Bankrupt-Prevention.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading important libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Bankrupt Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>industrial_risk</th>\n",
       "      <th>management_risk</th>\n",
       "      <th>financial_flexibility</th>\n",
       "      <th>credibility</th>\n",
       "      <th>competitiveness</th>\n",
       "      <th>operating_risk</th>\n",
       "      <th>class</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>bankruptcy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>bankruptcy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>bankruptcy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>bankruptcy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>bankruptcy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     industrial_risk   management_risk   financial_flexibility   credibility  \\\n",
       "0                0.5               1.0                     0.0           0.0   \n",
       "1                0.0               1.0                     0.0           0.0   \n",
       "2                1.0               0.0                     0.0           0.0   \n",
       "3                0.5               0.0                     0.0           0.5   \n",
       "4                1.0               1.0                     0.0           0.0   \n",
       "..               ...               ...                     ...           ...   \n",
       "245              0.0               1.0                     1.0           1.0   \n",
       "246              1.0               1.0                     0.5           1.0   \n",
       "247              0.0               1.0                     1.0           0.5   \n",
       "248              1.0               0.0                     0.5           1.0   \n",
       "249              1.0               0.0                     0.5           0.5   \n",
       "\n",
       "      competitiveness   operating_risk           class  \n",
       "0                 0.0              0.5      bankruptcy  \n",
       "1                 0.0              1.0      bankruptcy  \n",
       "2                 0.0              1.0      bankruptcy  \n",
       "3                 0.0              1.0      bankruptcy  \n",
       "4                 0.0              1.0      bankruptcy  \n",
       "..                ...              ...             ...  \n",
       "245               1.0              1.0  non-bankruptcy  \n",
       "246               1.0              0.0  non-bankruptcy  \n",
       "247               0.5              0.0  non-bankruptcy  \n",
       "248               0.5              0.0  non-bankruptcy  \n",
       "249               1.0              1.0  non-bankruptcy  \n",
       "\n",
       "[250 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankrupt = pd.read_csv(\"/media/gargi/Data/DataSets/bankruptcy-prevention.csv\", sep = ';', header = 0)\n",
    "bankrupt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>industrial_risk</th>\n",
       "      <th>management_risk</th>\n",
       "      <th>financial_flexibility</th>\n",
       "      <th>credibility</th>\n",
       "      <th>competitiveness</th>\n",
       "      <th>operating_risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>250.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.518000</td>\n",
       "      <td>0.614000</td>\n",
       "      <td>0.376000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.476000</td>\n",
       "      <td>0.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.411526</td>\n",
       "      <td>0.410705</td>\n",
       "      <td>0.401583</td>\n",
       "      <td>0.415682</td>\n",
       "      <td>0.440682</td>\n",
       "      <td>0.434575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       industrial_risk   management_risk   financial_flexibility  \\\n",
       "count       250.000000        250.000000              250.000000   \n",
       "mean          0.518000          0.614000                0.376000   \n",
       "std           0.411526          0.410705                0.401583   \n",
       "min           0.000000          0.000000                0.000000   \n",
       "25%           0.000000          0.500000                0.000000   \n",
       "50%           0.500000          0.500000                0.500000   \n",
       "75%           1.000000          1.000000                0.500000   \n",
       "max           1.000000          1.000000                1.000000   \n",
       "\n",
       "        credibility   competitiveness   operating_risk  \n",
       "count    250.000000        250.000000       250.000000  \n",
       "mean       0.470000          0.476000         0.570000  \n",
       "std        0.415682          0.440682         0.434575  \n",
       "min        0.000000          0.000000         0.000000  \n",
       "25%        0.000000          0.000000         0.000000  \n",
       "50%        0.500000          0.500000         0.500000  \n",
       "75%        1.000000          1.000000         1.000000  \n",
       "max        1.000000          1.000000         1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankrupt.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 250 entries, 0 to 249\n",
      "Data columns (total 7 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   industrial_risk         250 non-null    float64\n",
