquidity',\n",
       " 'Profitability',\n",
       " 'Productivity',\n",
       " 'Leverage Ratio',\n",
       " 'Asset Turnover',\n",
       " 'Operational Margin',\n",
       " 'Return on Equity',\n",
       " 'Market Book Ratio',\n",
       " 'Assets Growth',\n",
       " 'Sales Growth',\n",
       " 'Employee Growth',\n",
       " \"Tobin's Q_surrogate\",\n",
       " 'EPS_surrogate',\n",
       " 'Liquidity_surrogate',\n",
       " 'Profitability_surrogate',\n",
       " 'Productivity_surrogate',\n",
       " 'Leverage Ratio_surrogate',\n",
       " 'Asset Turnover_surrogate',\n",
       " 'Operational Margin_surrogate',\n",
       " 'Return on Equity_surrogate',\n",
       " 'Market Book Ratio_surrogate',\n",
       " 'Assets Growth_surrogate',\n",
       " 'Sales Growth_surrogate',\n",
       " 'Employee Growth_surrogate']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=list( df_cleaned.columns)\n",
    "x.remove('BK')\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "v9qf5SKTAwS-"
   },
   "source": [
    "## Scale data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 462
    },
    "colab_type": "code",
    "id": "caJss7YtA0ki",
    "outputId": "57b3cdd2-5ffa-4ba5-bdb6-37ceb02ea0ec"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n",
      "//anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:543: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "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>Data Year - Fiscal</th>\n",
       "      <th>Tobin's Q</th>\n",
       "      <th>EPS</th>\n",
       "      <th>Liquidity</th>\n",
       "      <th>Profitability</th>\n",
       "      <th>Productivity</th>\n",
       "      <th>Leverage Ratio</th>\n",
       "      <th>Asset Turnover</th>\n",
       "      <th>Operational Margin</th>\n",
       "      <th>Return on Equity</th>\n",
       "      <th>Market Book Ratio</th>\n",
       "      <th>Assets Growth</th>\n",
       "      <th>Sales Growth</th>\n",
       "      <th>Employee Growth</th>\n",
       "      <th>Tobin's Q_surrogate</th>\n",
       "      <th>EPS_surrogate</th>\n",
       "      <th>Liquidity_surrogate</th>\n",
       "      <th>Profitability_surrogate</th>\n",
       "      <th>Productivity_surrogate</th>\n",
       "      <th>Leverage Ratio_surrogate</th>\n",
       "      <th>Asset Turnover_surrogate</th>\n",
       "      <th>Operational Margin_surrogate</th>\n",
       "      <th>Return on Equity_surrogate</th>\n",
       "      <th>Market Book Ratio_surrogate</th>\n",
       "      <th>Assets Growth_surrogate</th>\n",
       "      <th>Sales Growth_surrogate</th>\n",
       "      <th>Employee Growth_surrogate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1979</td>\n",
       "      <td>-0.026975</td>\n",
       "      <td>0.007306</td>\n",
       "      <td>0.024572</td>\n",
       "      <td>0.043823</td>\n",
       "      <td>0.037659</td>\n",
       "      <td>-0.000059</td>\n",
       "      <td>0.340233</td>\n",
       "      <td>0.036123</td>\n",
       "      <td>0.006416</td>\n",
       "      <td>-0.013666</td>\n",
       "      <td>-0.016897</td>\n",
       "      <td>-0.010304</td>\n",
       "      <td>-0.023455</td>\n",
       "      <td>-0.052161</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980</td>\n",
       "      <td>-0.026975</td>\n",
       "      <td>0.007229</td>\n",
       "      <td>0.024572</td>\n",
       "      <td>0.043838</td>\n",
       "      <td>0.037380</td>\n",
       "      <td>-0.000138</td>\n",
       "      <td>0.255079</td>\n",
       "      <td>0.036123</td>\n",
       "      <td>0.006359</td>\n",
       "      <td>-0.013658</td>\n",
       "      <td>-0.015120</td>\n",
       "      <td>-0.010220</td>\n",
       "      <td>-0.020486</td>\n",
       "      <td>-0.052161</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1981</td>\n",
       "      <td>-0.027097</td>\n",
       "      <td>0.006728</td>\n",
       "      <td>0.024243</td>\n",
       "      <td>0.043749</td>\n",
       "      <td>0.036264</td>\n",
       "      <td>-0.001245</td>\n",
       "      <td>0.236155</td>\n",
       "      <td>0.036027</td>\n",
       "      <td>0.006104</td>\n",
       "      <td>-0.013653</td>\n",
       "      <td>-0.011717</td>\n",
       "      <td>-0.008387</td>\n",
       "      <td>0.018408</td>\n",
       "      <td>-0.052161</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1982</td>\n",
       "      <td>-0.026808</td>\n",
       "      <td>0.006910</td>\n",
       "      <td>0.023913</td>\n",
