credit card approval prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1ab43a6c",
   "metadata": {},
   "source": [
    "# Credit Card Ap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "26d17d8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from intro_Data import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "8006765d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('cc_approvals.data',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "99df49ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "attribute = ['A'+str(i+1) for i in range(len(data.columns))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "23fc46ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns = attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "b99b1798",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "      <th>A16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b</td>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.25</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00202</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>3.04</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00043</td>\n",
       "      <td>560</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>24.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>1.50</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00280</td>\n",
       "      <td>824</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>3.75</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>00100</td>\n",
       "      <td>3</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.71</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>00120</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  A1     A2     A3 A4 A5 A6 A7    A8 A9 A10  A11 A12 A13    A14  A15 A16\n",
       "0  b  30.83  0.000  u  g  w  v  1.25  t   t    1   f   g  00202    0   +\n",
       "1  a  58.67  4.460  u  g  q  h  3.04  t   t    6   f   g  00043  560   +\n",
       "2  a  24.50  0.500  u  g  q  h  1.50  t   f    0   f   g  00280  824   +\n",
       "3  b  27.83  1.540  u  g  w  v  3.75  t   t    5   t   g  00100    3   +\n",
       "4  b  20.17  5.625  u  g  w  v  1.71  t   f    0   f   s  00120    0   +"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "b98cbc30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 690 entries, 0 to 689\n",
      "Data columns (total 16 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   A1      690 non-null    object \n",
      " 1   A2      690 non-null    object \n",
      " 2   A3      690 non-null    float64\n",
      " 3   A4      690 non-null    object \n",
      " 4   A5      690 non-null    object \n",
      " 5   A6      690 non-null    object \n",
      " 6   A7      690 non-null    object \n",
      " 7   A8      690 non-null    float64\n",
      " 8   A9      690 non-null    object \n",
      " 9   A10     690 non-null    object \n",
      " 10  A11     690 non-null    int64  \n",
      " 11  A12     690 non-null    object \n",
      " 12  A13     690 non-null    object \n",
      " 13  A14     690 non-null    object \n",
      " 14  A15     690 non-null    int64  \n",
      " 15  A16     690 non-null    object \n",
      "dtypes: float64(2), int64(2), object(12)\n",
      "memory usage: 86.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "2749e538",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['A16'] = data['A16'].apply(lambda x: 1 if x=='+' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "1d27cc44",
   "metadata": {},
   "outputs": [],
   "source": [
    "for columns in data.columns:\n",
    "    data[columns] = data[columns].apply(lambda x: np.nan if x=='?' else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "f229499f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['A2'] = data['A2'].astype('float64')\n",
    "data['A8'] = data['A8'].astype('float64')\n",
    "data['A14'] = data['A14'].astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "75bbbc14",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A1 has 2 unique values\n",
      "A2 has 349 unique values\n",
      "A3 has 215 unique values\n",
      "A4 has 3 unique values\n",
      "A5 has 3 unique values\n",
      "A6 has 14 unique values\n",
      "A7 has 9 unique values\n",
      "A8 has 132 unique values\n",
      "A9 has 2 unique values\n",
      "A10 has 2 unique values\n",
      "A11 has 23 unique values\n",
      "A12 has 2 unique values\n",
      "A13 has 3 unique values\n",
      "A14 has 170 unique values\n",
      "A15 has 240 unique values\n",
      "A16 has 2 unique values\n"
     ]
    }
   ],
   "source": [
    "for columns in data.columns:\n",
    "    print(columns,'has',data[columns].nunique(),'unique values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "ac7d22e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = np.triu(corr_matrix)\n",
    "cols = ['A2','A3','A8','A11','A14','A15']\n",
    "corr_matrix = data[cols].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "d5980820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr_matrix,mask=mask,annot=True,cmap='BuPu',vmin=-1,vmax=1,\n",
    "           fmt='.2f',xticklabels=cols[:-1],yticklabels=['']+cols[1:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79905907",
   "metadata": {},
   "source": [
    "Numerical values don't seem to have any strong linear correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "4f604deb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "8ec6d69c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for columns in cols:\n",
    "    median = np.nanmedian(data[columns])\n",
    "    data[columns] = data[columns].apply(lambda x: median if pd.isnull(x) else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "cea8574a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A1 has 12 missing values\n",
      "A2 has 0 missing values\n",
      "A3 has 0 missing values\n",
      "A4 has 6 missing values\n",
      "A5 has 6 missing values\n",
      "A6 has 9 missing values\n",
      "A7 has 9 missing values\n",
      "A8 has 0 missing values\n",
      "A9 has 0 missing values\n",
      "A10 has 0 missing values\n",
      "A11 has 0 missing values\n",
      "A12 has 0 missing values\n",
      "A13 has 0 missing values\n",
      "A14 has 0 missing values\n",
      "A15 has 0 missing values\n",
      "A16 has 0 missing values\n"
     ]
    }
   ],
   "source": [
    "for columns in data.columns:\n",
    "    print(columns,'has',data[columns].isnull().sum(),'missing values')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "023baef6",
   "metadata": {},
   "source": [