      " 1    management_risk        250 non-null    float64\n",
      " 2    financial_flexibility  250 non-null    float64\n",
      " 3    credibility            250 non-null    float64\n",
      " 4    competitiveness        250 non-null    float64\n",
      " 5    operating_risk         250 non-null    float64\n",
      " 6    class                  250 non-null    object \n",
      "dtypes: float64(6), object(1)\n",
      "memory usage: 13.8+ KB\n",
      "None\n",
      "(250, 7)\n"
     ]
    }
   ],
   "source": [
    "print(bankrupt.info())\n",
    "print(bankrupt.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## checking is there any missing values are there in data or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "industrial_risk           0\n",
       " management_risk          0\n",
       " financial_flexibility    0\n",
       " credibility              0\n",
       " competitiveness          0\n",
       " operating_risk           0\n",
       " class                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankrupt.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>financial_flexibility</th>\n",
       "      <th>credibility</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
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       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
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       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
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       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     industrial_risk   management_risk   financial_flexibility   credibility  \\\n",
       "0                0.5               1.0                     0.0           0.0   \n",
       "1                0.0               1.0                     0.0           0.0   \n",
       "2                1.0               0.0                     0.0           0.0   \n",
       "3                0.5               0.0                     0.0           0.5   \n",
       "4                1.0               1.0                     0.0           0.0   \n",
       "..               ...               ...                     ...           ...   \n",
       "245              0.0               1.0                     1.0           1.0   \n",
       "246              1.0               1.0                     0.5           1.0   \n",
       "247              0.0               1.0                     1.0           0.5   \n",
       "248              1.0               0.0                     0.5           1.0   \n",
       "249              1.0               0.0                     0.5           0.5   \n",
       "\n",
       "      competitiveness   operating_risk           class  \n",
       "0                 0.0              0.5      bankruptcy  \n",
       "1                 0.0              1.0      bankruptcy  \n",
       "2                 0.0              1.0      bankruptcy  \n",
       "3                 0.0              1.0      bankruptcy  \n",
       "4                 0.0              1.0      bankruptcy  \n",
       "..                ...              ...             ...  \n",
       "245               1.0              1.0  non-bankruptcy  \n",
       "246               1.0              0.0  non-bankruptcy  \n",
       "247               0.5              0.0  non-bankruptcy  \n",
       "248               0.5              0.0  non-bankruptcy  \n",
       "249               1.0              1.0  non-bankruptcy  \n",
       "\n",
       "[250 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankrupt_new = bankrupt.iloc[:,:]\n",
    "bankrupt_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>industrial_risk</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>bankruptcy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>245</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     industrial_risk   management_risk   financial_flexibility   credibility  \\\n",
       "0                0.5               1.0                     0.0           0.0   \n",
       "1                0.0               1.0                     0.0           0.0   \n",
       "2                1.0               0.0                     0.0           0.0   \n",
       "3                0.5               0.0                     0.0           0.5   \n",
       "4                1.0               1.0                     0.0           0.0   \n",
       "..               ...               ...                     ...           ...   \n",
       "245              0.0               1.0                     1.0           1.0   \n",
       "246              1.0               1.0                     0.5           1.0   \n",
       "247              0.0               1.0                     1.0           0.5   \n",
       "248              1.0               0.0                     0.5           1.0   \n",
       "249              1.0               0.0                     0.5           0.5   \n",
       "\n",
       "      competitiveness   operating_risk           class  class_yn  \n",
       "0                 0.0              0.5      bankruptcy         1  \n",
       "1                 0.0              1.0      bankruptcy         1  \n",
       "2                 0.0              1.0      bankruptcy         1  \n",
       "3                 0.0              1.0      bankruptcy         1  \n",