       "      <td>0.043764</td>\n",
       "      <td>0.036264</td>\n",
       "      <td>-0.002154</td>\n",
       "      <td>0.160463</td>\n",
       "      <td>0.036075</td>\n",
       "      <td>0.006133</td>\n",
       "      <td>-0.013548</td>\n",
       "      <td>-0.017190</td>\n",
       "      <td>-0.010999</td>\n",
       "      <td>-0.030540</td>\n",
       "      <td>-0.052161</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1983</td>\n",
       "      <td>-0.026664</td>\n",
       "      <td>0.006928</td>\n",
       "      <td>0.024984</td>\n",
       "      <td>0.043749</td>\n",
       "      <td>0.036264</td>\n",
       "      <td>-0.004525</td>\n",
       "      <td>0.117886</td>\n",
       "      <td>0.036075</td>\n",
       "      <td>0.006104</td>\n",
       "      <td>-0.013418</td>\n",
       "      <td>-0.013617</td>\n",
       "      <td>-0.009446</td>\n",
       "      <td>-0.019536</td>\n",
       "      <td>-0.052161</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Data Year - Fiscal  Tobin's Q       EPS  Liquidity  Profitability  \\\n",
       "0               1979  -0.026975  0.007306   0.024572       0.043823   \n",
       "1               1980  -0.026975  0.007229   0.024572       0.043838   \n",
       "2               1981  -0.027097  0.006728   0.024243       0.043749   \n",
       "3               1982  -0.026808  0.006910   0.023913       0.043764   \n",
       "4               1983  -0.026664  0.006928   0.024984       0.043749   \n",
       "\n",
       "   Productivity  Leverage Ratio  Asset Turnover  Operational Margin  \\\n",
       "0      0.037659       -0.000059        0.340233            0.036123   \n",
       "1      0.037380       -0.000138        0.255079            0.036123   \n",
       "2      0.036264       -0.001245        0.236155            0.036027   \n",
       "3      0.036264       -0.002154        0.160463            0.036075   \n",
       "4      0.036264       -0.004525        0.117886            0.036075   \n",
       "\n",
       "   Return on Equity  Market Book Ratio  Assets Growth  Sales Growth  \\\n",
       "0          0.006416          -0.013666      -0.016897     -0.010304   \n",
       "1          0.006359          -0.013658      -0.015120     -0.010220   \n",
       "2          0.006104          -0.013653      -0.011717     -0.008387   \n",
       "3          0.006133          -0.013548      -0.017190     -0.010999   \n",
       "4          0.006104          -0.013418      -0.013617     -0.009446   \n",
       "\n",
       "   Employee Growth  Tobin's Q_surrogate  EPS_surrogate  Liquidity_surrogate  \\\n",
       "0        -0.023455            -0.052161              0                    0   \n",
       "1        -0.020486            -0.052161              0                    0   \n",
       "2         0.018408            -0.052161              0                    0   \n",
       "3        -0.030540            -0.052161              0                    0   \n",
       "4        -0.019536            -0.052161              0                    0   \n",
       "\n",
       "   Profitability_surrogate  Productivity_surrogate  Leverage Ratio_surrogate  \\\n",
       "0                        0                       0                         0   \n",
       "1                        0                       0                         0   \n",
       "2                        0                       0                         0   \n",
       "3                        0                       0                         0   \n",
       "4                        0                       0                         0   \n",
       "\n",
       "   Asset Turnover_surrogate  Operational Margin_surrogate  \\\n",
       "0                         0                             0   \n",
       "1                         0                             0   \n",
       "2                         0                             0   \n",
       "3                         0                             0   \n",
       "4                         0                             0   \n",
       "\n",
       "   Return on Equity_surrogate  Market Book Ratio_surrogate  \\\n",
       "0                           0                            0   \n",
       "1                           0                            0   \n",
       "2                           0                            0   \n",
       "3                           0                            0   \n",
       "4                           0                            0   \n",