    "We will drop other missing values as we cannot infer anything about them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "189b8d36",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "e575bdcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "      <th>A16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b</td>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.25</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>202.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>3.04</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>43.0</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>24.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>1.50</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>280.0</td>\n",
       "      <td>824</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>3.75</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.71</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  A1     A2     A3 A4 A5 A6 A7    A8 A9 A10  A11 A12 A13    A14  A15  A16\n",
       "0  b  30.83  0.000  u  g  w  v  1.25  t   t    1   f   g  202.0    0    1\n",
       "1  a  58.67  4.460  u  g  q  h  3.04  t   t    6   f   g   43.0  560    1\n",
       "2  a  24.50  0.500  u  g  q  h  1.50  t   f    0   f   g  280.0  824    1\n",
       "3  b  27.83  1.540  u  g  w  v  3.75  t   t    5   t   g  100.0    3    1\n",
       "4  b  20.17  5.625  u  g  w  v  1.71  t   f    0   f   s  120.0    0    1"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "cbd0f7f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature = data.drop('A16',axis=1)\n",
    "target = data['A16']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "70f8b723",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b</td>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.25</td>\n",
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       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>202.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>3.04</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>43.0</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>24.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>1.50</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>280.0</td>\n",
       "      <td>824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>3.75</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.71</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  A1     A2     A3 A4 A5 A6 A7    A8 A9 A10  A11 A12 A13    A14  A15\n",
       "0  b  30.83  0.000  u  g  w  v  1.25  t   t    1   f   g  202.0    0\n",
       "1  a  58.67  4.460  u  g  q  h  3.04  t   t    6   f   g   43.0  560\n",
       "2  a  24.50  0.500  u  g  q  h  1.50  t   f    0   f   g  280.0  824\n",
       "3  b  27.83  1.540  u  g  w  v  3.75  t   t    5   t   g  100.0    3\n",
       "4  b  20.17  5.625  u  g  w  v  1.71  t   f    0   f   s  120.0    0"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c8708a8",
   "metadata": {},
   "source": [
    "Create dummy variables for categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "63ccf55e",
   "metadata": {},
   "outputs": [],
   "source": [
    "A1 = pd.get_dummies(feature['A1'],drop_first=True)\n",
    "A4 = pd.get_dummies(feature['A4'],drop_first=True)\n",
    "A5 = pd.get_dummies(feature['A5'],drop_first=True)\n",
    "A6 = pd.get_dummies(feature['A6'],drop_first=True)\n",
    "A7 = pd.get_dummies(feature['A7'],drop_first=True)\n",
    "A9 = pd.get_dummies(feature['A9'],drop_first=True)\n",
    "A10 = pd.get_dummies(feature['A10'],drop_first=True)\n",
    "A12 = pd.get_dummies(feature['A12'],drop_first=True)\n",
    "A13 = pd.get_dummies(feature['A13'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "74548448",
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy_col = ['A1','A4','A5','A6','A7','A9','A10','A12','A13']\n",
    "feature = feature.drop(dummy_col,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "9ea0b8d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature = pd.concat([feature,A1,A4,A5,A6,A7,A9,A10,A12,A13],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "98d60e17",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>24.50</td>\n",
       "      <td>0.500</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>3.75</td>\n",
       "      <td>5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      A2     A3    A8  A11    A14  A15  b  u  y  gg  ...  j  n  o  v  z  t  t  \\\n",
       "0  30.83  0.000  1.25    1  202.0    0  1  1  0   0  ...  0  0  0  1  0  1  1   \n",
       "1  58.67  4.460  3.04    6   43.0  560  0  1  0   0  ...  0  0  0  0  0  1  1   \n",
       "2  24.50  0.500  1.50    0  280.0  824  0  1  0   0  ...  0  0  0  0  0  1  0   \n",
       "3  27.83  1.540  3.75    5  100.0    3  1  1  0   0  ...  0  0  0  1  0  1  1   \n",
       "4  20.17  5.625  1.71    0  120.0    0  1  1  0   0  ...  0  0  0  1  0  1  0   \n",
       "\n",
       "   t  p  s  \n",
       "0  0  0  0  \n",
       "1  0  0  0  \n",
       "2  0  0  0  \n",
       "3  1  0  0  \n",
       "4  0  0  1  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7161169",
   "metadata": {},
   "source": [
    "# Now we are ready to model our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "b4b1bc48",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = feature.copy().values\n",
    "y = target.copy().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "86609dd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import tree\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "a4082c81",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "650af8e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "1b870ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "2f5f9ac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Single Decision Tree Train Score: 1.0\n",
      "Single Decision Tree Test Score: 0.8267326732673267\n"
     ]
    }
   ],
   "source": [
    "print('Single Decision Tree Train Score:',dtree.score(X_train,y_train))\n",
    "print('Single Decision Tree Test Score:',dtree.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8353612f",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "3f63e011",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "82c097d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "7a7f1b55",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = dict(n_estimators=[1,100,500],min_samples_split=[i for i in range(4,10)],\n",
    "                max_depth=[i for i in range(4,10)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "36594dce",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(rfc,param_grid,scoring='accuracy',cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "8c5910a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [4, 5, 6, 7, 8, 9],\n",
       "                         &#x27;min_samples_split&#x27;: [4, 5, 6, 7, 8, 9],\n",