       "4                 0.0              1.0      bankruptcy         1  \n",
       "..                ...              ...             ...       ...  \n",
       "245               1.0              1.0  non-bankruptcy         1  \n",
       "246               1.0              0.0  non-bankruptcy         1  \n",
       "247               0.5              0.0  non-bankruptcy         1  \n",
       "248               0.5              0.0  non-bankruptcy         1  \n",
       "249               1.0              1.0  non-bankruptcy         1  \n",
       "\n",
       "[250 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankrupt_new[\"class_yn\"] = 1\n",
    "bankrupt_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Here we are changing  the target variable to bankruptcy = 0, non-bankruptcy = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "bankrupt_new.loc[bankrupt[' class'] == 'bankruptcy', 'class_yn'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>...</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>non-bankruptcy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     industrial_risk   management_risk   financial_flexibility   credibility  \\\n",
       "0                0.5               1.0                     0.0           0.0   \n",
       "1                0.0               1.0                     0.0           0.0   \n",
       "2                1.0               0.0                     0.0           0.0   \n",
       "3                0.5               0.0                     0.0           0.5   \n",
       "4                1.0               1.0                     0.0           0.0   \n",
       "..               ...               ...                     ...           ...   \n",
       "245              0.0               1.0                     1.0           1.0   \n",
       "246              1.0               1.0                     0.5           1.0   \n",
       "247              0.0               1.0                     1.0           0.5   \n",
       "248              1.0               0.0                     0.5           1.0   \n",
       "249              1.0               0.0                     0.5           0.5   \n",
       "\n",
       "      competitiveness   operating_risk           class  class_yn  \n",
       "0                 0.0              0.5      bankruptcy         0  \n",
       "1                 0.0              1.0      bankruptcy         0  \n",
       "2                 0.0              1.0      bankruptcy         0  \n",
       "3                 0.0              1.0      bankruptcy         0  \n",
       "4                 0.0              1.0      bankruptcy         0  \n",
       "..                ...              ...             ...       ...  \n",
       "245               1.0              1.0  non-bankruptcy         1  \n",
       "246               1.0              0.0  non-bankruptcy         1  \n",
       "247               0.5              0.0  non-bankruptcy         1  \n",
       "248               0.5              0.0  non-bankruptcy         1  \n",
       "249               1.0              1.0  non-bankruptcy         1  \n",
       "\n",
       "[250 rows x 8 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankrupt_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   industrial_risk   management_risk   financial_flexibility   credibility  \\\n",
       "0              0.5               1.0                     0.0           0.0   \n",
       "1              0.0               1.0                     0.0           0.0   \n",
       "2              1.0               0.0                     0.0           0.0   \n",
       "3              0.5               0.0                     0.0           0.5   \n",
       "4              1.0               1.0                     0.0           0.0   \n",
       "\n",
       "    competitiveness   operating_risk  class_yn  \n",
       "0               0.0              0.5         0  \n",
       "1               0.0              1.0         0  \n",
       "2               0.0              1.0         0  \n",
       "3               0.0              1.0         0  \n",
       "4               0.0              1.0         0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankrupt_new.drop(' class', inplace = True, axis =1)\n",
    "bankrupt_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis (EDA)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>industrial_risk</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.255127</td>\n",
       "      <td>-0.162624</td>\n",
       "      <td>-0.014438</td>\n",
       "      <td>-0.257814</td>\n",
       "      <td>0.144507</td>\n",
       "      <td>-0.227823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management_risk</th>\n",
       "      <td>0.255127</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.254845</td>\n",
       "      <td>-0.303341</td>\n",
       "      <td>-0.306568</td>\n",
       "      <td>0.213874</td>\n",
       "      <td>-0.370838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>financial_flexibility</th>\n",
       "      <td>-0.162624</td>\n",
       "      <td>-0.254845</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.524951</td>\n",
       "      <td>0.686612</td>\n",
       "      <td>-0.116903</td>\n",
       "      <td>0.751020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>credibility</th>\n",