       "\n",
       "   Assets Growth_surrogate  Sales Growth_surrogate  Employee Growth_surrogate  \n",
       "0                        1                       1                          1  \n",
       "1                        0                       0                          0  \n",
       "2                        0                       0                          0  \n",
       "3                        0                       0                          0  \n",
       "4                        0                       0                          0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features =list( df_cleaned.columns)\n",
    "features.remove('BK')\n",
    "features_to_be_scaled=features[1:15]\n",
    "\n",
    "X=df_cleaned[features]\n",
    "y=df_cleaned['BK']\n",
    "X[features_to_be_scaled] = StandardScaler().fit_transform(X[features_to_be_scaled])\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "P1MHbwkf_8JH"
   },
   "source": [
    "## Seperate data into train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "JHursSmTABvS",
    "outputId": "bbb22fd6-bbd7-4075-c578-58e7eb7356c2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00600826944611939"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(y)/len(y) # check the ratio of True in target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "46Y7CPRdVr0c",
    "outputId": "a563809a-78ad-4201-8a83-0c50b6e82f50"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "558"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "bIlfjPGrBwKg",
    "outputId": "c849ab86-7680-4aa9-a03f-b32018ddd9d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data Year - Fiscal    39\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.iloc[:,0:1].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eiFU8VUkDVmE"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train_UN, X_test, y_train_UN, y_test = train_test_split(X, y, test_size=0.2, random_state=1028, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lK1dV0mEVgjz"
   },
   "source": [
    "## Balance data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hP1tbfXzVo9k"
   },
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Aq-cZ1TyV3X0",
    "outputId": "0b7c2725-675b-4da9-805b-e9099a93cbe9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " sm = SMOTE(random_state=42)\n",
    "X_train, y_train = sm.fit_resample(X_train_UN, y_train_UN)\n",
    "sum(y_train)/len(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yl2hOeayVcrO"
   },
   "source": [
    "## Build models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7Ge3X0GbFslk"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, cohen_kappa_score, f1_score, log_loss,roc_auc_score,precision_score,recall_score\n",
    "def score_list(predicion,y_test=y_test):\n",
    "  '''this function will print Accurarcy, Kappa, F1 score, Log Loss and AUC'''\n",
    "  print('\\n ****************************')\n",
    "  print(\"Accuracy = {:.2f}\".format(accuracy_score(y_test, predicion)))\n",
    "  print(\"F1 Score = {:.2f}\".format(f1_score(y_test, predicion)))\n",
    "  print(\"AUC = {:.2f}\".format(roc_auc_score(y_test, predicion)))\n",
    "  print(\"Precision = {:.2f}\".format(precision_score(y_test, predicion)))\n",
    "  print(\"Recall = {:.2f}\".format(recall_score(y_test, predicion)))\n",
    "  print('**************************** \\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y61doIhZF0E1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=DecisionTreeClassifier(class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features=None,\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              presort=True, random_state=42,\n",
       "                                              splitter='best'),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'criterion': ('gini', 'entropy'),\n",
       "                         'max_depth': [2, 4, 6, 8, 10],\n",
       "                         'max_features': [None, 'auto'],\n",
       "                         'max_leaf_nodes': [None, 5, 10, 50],\n",
       "                         'min_impurity_decrease': [0, 0.1, 0.2],\n",
       "                         'min_samples_leaf': [1, 5, 10],\n",
       "                         'min_samples_split': [2, 10, 50]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ****************************\n",
      "Accuracy = 0.93\n",
      "F1 Score = 0.11\n",
      "AUC = 0.84\n",
      "Precision = 0.06\n",
      "Recall = 0.75\n",