       "                         &#x27;n_estimators&#x27;: [1, 100, 500]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [4, 5, 6, 7, 8, 9],\n",
       "                         &#x27;min_samples_split&#x27;: [4, 5, 6, 7, 8, 9],\n",
       "                         &#x27;n_estimators&#x27;: [1, 100, 500]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={'max_depth': [4, 5, 6, 7, 8, 9],\n",
       "                         'min_samples_split': [4, 5, 6, 7, 8, 9],\n",
       "                         'n_estimators': [1, 100, 500]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "0c12a1f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'max_depth': 5, 'min_samples_split': 4, 'n_estimators': 100}\n",
      "Best Score: 0.8690768380320619\n"
     ]
    }
   ],
   "source": [
    "print('Best Hyperparameters:',grid.best_params_)\n",
    "print('Best Score:',grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "25a5f48a",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.DataFrame(grid.cv_results_)[['param_n_estimators','param_max_depth',\n",
    "                                         'param_min_samples_split','mean_test_score','rank_test_score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "f4ad7af3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.869077</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   param_n_estimators param_max_depth param_min_samples_split  \\\n",
       "19                100               5                       4   \n",
       "\n",
       "    mean_test_score  rank_test_score  \n",
       "19         0.869077                1  "
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[results['rank_test_score']==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6742245f",
   "metadata": {},
   "source": [
    "We found the best parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "0b1d2502",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=100,max_depth=5,min_samples_split=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "id": "6ac73c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_score, test_score = do_Kfold(rfc,X,y,k=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "c1dd8263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_score,'-xk',label='Train')\n",
    "plt.plot(test_score,'-xr',label='Test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "id": "2872f307",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Train Score w/ Optimal Hyperparameters: 0.9133960440622495\n",
      "Average Test Score w/ Optimal Hyperparameters: 0.865891132572432\n"
     ]
    }
   ],
   "source": [
    "print('Average Train Score w/ Optimal Hyperparameters:',np.mean(train_score))\n",
    "print('Average Test Score w/ Optimal Hyperparameters:',np.mean(test_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "597d6510",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "c146d27d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "ba5b5b69",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "id": "cc98c37d",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "1d309417",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_score, test_score = do_Kfold(log_reg,X,y,k=10,scaler=scaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "5e5af731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_score,'-xk',label='Train')\n",
    "plt.plot(test_score,'-xr',label='Test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "id": "12b4e5cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Train Score w/ Optimal Hyperparameters: 0.890710630072595\n",
      "Average Test Score w/ Optimal Hyperparameters: 0.8673617208077262\n"
     ]
    }
   ],
   "source": [
    "print('Average Train Score w/ Optimal Hyperparameters:',np.mean(train_score))\n",
    "print('Average Test Score w/ Optimal Hyperparameters:',np.mean(test_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4127dd1",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "ac80424b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "id": "2d29066c",
   "metadata": {},
   "outputs": [],
   "source": [
    "neighbor_range = range(100,200)\n",
    "k = 5\n",
    "train = []\n",
    "test = []\n",
    "\n",
    "for i in neighbor_range:\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    train_score, test_score = do_Kfold(knn,X,y,k,scaler=scaler)\n",
    "    train.append(np.mean(train_score))\n",
    "    test.append(np.mean(test_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "id": "449f2038",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train,'-xk',label='Train')\n",
    "plt.plot(test,'-xr',label='Test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "id": "7689499d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Test Score: 0.8360641238253178\n",
      "Number of neighbors: 161\n"
     ]
    }
   ],
   "source": [
    "print('Maximum Test Score:',np.max(test))\n",
    "print('Number of neighbors:',np.argmax(test)+100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "id": "44442103",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=161)\n",
    "train, test = do_Kfold(knn,X,y,k=10,scaler=scaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "id": "169877d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Train Score w/ n_neighbors = 161: 0.8226530152768168\n",
      "Average Test Score w/ n_neighbors = 161: 0.8331650570676032\n"
     ]
    }
   ],
   "source": [
    "print('Average Train Score w/ n_neighbors = 161:',np.mean(train))\n",
    "print('Average Test Score w/ n_neighbors = 161:',np.mean(test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59e82fd7",
   "metadata": {},
   "source": [
    "### Naive-Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 729,
   "id": "4c7919c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 730,
   "id": "0ed46477",
   "metadata": {},
   "outputs": [],
   "source": [
    "mnb = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 731,
   "id": "35124fa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = do_Kfold(mnb,X,y,k=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 732,
   "id": "1aac2533",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Train Score: 0.6749461851888461\n",
      "Average Test Score: 0.6766022827041265\n"
     ]
    }
   ],
   "source": [
    "print('Average Train Score:',np.mean(train))\n",
    "print('Average Test Score:',np.mean(test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55aeb79f",
   "metadata": {},
   "source": [
    "Produces bad accuracy because the features are not independent(?)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e57a43a9",
   "metadata": {},
   "source": [
    "### Linear SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "id": "a1af6f41",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "id": "961ac18b",