       "      <td>-0.014438</td>\n",
       "      <td>-0.303341</td>\n",
       "      <td>0.524951</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.675689</td>\n",
       "      <td>-0.288458</td>\n",
       "      <td>0.755909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>competitiveness</th>\n",
       "      <td>-0.257814</td>\n",
       "      <td>-0.306568</td>\n",
       "      <td>0.686612</td>\n",
       "      <td>0.675689</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.211383</td>\n",
       "      <td>0.899452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>operating_risk</th>\n",
       "      <td>0.144507</td>\n",
       "      <td>0.213874</td>\n",
       "      <td>-0.116903</td>\n",
       "      <td>-0.288458</td>\n",
       "      <td>-0.211383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.279786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class_yn</th>\n",
       "      <td>-0.227823</td>\n",
       "      <td>-0.370838</td>\n",
       "      <td>0.751020</td>\n",
       "      <td>0.755909</td>\n",
       "      <td>0.899452</td>\n",
       "      <td>-0.279786</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        industrial_risk   management_risk  \\\n",
       "industrial_risk                1.000000          0.255127   \n",
       " management_risk               0.255127          1.000000   \n",
       " financial_flexibility        -0.162624         -0.254845   \n",
       " credibility                  -0.014438         -0.303341   \n",
       " competitiveness              -0.257814         -0.306568   \n",
       " operating_risk                0.144507          0.213874   \n",
       "class_yn                      -0.227823         -0.370838   \n",
       "\n",
       "                         financial_flexibility   credibility  \\\n",
       "industrial_risk                      -0.162624     -0.014438   \n",
       " management_risk                     -0.254845     -0.303341   \n",
       " financial_flexibility                1.000000      0.524951   \n",
       " credibility                          0.524951      1.000000   \n",
       " competitiveness                      0.686612      0.675689   \n",
       " operating_risk                      -0.116903     -0.288458   \n",
       "class_yn                              0.751020      0.755909   \n",
       "\n",
       "                         competitiveness   operating_risk  class_yn  \n",
       "industrial_risk                -0.257814         0.144507 -0.227823  \n",
       " management_risk               -0.306568         0.213874 -0.370838  \n",
       " financial_flexibility          0.686612        -0.116903  0.751020  \n",
       " credibility                    0.675689        -0.288458  0.755909  \n",
       " competitiveness                1.000000        -0.211383  0.899452  \n",
       " operating_risk                -0.211383         1.000000 -0.279786  \n",
       "class_yn                        0.899452        -0.279786  1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankrupt_new.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(bankrupt_new.corr(), vmin = -1, vmax = 1, annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='class_yn', ylabel='count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JjaEg7UeSdyd5a991SONkKEiSGkNB6iT57SRfT3Jrkk/u0/emJF/r+j6V5Liu/ewkt3ft13Vtpya5Ickt3fut2s/x3pPkLUPrG5JcmOSVSb6Q5Kok30hyeZK5Ji6UjjjvaJYYfJADVwOvqKoHkywBLgS+V1XvT/LMqnqo2/bPgQeq6oNJbgPOqKrvJHlGVT2S5IPAV6vq8m4+qUVV9YM5jrkCuLqqXprkGGA7g+dbvAi4BjiVweSE/wn8UVV9adT/DpIjBWngVcBVVfUgQFXt+2CXFyb5jy4EfpPBBzYMPrAvS/ImYFHX9hXgHUneDjxnrkDojvEt4KEkpwG/DNy8N3iAG6pqZ1X9CLgFWHEEfkfpoAwFaSAc+DkSlwFvrqoXAX8KPAWgqs4H/pjB9OO3dCOKfwTOBH4AfDbJqw7wvh8Ffgd4A3DpUPujQ68fAxYfyi8jHS5DQRrYCrw+yTMButNHw54G7EryJAYjBbrtnldV11fVu4AHgZOTPBf4ZlX9DYNpyF98gON+msEziH+OwUy1Uq/834cEVNUdSTYAX0zyGHAz8K2hTf4EuB74NnAbg5AAeF93ITkMguVW4CLg3CT/B9wP/NkBjvvDJJ8HHqmqx47sbyUdOi80Sz3qLjDfBJxdVdv7rkfy9JHUkySnADuArQaCFgpHCtKIddcpts7RtXro20bSgmAoSJIaTx9JkhpDQZLUGAqSpMZQkCQ1/w9EVS5krteAZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'class_yn', data = bankrupt_new, palette = 'hls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel=' financial_flexibility', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = ' financial_flexibility', data = bankrupt_new, palette = 'hls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='class_yn'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for visualization \n",
    "\n",
    "pd.crosstab(bankrupt.class_yn, bankrupt.industrial_risk).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['industrial_risk', ' management_risk', ' financial_flexibility',\n",