      "**************************** \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# build model using grid search with 5Fold Cross validation to train and test\n",
    "treeclf = DecisionTreeClassifier(splitter='best', presort=True, class_weight=None, random_state=42)\n",
    "parameters = {'criterion':('gini', 'entropy'), 'max_depth':[2, 4, 6, 8, 10], 'min_samples_split':[2, 10, 50], 'min_samples_leaf':[1, 5, 10],\n",
    "             'max_features':[None, 'auto'], 'max_leaf_nodes':[None, 5, 10, 50], 'min_impurity_decrease':[0, 0.1, 0.2]}\n",
    "cv_clf = GridSearchCV(treeclf, parameters, scoring='roc_auc', cv=5, return_train_score=True)\n",
    "cv_clf.fit(X_train, y_train)\n",
    "\n",
    "# predict on test data\n",
    "y_pred_dt_cv = cv_clf.predict(X_test)\n",
    "\n",
    "# print the score with the function defined myself\n",
    "score_list(y_pred_dt_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "# Adopted from: https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html\n",
    "\n",
    "\n",
    "def plot_boundaries(X_train, X_test, y_train, y_test, clf, clf_name, ax, hide_ticks=True):\n",
    "    cm = plt.cm.RdBu\n",
    "    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
    "\n",
    "    X = np.concatenate((X_train, X_test), axis=0)\n",
    "    y = np.concatenate((y_train, y_test), axis=0)\n",
    "    \n",
    "    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02));\n",
    "    \n",
    "    \n",
    "    score = clf.score(X_test, y_test);\n",
    "\n",
    "    # Plot the decision boundary. For that, we will assign a color to each\n",
    "    # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "    if hasattr(clf, \"decision_function\"):\n",
    "        Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]);\n",
    "    else:\n",
    "        Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1];\n",
    "\n",
    "    # Put the result into a color plot\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    ax.contourf(xx, yy, Z, cmap=cm, alpha=.8);\n",
    "\n",
    "    # Plot the training points\n",
    "    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, s=100, cmap=cm_bright, edgecolors='k');\n",
    "    # Plot the testing points\n",
    "    ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, s=50, cmap=cm_bright, edgecolors='k', alpha=0.6);\n",
    "\n",
    "    ax.set_xlim(xx.min(), xx.max());\n",
    "    ax.set_ylim(yy.min(), yy.max());\n",
    "    if hide_ticks:\n",
    "        ax.set_xticks(());\n",
    "        ax.set_yticks(());\n",
    "    else:\n",
    "        ax.tick_params(axis='both', which='major', labelsize=18)\n",
    "        #ax.yticks(fontsize=18);\n",
    "        \n",
    "    ax.set_title(clf_name, fontsize=28);\n",
    "    ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), size=35, horizontalalignment='right');\n",
    "    ax.grid();\n",
    "    \n",
    "    \n",
    "\n",
    "\n",
    "def plot_roc(clf, X_test, y_test, name, ax, show_thresholds=True):\n",
    "    y_pred_rf = clf.predict_proba(X_test)[:, 1]\n",
    "    fpr, tpr, thr = roc_curve(y_test, y_pred_rf)\n",
    "\n",
    "    ax.plot([0, 1], [0, 1], 'k--');\n",
    "    ax.plot(fpr, tpr, label='{}, AUC={:.2f}'.format(name, auc(fpr, tpr)));\n",
    "    ax.scatter(fpr, tpr);\n",
    "\n",
    "    if show_thresholds:\n",
    "        for i, th in enumerate(thr):\n",
    "            ax.text(x=fpr[i], y=tpr[i], s=\"{:.2f}\".format(th), fontsize=14, \n",
    "                     horizontalalignment='left', verticalalignment='top', color='black',\n",
    "                     bbox=dict(facecolor='white', edgecolor='black', boxstyle='round,pad=0.1', alpha=0.1));\n",
    "        \n",
    "    ax.set_xlabel('False positive rate', fontsize=18);\n",
    "    ax.set_ylabel('True positive rate', fontsize=18);\n",
    "    ax.tick_params(axis='both', which='major', labelsize=18);\n",
    "    ax.grid(True);\n",
    "    ax.set_title('ROC Curve', fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('default');\n",
    "figure = plt.figure(figsize=(12, 6));    \n",
    "ax = plt.subplot(1, 1, 1);\n",
    "plot_roc(cv_clf, X_test, y_test, \"Decision Tree\", ax)\n",
    "plt.legend(loc='lower right', fontsize=8);\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3thJHbBcF3Dp"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=GaussianNB(priors=None, var_smoothing=1e-09), iid='warn',\n",
       "             n_jobs=None,\n",
       "             param_grid={'var_smoothing': [1e-07, 1e-08, 1e-09, 1e-10, 1e-11]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ****************************\n",