   "metadata": {},
   "outputs": [],
   "source": [
    "C = [1,0.1,0.01,0.001,0.0001,0.00001]\n",
    "\n",
    "train_avg = []\n",
    "test_avg = []\n",
    "\n",
    "for i in C:\n",
    "    svm = LinearSVC(max_iter = 10000, C = i)\n",
    "    train, test = do_Kfold(svm,X,y,k=10,scaler=scaler)\n",
    "    train_avg.append(np.mean(train))\n",
    "    test_avg.append(np.mean(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "id": "9495d1f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_avg,'-xk',label='Train')\n",
    "plt.plot(test_avg,'-xr',label='Test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80eaf204",
   "metadata": {},
   "source": [
    "We see the best result at C = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "id": "5a868652",
   "metadata": {},
   "outputs": [],
   "source": [
    "svm = LinearSVC(max_iter = 10000, C = 0.001)\n",
    "train, test = do_Kfold(svm,X,y,k=10,scaler=scaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "id": "a1e5da70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Train Score: 0.8837567131231261\n",
      "Average Test Score: 0.8733757682177348\n"
     ]
    }
   ],
   "source": [
    "print('Average Train Score:',np.mean(train))\n",
    "print('Average Test Score:',np.mean(test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fd1d5a3",
   "metadata": {},
   "source": [
    "### Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 708,
   "id": "86517a8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 709,
   "id": "0f3c4449",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=146)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 710,
   "id": "52d221b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 711,
   "id": "80529fd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "mms = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 712,
   "id": "21027e4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = mms.fit_transform(X_train)\n",
    "X_test = mms.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 713,
   "id": "83708fe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stopping = EarlyStopping(monitor='val_loss',patience=25,mode='min',verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 714,
   "id": "7d4527e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(20,activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(10,activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(10,activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(1,activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',optimizer='adam',metrics='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 715,
   "id": "c8998e55",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "2/2 [==============================] - 0s 59ms/step - loss: 0.7744 - accuracy: 0.4925 - val_loss: 0.7037 - val_accuracy: 0.4208\n",
      "Epoch 2/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.7451 - accuracy: 0.5011 - val_loss: 0.7001 - val_accuracy: 0.4208\n",
      "Epoch 3/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.7190 - accuracy: 0.5458 - val_loss: 0.6967 - val_accuracy: 0.4257\n",
      "Epoch 4/300\n",
      "2/2 [==============================] - 0s 14ms/step - loss: 0.7336 - accuracy: 0.5075 - val_loss: 0.6937 - val_accuracy: 0.4307\n",
      "Epoch 5/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.7420 - accuracy: 0.4648 - val_loss: 0.6909 - val_accuracy: 0.4356\n",
      "Epoch 6/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.7195 - accuracy: 0.5245 - val_loss: 0.6885 - val_accuracy: 0.4802\n",
      "Epoch 7/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.7252 - accuracy: 0.5267 - val_loss: 0.6863 - val_accuracy: 0.4802\n",
      "Epoch 8/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.6901 - accuracy: 0.5437 - val_loss: 0.6841 - val_accuracy: 0.5099\n",
      "Epoch 9/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.6974 - accuracy: 0.5501 - val_loss: 0.6823 - val_accuracy: 0.5495\n",
      "Epoch 10/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.7023 - accuracy: 0.5203 - val_loss: 0.6805 - val_accuracy: 0.5842\n",
      "Epoch 11/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.7105 - accuracy: 0.5160 - val_loss: 0.6789 - val_accuracy: 0.5941\n",
      "Epoch 12/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.7012 - accuracy: 0.5309 - val_loss: 0.6774 - val_accuracy: 0.6337\n",
      "Epoch 13/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.7184 - accuracy: 0.4947 - val_loss: 0.6758 - val_accuracy: 0.6832\n",
      "Epoch 14/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.7081 - accuracy: 0.5053 - val_loss: 0.6744 - val_accuracy: 0.6881\n",
      "Epoch 15/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.6929 - accuracy: 0.5267 - val_loss: 0.6733 - val_accuracy: 0.7030\n",
      "Epoch 16/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.6781 - accuracy: 0.5885 - val_loss: 0.6723 - val_accuracy: 0.7129\n",
      "Epoch 17/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.6873 - accuracy: 0.5544 - val_loss: 0.6713 - val_accuracy: 0.7228\n",
      "Epoch 18/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.6938 - accuracy: 0.5394 - val_loss: 0.6704 - val_accuracy: 0.7475\n",
      "Epoch 19/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.6794 - accuracy: 0.5693 - val_loss: 0.6694 - val_accuracy: 0.7475\n",
      "Epoch 20/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.6778 - accuracy: 0.5672 - val_loss: 0.6684 - val_accuracy: 0.7475\n",
      "Epoch 21/300\n",
      "2/2 [==============================] - 0s 14ms/step - loss: 0.6905 - accuracy: 0.5714 - val_loss: 0.6671 - val_accuracy: 0.7525\n",
      "Epoch 22/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.6784 - accuracy: 0.5437 - val_loss: 0.6655 - val_accuracy: 0.7574\n",
      "Epoch 23/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.6728 - accuracy: 0.5885 - val_loss: 0.6638 - val_accuracy: 0.7574\n",
      "Epoch 24/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.6865 - accuracy: 0.5608 - val_loss: 0.6619 - val_accuracy: 0.7574\n",
      "Epoch 25/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.6987 - accuracy: 0.5629 - val_loss: 0.6604 - val_accuracy: 0.7624\n",
      "Epoch 26/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.6776 - accuracy: 0.5885 - val_loss: 0.6587 - val_accuracy: 0.7673\n",
      "Epoch 27/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.6814 - accuracy: 0.6098 - val_loss: 0.6570 - val_accuracy: 0.7871\n",
      "Epoch 28/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.6656 - accuracy: 0.6162 - val_loss: 0.6552 - val_accuracy: 0.7921\n",
      "Epoch 29/300\n",