       "       ' credibility', ' competitiveness', ' operating_risk', 'class_yn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankrupt_new.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel=' financial_flexibility'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(bankrupt_new[' financial_flexibility'], bankrupt_new['class_yn']).plot(kind = 'bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel=' credibility'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lqVAGuCQVygCXpEIZ4JJUKF/osA8i4lDa7khN4PnMfKHOJamLImJYZr5c7zqkveFVKF0QEeOB2cCBwOZKcwPwW+BvMvPJ+lSmakTEP2Xm9ZXl44B/BwbRdnfxhZn5WB3LU5UcQBngXRIRK4G/bP8/ekScDnwvM8fVpTBVJSKezMxTKssPADdn5oMRMQG4MTM/WN8KtScOoN7mFErXvK+jUVpm/kdEvK8eBanLDsvMBwEy8/GIGFrvgtSp29n9AOo2oN8MoAzwrnmwMnK7g7efyHgE8OfAorpVpWodHRELaZsyaYiI92bmq5Vtg+pYl6rjAKrCAO+CzLwiIj5B2/PPD6ctCFqA72TmT+panKrR/rn1+8HOOdXv1r4c7SUHUBXOgUsqzm4GUAv72wDKAO9mETGj8gx0Fcjzp5J4I0/381UrZfP8FazyBrB+wznwLoqIY3n7n3BJ28ssFmbm9+pamKri+euz+tVfwI7AuyAivkLbS5wDeJy2l1sEMD8iZtWzNnXO89envV7vAmrJOfAuiIj/Ao7PzO3t2gcDazJzTH0qUzU8f31XRDyXmUfWu45acQqla94EDgOebdc+qrJNvZvnr2AR8dTuNgGH1rKWejPAu+aLwOKIWM/b16EeCRwDfKFeRalqX8TzV7JDgUnA/7RrD+AXtS+nfgzwLsjMRRHxR8AE3nkd6rLM3FHX4tQpz1/x7gf2z8yV7TdExNKaV1NHzoFLUqG8CkWSCmWAS1KhDHD1WxGxMSJGVJZ/Ufk8JyLu383+P4mIgyrLv6t8HhYR91aWx0fE5JoUL2GAq4+JiC79MF/NSxwyc3Jm/rZd2/OZeUFldTxggKtmDHAVJSI+HhFPRsSqiFhcabsmIuZExMPAHRExMiJ+GBHLKv+dWdlveEQ8HBErIuJ77HLb9Vsj6or3R8SPI2JtRMyOiLceN7tzxL7LcY0RsbpyE9B1wIURsTIiLoyI9RExsrLffhHx6/bHS/vCywhVjEoY3gJMzMxnImLYLpv/BPhQZv4hIu4C/jUzH42II4GHgLHA1cCjmXldRHwS2N2DjyYAx9F2o88i4Hzg3j3VlpmvR8RVQFNmfqFS77HAJcCNwEeBVZn5Ule+u9QRA1wlOR34WWY+A9DuLfILM/MPleWPAsdF7Bxgvz8iDgAm0hbGZOYDEdH+RpC3PJ6ZGwAiYj7wIToJ8N34AXAfbQE+jbbXfUndxgBXSYK2Jwd25Pe7LO8HnLFLoLcd3Bbo1dz40H6fLt0skZmbIuKFiPgIcBpto3Gp2zgHrpL8Ejg7IkYDtJtC2dXD7HJLfOUt5gA/oxKilTe6HLyb4ydExOjK3PeFwKNV1rcNOKBd2/eBO4G7vctT3c0AVzEycytt89Y/iohVwL/tZtcrgKaIeCoi1gJ/VWm/FpgYEU8Cfwo8t5vjfwncAKwGngF+XGWJP6Vt6mZlRFxYaVsI7I/TJ+oB3kov9aCIaKLtB9Wz6l2L+h7nwKUeUnk5xF/j3Ld6iCNwSSqUc+CSVCgDXJIKZYBLUqEMcEkqlAEuSYX6f2E/zNsdntw7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(bankrupt_new[' credibility'], bankrupt_new.class_yn).plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel=' operating_risk'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(bankrupt_new[' operating_risk'], bankrupt_new.class_yn).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel=' financial_flexibility'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(bankrupt_new[' financial_flexibility'], bankrupt_new[' credibility']).plot(kind = 'bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(250, 7)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(bankrupt_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input\n",
    "x = bankrupt_new.iloc[:,:-1]\n",
    "\n",
    "# Target variable\n",
    "\n",
    "y = bankrupt_new.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split # trian and test\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing \n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sliptting the data into train and test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing models\n",
    "## 1. Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logisticlassifier = LogisticRegression() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.43044914, -0.73309162,  2.37729553,  2.31022854,  3.68601327,\n",
       "        -0.53101906]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logisticlassifier.fit(x_train, y_train)\n",
    "\n",
    "logisticlassifier.coef_ # coefficients of features\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  After the traing the model then we prediction on test data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0,\n",
       "       1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = logisticlassifier.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### let's test the performance of our model - confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[26,  0],\n",
       "       [ 0, 37]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_logist = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "confusion_logist\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Accuracy of a Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9946524064171123"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train Accuracy\n",
    "\n",
    "train_acc_logist = np.mean(logisticlassifier.predict(x_train)== y_train)\n",
    "train_acc_logist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test Accuracy\n",
    "\n",
    "test_acc_logist = np.mean(logisticlassifier.predict(x_test)== y_test)\n",
    "test_acc_logist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "logistic_acc = accuracy_score(y_test, y_pred)\n",
    "logistic_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy of overall model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.5741981 , -0.75397787,  2.61063166,  2.41434068,  4.00955826,\n",
       "        -0.51358089]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logisticlassifier.fit(x, y)\n",
    "\n",
    "logisticlassifier.coef_ # coefficients of features\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[106,   1],\n",
       "       [  0, 143]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = logisticlassifier.predict(x)\n",
    "\n",
    "confusion_matrix = confusion_matrix(y, y_pred)\n",
    "confusion_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.996"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc = accuracy_score(y, y_pred)\n",
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logisticlassifier.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9946524064171123"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logisticlassifier.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the accuracy we can say that the model is overfitted to avoid overfit problem we use Regularozation method\n",
    "#### here we have L1, L2 regularization\n",
    "##### It turns out they have different but equally useful properties. From a practical standpoint, L1 tends to shrink coefficients to zero whereas L2 tends to shrink coefficients evenly. L1 is therefore useful for feature selection, as we can drop any variables associated with coefficients that go to zero.\n",