      "Accuracy = 0.15\n",
      "F1 Score = 0.01\n",
      "AUC = 0.53\n",
      "Precision = 0.01\n",
      "Recall = 0.91\n",
      "**************************** \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "# build model using grid search with 5Fold Cross validation to train and test\n",
    "gnb = GaussianNB()\n",
    "parameters = {\"var_smoothing\":[1e-7,1e-8,1e-9,1e-10,1e-11]}\n",
    "cv_gnb = GridSearchCV(gnb, parameters, scoring='roc_auc', cv=5, return_train_score=True)\n",
    "cv_gnb.fit(X_train, y_train)\n",
    "\n",
    "# predict on test data\n",
    "y_pred_gnb_cv = cv_gnb.predict(X_test)\n",
    "\n",
    "# print the score with the function defined myself\n",
    "score_list(y_pred_gnb_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('default');\n",
    "figure = plt.figure(figsize=(10, 6));    \n",
    "ax = plt.subplot(1, 1, 1);\n",
    "plot_roc(cv_gnb, X_test, y_test, \"GNB\", ax)\n",
    "plt.legend(loc='lower right', fontsize=18);\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uhMz9MvZHFPF"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,\n",
       "                                            metric='minkowski',\n",
       "                                            metric_params=None, n_jobs=None,\n",
       "                                            n_neighbors=5, p=2,\n",
       "                                            weights='uniform'),\n",
       "             iid='warn', n_jobs=None, param_grid={'n_neighbors': range(2, 11)},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ****************************\n",
      "Accuracy = 0.94\n",
      "F1 Score = 0.08\n",
      "AUC = 0.69\n",
      "Precision = 0.04\n",
      "Recall = 0.44\n",
      "**************************** \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "\n",
    "# build model using grid search with 5Fold Cross validation to train and test\n",
    "knn_clf = KNeighborsClassifier()\n",
    "parameters = {\"n_neighbors\":range(2,11)}\n",
    "cv_knn_clf = GridSearchCV(knn_clf, parameters, scoring='roc_auc', cv=5, return_train_score=True)\n",
    "cv_knn_clf.fit(X_train, y_train)\n",
    "\n",
    "# predict on test data\n",
    "y_pred_knn_clf_cv = cv_knn_clf.predict(X_test)\n",
    "\n",
    "# print the score with the function defined myself\n",
    "score_list(y_pred_knn_clf_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('default');\n",
    "figure = plt.figure(figsize=(10, 6));    \n",
    "ax = plt.subplot(1, 1, 1);\n",
    "plot_roc(cv_knn_clf, X_test, y_test, \"GNB\", ax)\n",
    "plt.legend(loc='lower right', fontsize=18);\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nerual Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bVfVh-gpHI7n"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=MLPClassifier(activation='relu', alpha=0.0001,\n",
       "                                     batch_size='auto', beta_1=0.9,\n",
       "                                     beta_2=0.999, early_stopping=False,\n",
       "                                     epsilon=1e-08, hidden_layer_sizes=(100,),\n",
       "                                     learning_rate='constant',\n",
       "                                     learning_rate_init=0.001, max_iter=200,\n",
       "                                     momentum=0.9, n_iter_no_change=10,\n",
       "                                     nesterovs_momentum=True, power_t=0.5,\n",
       "                                     random_state=1, shuffle=True,\n",
       "                                     solver='lbfgs', tol=0.0001,\n",
       "                                     validation_fraction=0.1, verbose=False,\n",
       "                                     warm_start=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'activation': ['logistic', 'tanh', 'relu'],\n",
       "                         'alpha': [0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3],\n",
       "                         'hidden_layer_sizes': range(2, 11)},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ****************************\n",
      "Accuracy = 0.99\n",
      "F1 Score = 0.00\n",
      "AUC = 0.50\n",
      "Precision = 0.00\n",
      "Recall = 0.00\n",
      "**************************** \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "\n",
    "# build model using grid search with 5Fold Cross validation to train and test\n",
    "nn_clf = MLPClassifier(solver='lbfgs',random_state=1, verbose=False)\n",