      "2/2 [==============================] - 0s 14ms/step - loss: 0.6601 - accuracy: 0.6226 - val_loss: 0.6534 - val_accuracy: 0.7921\n",
      "Epoch 30/300\n",
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      "Epoch 32/300\n",
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      "Epoch 39/300\n",
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      "Epoch 43/300\n",
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      "Epoch 44/300\n",
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      "Epoch 45/300\n",
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      "Epoch 46/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.6478 - accuracy: 0.6439 - val_loss: 0.6265 - val_accuracy: 0.8267\n",
      "Epoch 47/300\n",
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      "Epoch 48/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.6638 - accuracy: 0.6077 - val_loss: 0.6231 - val_accuracy: 0.8366\n",
      "Epoch 49/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.6417 - accuracy: 0.6631 - val_loss: 0.6214 - val_accuracy: 0.8366\n",
      "Epoch 50/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.6349 - accuracy: 0.6567 - val_loss: 0.6195 - val_accuracy: 0.8267\n",
      "Epoch 51/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.6462 - accuracy: 0.6674 - val_loss: 0.6175 - val_accuracy: 0.8317\n",
      "Epoch 52/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.6344 - accuracy: 0.6716 - val_loss: 0.6157 - val_accuracy: 0.8267\n",
      "Epoch 53/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.6485 - accuracy: 0.6247 - val_loss: 0.6140 - val_accuracy: 0.8267\n",
      "Epoch 54/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.6578 - accuracy: 0.5991 - val_loss: 0.6125 - val_accuracy: 0.8267\n",
      "Epoch 55/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.6350 - accuracy: 0.6567 - val_loss: 0.6109 - val_accuracy: 0.8267\n",
      "Epoch 56/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.6383 - accuracy: 0.6418 - val_loss: 0.6093 - val_accuracy: 0.8218\n",
      "Epoch 57/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.6459 - accuracy: 0.6482 - val_loss: 0.6079 - val_accuracy: 0.8218\n",
      "Epoch 58/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.6244 - accuracy: 0.6930 - val_loss: 0.6062 - val_accuracy: 0.8218\n",
      "Epoch 59/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 10ms/step - loss: 0.6437 - accuracy: 0.6375 - val_loss: 0.6046 - val_accuracy: 0.8218\n",
      "Epoch 60/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.6187 - accuracy: 0.7036 - val_loss: 0.6028 - val_accuracy: 0.8218\n",
      "Epoch 61/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.6312 - accuracy: 0.6759 - val_loss: 0.6008 - val_accuracy: 0.8218\n",
      "Epoch 62/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.6235 - accuracy: 0.6588 - val_loss: 0.5988 - val_accuracy: 0.8218\n",
      "Epoch 63/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.6350 - accuracy: 0.6908 - val_loss: 0.5969 - val_accuracy: 0.8218\n",
      "Epoch 64/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.6408 - accuracy: 0.6418 - val_loss: 0.5952 - val_accuracy: 0.8218\n",
      "Epoch 65/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.6435 - accuracy: 0.6375 - val_loss: 0.5938 - val_accuracy: 0.8218\n",
      "Epoch 66/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.6162 - accuracy: 0.7122 - val_loss: 0.5922 - val_accuracy: 0.8267\n",
      "Epoch 67/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.6066 - accuracy: 0.7122 - val_loss: 0.5901 - val_accuracy: 0.8267\n",
      "Epoch 68/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.6131 - accuracy: 0.7122 - val_loss: 0.5879 - val_accuracy: 0.8317\n",
      "Epoch 69/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.6226 - accuracy: 0.7164 - val_loss: 0.5856 - val_accuracy: 0.8267\n",
      "Epoch 70/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.6179 - accuracy: 0.6844 - val_loss: 0.5834 - val_accuracy: 0.8317\n",
      "Epoch 71/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.6152 - accuracy: 0.6930 - val_loss: 0.5810 - val_accuracy: 0.8366\n",
      "Epoch 72/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.6037 - accuracy: 0.7079 - val_loss: 0.5786 - val_accuracy: 0.8366\n",
      "Epoch 73/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.6002 - accuracy: 0.6802 - val_loss: 0.5761 - val_accuracy: 0.8366\n",
      "Epoch 74/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.6045 - accuracy: 0.6908 - val_loss: 0.5739 - val_accuracy: 0.8416\n",
      "Epoch 75/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.6037 - accuracy: 0.6972 - val_loss: 0.5718 - val_accuracy: 0.8366\n",
      "Epoch 76/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.6096 - accuracy: 0.7207 - val_loss: 0.5696 - val_accuracy: 0.8366\n",
      "Epoch 77/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.5997 - accuracy: 0.7058 - val_loss: 0.5674 - val_accuracy: 0.8366\n",
      "Epoch 78/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.6102 - accuracy: 0.6866 - val_loss: 0.5652 - val_accuracy: 0.8416\n",
      "Epoch 79/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.6156 - accuracy: 0.6908 - val_loss: 0.5629 - val_accuracy: 0.8366\n",
      "Epoch 80/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.6172 - accuracy: 0.7164 - val_loss: 0.5609 - val_accuracy: 0.8317\n",
      "Epoch 81/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.5903 - accuracy: 0.7399 - val_loss: 0.5587 - val_accuracy: 0.8317\n",
      "Epoch 82/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5903 - accuracy: 0.7058 - val_loss: 0.5563 - val_accuracy: 0.8366\n",
      "Epoch 83/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.6237 - accuracy: 0.7015 - val_loss: 0.5541 - val_accuracy: 0.8366\n",
      "Epoch 84/300\n",
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      "Epoch 85/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.6061 - accuracy: 0.7015 - val_loss: 0.5501 - val_accuracy: 0.8416\n",
      "Epoch 86/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.5962 - accuracy: 0.6994 - val_loss: 0.5480 - val_accuracy: 0.8416\n",
      "Epoch 87/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5942 - accuracy: 0.7186 - val_loss: 0.5460 - val_accuracy: 0.8465\n",
      "Epoch 88/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5754 - accuracy: 0.7100 - val_loss: 0.5440 - val_accuracy: 0.8416\n",
      "Epoch 89/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5990 - accuracy: 0.7015 - val_loss: 0.5423 - val_accuracy: 0.8416\n",
      "Epoch 90/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.5695 - accuracy: 0.7420 - val_loss: 0.5404 - val_accuracy: 0.8416\n",