    "#### L1 = lasso regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=50, max_iter=100, tol=0.1)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "lasso_reg = linear_model.Lasso(alpha = 50, max_iter = 100, tol =0.1)\n",
    "\n",
    "lasso_reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0017265366431771234"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_reg.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_reg.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### L2 = Ridge regularization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=50, max_iter=100, tol=0.1)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "ridge_reg = Ridge(alpha = 50, max_iter = 100, tol = 0.1)\n",
    "\n",
    "ridge_reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7688398562243322"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_reg.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7452859981668728"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_reg.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. KNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier as KNC\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To choose k value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.937253933193772"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "math.sqrt(len(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we are choosing the k value to be  7 (choosing odd value)\n",
    "\n",
    "Define the model KNN and fit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "KNN_classifier = KNC(n_neighbors =7, p = 2, metric = 'euclidean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(metric='euclidean', n_neighbors=7)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_classifier.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predict the Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = KNN_classifier.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluate model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[26  0]\n",
      " [ 1 36]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9863013698630138\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "print(f1_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy of KNN model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9841269841269841"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "KNN_acc = accuracy_score(y_test, y_pred)\n",
    "KNN_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "from sklearn.naive_bayes import MultinomialNB\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating GaussianNB and MultinomialNB functions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "GNB = GaussianNB()\n",
    "MNB = MultinomialNB()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building the model with GaussianNB\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0,\n",
       "       1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Naive_GNB = GNB.fit(x_train ,y_train)\n",
    "\n",
    "y_pred = Naive_GNB.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluate Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[26  0]\n",
      " [ 0 37]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy of GNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "GNB_acc = accuracy_score(y_test , y_pred)\n",
    "GNB_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building the model with MultinomialNB\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0,\n",
       "       1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Naive_MNB = MNB.fit(x_train ,y_train)\n",
    "\n",
    "y_pred = Naive_MNB.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluating Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[26  0]\n",
      " [ 0 37]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy of MNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "MNB_acc = accuracy_score(y_test , y_pred)\n",
    "MNB_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Kernel = Linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_linear = SVC(kernel = 'linear')\n",
    "\n",
    "model_linear.fit(x_train, y_train)\n",
    "\n",
    "pred_test_linear = model_linear.predict(x_test)\n",
    "\n",
    "np.mean(pred_test_linear==y_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Kernel = ploy model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9841269841269841"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_poly = SVC(kernel = \"poly\")\n",
    "model_poly.fit(x_train,y_train)\n",
    "pred_test_poly = model_poly.predict(x_test)\n",
    "\n",
    "np.mean(pred_test_poly==y_test) # Accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Kernel = 'rbf' model --> Radial Basis Function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rbf = SVC(kernel = \"rbf\")\n",
    "model_rbf.fit(x_train,y_train)\n",
    "pred_test_rbf = model_rbf.predict(x_test)\n",
    "\n",
    "np.mean(pred_test_rbf==y_test) # Accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from the above kernels in SVM polynomial kernel giving good accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle_out = open(\"model_poly.pkl\",\"wb\")\n",
    "pickle.dump(model_poly, pickle_out)\n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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