    "parameters = {'activation':['logistic','tanh','relu'],'alpha':[0.001,0.003,0.01,0.03,0.1,0.3,1,3],'hidden_layer_sizes':range(2,11)}\n",
    "cv_nn_clf = GridSearchCV(nn_clf, parameters, scoring='roc_auc', cv=5, return_train_score=False)\n",
    "cv_nn_clf.fit(X_train, y_train)\n",
    "\n",
    "# predict on test data\n",
    "y_pred_nn_clf_cv = cv_nn_clf.predict(X_test)\n",
    "\n",
    "# print the score with the function defined myself\n",
    "score_list(y_pred_nn_clf_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "A0TqxzuxHLbj"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=RandomForestClassifier(bootstrap=True, class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators='warn', n_jobs=None,\n",
       "                                              oob_score=False,\n",
       "                                              random_state=1120, verbose=0,\n",
       "                                              warm_start=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'max_depth': [2, 5, 8, 11],\n",
       "                         'n_estimators': [50, 100, 200, 300]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ****************************\n",
      "Accuracy = 0.95\n",
      "F1 Score = 0.15\n",
      "AUC = 0.83\n",
      "Precision = 0.08\n",
      "Recall = 0.71\n",
      "**************************** \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "\n",
    "# build model using grid search with 5Fold Cross validation to train and test\n",
    "rf_clf = RandomForestClassifier(random_state=1120)\n",
    "parameters = {\"n_estimators\":[50,100,200,300],'max_depth':[2,5,8,11]}\n",
    "cv_rf_clf = GridSearchCV(rf_clf, parameters, scoring='roc_auc', cv=5, return_train_score=False)\n",
    "cv_rf_clf.fit(X_train, y_train)\n",
    "\n",
    "# predict on test data\n",
    "y_pred_rf_clf_cv = cv_rf_clf.predict(X_test)\n",
    "\n",
    "# print the score with the function defined myself\n",
    "score_list(y_pred_rf_clf_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uKXEkR2z2AEZ"
   },
   "source": [
    "## Model result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7Mv1rLNCHNRA"
   },
   "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>accuracy_score</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>roc_auc_score</th>\n",
       "      <th>precision_score</th>\n",
       "      <th>recall_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision Trees</th>\n",
       "      <td>0.927</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random forest</th>\n",
       "      <td>0.950</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k-nearest neighbors</th>\n",
       "      <td>0.939</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naive Bayes</th>\n",
       "      <td>0.149</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neural network</th>\n",
       "      <td>0.994</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     accuracy_score  f1_score  roc_auc_score  precision_score  \\\n",
       "Decision Trees                0.927     0.110          0.839            0.059   \n",
       "random forest                 0.950     0.147          0.833            0.082   \n",
       "k-nearest neighbors           0.939     0.080          0.690            0.044   \n",
       "Naive Bayes                   0.149     0.013          0.528            0.006   \n",
       "neural network                0.994     0.000          0.500            0.000   \n",
       "\n",
       "                     recall_score  \n",
       "Decision Trees              0.750  \n",
       "random forest               0.714  \n",
       "k-nearest neighbors         0.438  \n",
       "Naive Bayes                 0.911  \n",
       "neural network              0.000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_score={}\n",
    "for i in [accuracy_score, f1_score,roc_auc_score,precision_score,recall_score]:\n",
    "  dict_score[i.__name__]=[]\n",
    "  for k in [y_pred_dt_cv,y_pred_gnb_cv,y_pred_knn_clf_cv,y_pred_nn_clf_cv,y_pred_rf_clf_cv]:\n",
    "    dict_score[i.__name__].append(round(i(y_test,k),3))\n",
    "\n",
    "df8_6=pd.DataFrame(dict_score,index=['Decision Trees','Naive Bayes','k-nearest neighbors','neural network','random forest'])\n",
    "df8_6.sort_values('roc_auc_score',ascending=False)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "TPU",
  "colab": {
   "collapsed_sections": [
    "tBxpYe8xFTot",
    "SoU3paMMFY2g"
   ],
   "name": "MMA 823 Assignment 2.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}