      "Epoch 91/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.5922 - accuracy: 0.7186 - val_loss: 0.5384 - val_accuracy: 0.8416\n",
      "Epoch 92/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5804 - accuracy: 0.7143 - val_loss: 0.5365 - val_accuracy: 0.8416\n",
      "Epoch 93/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5587 - accuracy: 0.7441 - val_loss: 0.5342 - val_accuracy: 0.8465\n",
      "Epoch 94/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.5695 - accuracy: 0.7527 - val_loss: 0.5317 - val_accuracy: 0.8416\n",
      "Epoch 95/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.5577 - accuracy: 0.7527 - val_loss: 0.5291 - val_accuracy: 0.8416\n",
      "Epoch 96/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.5730 - accuracy: 0.7228 - val_loss: 0.5263 - val_accuracy: 0.8416\n",
      "Epoch 97/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5581 - accuracy: 0.7591 - val_loss: 0.5234 - val_accuracy: 0.8416\n",
      "Epoch 98/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.5636 - accuracy: 0.7463 - val_loss: 0.5202 - val_accuracy: 0.8416\n",
      "Epoch 99/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5746 - accuracy: 0.7484 - val_loss: 0.5176 - val_accuracy: 0.8416\n",
      "Epoch 100/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.5362 - accuracy: 0.7655 - val_loss: 0.5149 - val_accuracy: 0.8416\n",
      "Epoch 101/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5408 - accuracy: 0.7612 - val_loss: 0.5122 - val_accuracy: 0.8416\n",
      "Epoch 102/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.5493 - accuracy: 0.7676 - val_loss: 0.5096 - val_accuracy: 0.8416\n",
      "Epoch 103/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5915 - accuracy: 0.7569 - val_loss: 0.5073 - val_accuracy: 0.8416\n",
      "Epoch 104/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.5442 - accuracy: 0.7783 - val_loss: 0.5053 - val_accuracy: 0.8416\n",
      "Epoch 105/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.5391 - accuracy: 0.7719 - val_loss: 0.5030 - val_accuracy: 0.8416\n",
      "Epoch 106/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.5264 - accuracy: 0.7761 - val_loss: 0.5004 - val_accuracy: 0.8416\n",
      "Epoch 107/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5405 - accuracy: 0.7697 - val_loss: 0.4980 - val_accuracy: 0.8416\n",
      "Epoch 108/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5348 - accuracy: 0.7740 - val_loss: 0.4957 - val_accuracy: 0.8416\n",
      "Epoch 109/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.5321 - accuracy: 0.7420 - val_loss: 0.4935 - val_accuracy: 0.8416\n",
      "Epoch 110/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.5289 - accuracy: 0.7697 - val_loss: 0.4911 - val_accuracy: 0.8416\n",
      "Epoch 111/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.5344 - accuracy: 0.7612 - val_loss: 0.4885 - val_accuracy: 0.8416\n",
      "Epoch 112/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5345 - accuracy: 0.7569 - val_loss: 0.4861 - val_accuracy: 0.8416\n",
      "Epoch 113/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5681 - accuracy: 0.7207 - val_loss: 0.4840 - val_accuracy: 0.8465\n",
      "Epoch 114/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.5050 - accuracy: 0.7996 - val_loss: 0.4821 - val_accuracy: 0.8416\n",
      "Epoch 115/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.5514 - accuracy: 0.7484 - val_loss: 0.4801 - val_accuracy: 0.8416\n",
      "Epoch 116/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.5224 - accuracy: 0.7697 - val_loss: 0.4781 - val_accuracy: 0.8416\n",
      "Epoch 117/300\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 12ms/step - loss: 0.5140 - accuracy: 0.7846 - val_loss: 0.4763 - val_accuracy: 0.8416\n",
      "Epoch 118/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.4979 - accuracy: 0.7974 - val_loss: 0.4744 - val_accuracy: 0.8416\n",
      "Epoch 119/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.5012 - accuracy: 0.7868 - val_loss: 0.4724 - val_accuracy: 0.8416\n",
      "Epoch 120/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5366 - accuracy: 0.7548 - val_loss: 0.4708 - val_accuracy: 0.8465\n",
      "Epoch 121/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4861 - accuracy: 0.7889 - val_loss: 0.4695 - val_accuracy: 0.8416\n",
      "Epoch 122/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5169 - accuracy: 0.7783 - val_loss: 0.4684 - val_accuracy: 0.8416\n",
      "Epoch 123/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5107 - accuracy: 0.7932 - val_loss: 0.4673 - val_accuracy: 0.8416\n",
      "Epoch 124/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5101 - accuracy: 0.7783 - val_loss: 0.4661 - val_accuracy: 0.8416\n",
      "Epoch 125/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5027 - accuracy: 0.7761 - val_loss: 0.4647 - val_accuracy: 0.8416\n",
      "Epoch 126/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.4793 - accuracy: 0.8145 - val_loss: 0.4630 - val_accuracy: 0.8366\n",
      "Epoch 127/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5218 - accuracy: 0.7953 - val_loss: 0.4612 - val_accuracy: 0.8366\n",
      "Epoch 128/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5061 - accuracy: 0.7804 - val_loss: 0.4595 - val_accuracy: 0.8366\n",
      "Epoch 129/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.4958 - accuracy: 0.7932 - val_loss: 0.4579 - val_accuracy: 0.8366\n",
      "Epoch 130/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4823 - accuracy: 0.7825 - val_loss: 0.4558 - val_accuracy: 0.8366\n",
      "Epoch 131/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.5263 - accuracy: 0.7932 - val_loss: 0.4537 - val_accuracy: 0.8416\n",
      "Epoch 132/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.4861 - accuracy: 0.7846 - val_loss: 0.4518 - val_accuracy: 0.8465\n",
      "Epoch 133/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4860 - accuracy: 0.8102 - val_loss: 0.4502 - val_accuracy: 0.8465\n",
      "Epoch 134/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4815 - accuracy: 0.7932 - val_loss: 0.4486 - val_accuracy: 0.8465\n",
      "Epoch 135/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4955 - accuracy: 0.7868 - val_loss: 0.4472 - val_accuracy: 0.8465\n",
      "Epoch 136/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4790 - accuracy: 0.7889 - val_loss: 0.4456 - val_accuracy: 0.8465\n",
      "Epoch 137/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.5040 - accuracy: 0.7932 - val_loss: 0.4442 - val_accuracy: 0.8465\n",
      "Epoch 138/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4728 - accuracy: 0.7974 - val_loss: 0.4431 - val_accuracy: 0.8465\n",
      "Epoch 139/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4742 - accuracy: 0.8102 - val_loss: 0.4421 - val_accuracy: 0.8465\n",
      "Epoch 140/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4656 - accuracy: 0.8081 - val_loss: 0.4410 - val_accuracy: 0.8366\n",
      "Epoch 141/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.4883 - accuracy: 0.7932 - val_loss: 0.4402 - val_accuracy: 0.8366\n",
      "Epoch 142/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.4706 - accuracy: 0.7889 - val_loss: 0.4394 - val_accuracy: 0.8267\n",
      "Epoch 143/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4882 - accuracy: 0.8017 - val_loss: 0.4388 - val_accuracy: 0.8317\n",
      "Epoch 144/300\n",
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      "Epoch 145/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.4698 - accuracy: 0.7996 - val_loss: 0.4378 - val_accuracy: 0.8317\n",
      "Epoch 146/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4716 - accuracy: 0.7996 - val_loss: 0.4371 - val_accuracy: 0.8317\n",
      "Epoch 147/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.4605 - accuracy: 0.8124 - val_loss: 0.4358 - val_accuracy: 0.8317\n",
      "Epoch 148/300\n",
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      "Epoch 149/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4982 - accuracy: 0.7825 - val_loss: 0.4335 - val_accuracy: 0.8317\n",
      "Epoch 150/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.4467 - accuracy: 0.8166 - val_loss: 0.4326 - val_accuracy: 0.8366\n",
      "Epoch 151/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.4597 - accuracy: 0.8188 - val_loss: 0.4316 - val_accuracy: 0.8366\n",
      "Epoch 152/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.4652 - accuracy: 0.8124 - val_loss: 0.4309 - val_accuracy: 0.8366\n",
      "Epoch 153/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4491 - accuracy: 0.8145 - val_loss: 0.4301 - val_accuracy: 0.8317\n",
      "Epoch 154/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.4582 - accuracy: 0.8273 - val_loss: 0.4291 - val_accuracy: 0.8366\n",
      "Epoch 155/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4560 - accuracy: 0.8166 - val_loss: 0.4281 - val_accuracy: 0.8366\n",
      "Epoch 156/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4876 - accuracy: 0.8017 - val_loss: 0.4274 - val_accuracy: 0.8366\n",
      "Epoch 157/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.4503 - accuracy: 0.8145 - val_loss: 0.4267 - val_accuracy: 0.8366\n",
      "Epoch 158/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4275 - accuracy: 0.8337 - val_loss: 0.4260 - val_accuracy: 0.8366\n",
      "Epoch 159/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.4504 - accuracy: 0.8124 - val_loss: 0.4255 - val_accuracy: 0.8366\n",
      "Epoch 160/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4577 - accuracy: 0.7996 - val_loss: 0.4250 - val_accuracy: 0.8366\n",
      "Epoch 161/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.4323 - accuracy: 0.8422 - val_loss: 0.4245 - val_accuracy: 0.8317\n",
      "Epoch 162/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.4601 - accuracy: 0.8102 - val_loss: 0.4238 - val_accuracy: 0.8317\n",
      "Epoch 163/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4973 - accuracy: 0.7910 - val_loss: 0.4230 - val_accuracy: 0.8317\n",
      "Epoch 164/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4557 - accuracy: 0.8145 - val_loss: 0.4225 - val_accuracy: 0.8317\n",
      "Epoch 165/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.4600 - accuracy: 0.8081 - val_loss: 0.4220 - val_accuracy: 0.8317\n",
      "Epoch 166/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4595 - accuracy: 0.7996 - val_loss: 0.4215 - val_accuracy: 0.8317\n",
      "Epoch 167/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.4447 - accuracy: 0.8380 - val_loss: 0.4214 - val_accuracy: 0.8317\n",
      "Epoch 168/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4680 - accuracy: 0.8209 - val_loss: 0.4211 - val_accuracy: 0.8317\n",
      "Epoch 169/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4396 - accuracy: 0.8188 - val_loss: 0.4206 - val_accuracy: 0.8317\n",
      "Epoch 170/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4521 - accuracy: 0.8166 - val_loss: 0.4199 - val_accuracy: 0.8317\n",
      "Epoch 171/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4400 - accuracy: 0.8550 - val_loss: 0.4192 - val_accuracy: 0.8317\n",
      "Epoch 172/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4300 - accuracy: 0.8230 - val_loss: 0.4186 - val_accuracy: 0.8366\n",
      "Epoch 173/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4178 - accuracy: 0.8465 - val_loss: 0.4183 - val_accuracy: 0.8317\n",
      "Epoch 174/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4515 - accuracy: 0.8230 - val_loss: 0.4179 - val_accuracy: 0.8366\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 175/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.4229 - accuracy: 0.8230 - val_loss: 0.4174 - val_accuracy: 0.8366\n",
      "Epoch 176/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.4204 - accuracy: 0.8465 - val_loss: 0.4171 - val_accuracy: 0.8366\n",
      "Epoch 177/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.4280 - accuracy: 0.8273 - val_loss: 0.4168 - val_accuracy: 0.8366\n",
      "Epoch 178/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4380 - accuracy: 0.8529 - val_loss: 0.4167 - val_accuracy: 0.8317\n",
      "Epoch 179/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4051 - accuracy: 0.8422 - val_loss: 0.4163 - val_accuracy: 0.8317\n",
      "Epoch 180/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.4626 - accuracy: 0.7996 - val_loss: 0.4158 - val_accuracy: 0.8317\n",
      "Epoch 181/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4399 - accuracy: 0.8252 - val_loss: 0.4155 - val_accuracy: 0.8317\n",
      "Epoch 182/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4558 - accuracy: 0.8102 - val_loss: 0.4150 - val_accuracy: 0.8317\n",
      "Epoch 183/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.3830 - accuracy: 0.8593 - val_loss: 0.4145 - val_accuracy: 0.8366\n",
      "Epoch 184/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.4304 - accuracy: 0.8401 - val_loss: 0.4138 - val_accuracy: 0.8366\n",
      "Epoch 185/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4064 - accuracy: 0.8529 - val_loss: 0.4133 - val_accuracy: 0.8366\n",
      "Epoch 186/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4255 - accuracy: 0.8593 - val_loss: 0.4133 - val_accuracy: 0.8317\n",
      "Epoch 187/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4074 - accuracy: 0.8422 - val_loss: 0.4129 - val_accuracy: 0.8317\n",
      "Epoch 188/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.4188 - accuracy: 0.8337 - val_loss: 0.4121 - val_accuracy: 0.8317\n",
      "Epoch 189/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4212 - accuracy: 0.8294 - val_loss: 0.4110 - val_accuracy: 0.8317\n",
      "Epoch 190/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4028 - accuracy: 0.8316 - val_loss: 0.4100 - val_accuracy: 0.8317\n",
      "Epoch 191/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4303 - accuracy: 0.8401 - val_loss: 0.4093 - val_accuracy: 0.8267\n",
      "Epoch 192/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4175 - accuracy: 0.8422 - val_loss: 0.4089 - val_accuracy: 0.8267\n",
      "Epoch 193/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4253 - accuracy: 0.8422 - val_loss: 0.4083 - val_accuracy: 0.8267\n",
      "Epoch 194/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4048 - accuracy: 0.8422 - val_loss: 0.4077 - val_accuracy: 0.8267\n",
      "Epoch 195/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.4003 - accuracy: 0.8443 - val_loss: 0.4075 - val_accuracy: 0.8267\n",
      "Epoch 196/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.3979 - accuracy: 0.8593 - val_loss: 0.4078 - val_accuracy: 0.8218\n",
      "Epoch 197/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.3892 - accuracy: 0.8358 - val_loss: 0.4083 - val_accuracy: 0.8267\n",
      "Epoch 198/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.4021 - accuracy: 0.8507 - val_loss: 0.4088 - val_accuracy: 0.8267\n",
      "Epoch 199/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4184 - accuracy: 0.8316 - val_loss: 0.4093 - val_accuracy: 0.8267\n",
      "Epoch 200/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.3919 - accuracy: 0.8550 - val_loss: 0.4099 - val_accuracy: 0.8267\n",
      "Epoch 201/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.4357 - accuracy: 0.8273 - val_loss: 0.4108 - val_accuracy: 0.8267\n",
      "Epoch 202/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4039 - accuracy: 0.8486 - val_loss: 0.4112 - val_accuracy: 0.8267\n",
      "Epoch 203/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.4008 - accuracy: 0.8380 - val_loss: 0.4109 - val_accuracy: 0.8267\n",
      "Epoch 204/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.3762 - accuracy: 0.8635 - val_loss: 0.4106 - val_accuracy: 0.8267\n",
      "Epoch 205/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.3865 - accuracy: 0.8507 - val_loss: 0.4101 - val_accuracy: 0.8267\n",
      "Epoch 206/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4300 - accuracy: 0.8486 - val_loss: 0.4098 - val_accuracy: 0.8267\n",
      "Epoch 207/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4196 - accuracy: 0.8316 - val_loss: 0.4094 - val_accuracy: 0.8267\n",
      "Epoch 208/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4058 - accuracy: 0.8443 - val_loss: 0.4089 - val_accuracy: 0.8267\n",
      "Epoch 209/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4187 - accuracy: 0.8337 - val_loss: 0.4081 - val_accuracy: 0.8267\n",
      "Epoch 210/300\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.4114 - accuracy: 0.8443 - val_loss: 0.4076 - val_accuracy: 0.8267\n",
      "Epoch 211/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4458 - accuracy: 0.8422 - val_loss: 0.4075 - val_accuracy: 0.8267\n",
      "Epoch 212/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4123 - accuracy: 0.8507 - val_loss: 0.4074 - val_accuracy: 0.8267\n",
      "Epoch 213/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4183 - accuracy: 0.8380 - val_loss: 0.4071 - val_accuracy: 0.8267\n",
      "Epoch 214/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.3830 - accuracy: 0.8486 - val_loss: 0.4066 - val_accuracy: 0.8267\n",
      "Epoch 215/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.3616 - accuracy: 0.8742 - val_loss: 0.4066 - val_accuracy: 0.8317\n",
      "Epoch 216/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4256 - accuracy: 0.8102 - val_loss: 0.4067 - val_accuracy: 0.8317\n",
      "Epoch 217/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4350 - accuracy: 0.8443 - val_loss: 0.4069 - val_accuracy: 0.8317\n",
      "Epoch 218/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.4038 - accuracy: 0.8486 - val_loss: 0.4073 - val_accuracy: 0.8317\n",
      "Epoch 219/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.4109 - accuracy: 0.8230 - val_loss: 0.4075 - val_accuracy: 0.8317\n",
      "Epoch 220/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.3966 - accuracy: 0.8422 - val_loss: 0.4076 - val_accuracy: 0.8317\n",
      "Epoch 221/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.3814 - accuracy: 0.8401 - val_loss: 0.4074 - val_accuracy: 0.8317\n",
      "Epoch 222/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.3636 - accuracy: 0.8934 - val_loss: 0.4071 - val_accuracy: 0.8317\n",
      "Epoch 223/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.3863 - accuracy: 0.8486 - val_loss: 0.4070 - val_accuracy: 0.8317\n",
      "Epoch 224/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.3823 - accuracy: 0.8507 - val_loss: 0.4072 - val_accuracy: 0.8317\n",
      "Epoch 225/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.3497 - accuracy: 0.8593 - val_loss: 0.4073 - val_accuracy: 0.8218\n",
      "Epoch 226/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.3534 - accuracy: 0.8614 - val_loss: 0.4070 - val_accuracy: 0.8218\n",
      "Epoch 227/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.3989 - accuracy: 0.8443 - val_loss: 0.4067 - val_accuracy: 0.8218\n",
      "Epoch 228/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.3954 - accuracy: 0.8529 - val_loss: 0.4066 - val_accuracy: 0.8267\n",
      "Epoch 229/300\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.4013 - accuracy: 0.8507 - val_loss: 0.4068 - val_accuracy: 0.8267\n",
      "Epoch 230/300\n",
      "2/2 [==============================] - 0s 16ms/step - loss: 0.3882 - accuracy: 0.8443 - val_loss: 0.4075 - val_accuracy: 0.8218\n",
      "Epoch 231/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.3524 - accuracy: 0.8571 - val_loss: 0.4083 - val_accuracy: 0.8218\n",
      "Epoch 232/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.3822 - accuracy: 0.8614 - val_loss: 0.4094 - val_accuracy: 0.8218\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 233/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.3553 - accuracy: 0.8657 - val_loss: 0.4099 - val_accuracy: 0.8218\n",
      "Epoch 234/300\n",
      "2/2 [==============================] - 0s 16ms/step - loss: 0.3990 - accuracy: 0.8593 - val_loss: 0.4099 - val_accuracy: 0.8168\n",
      "Epoch 235/300\n",
      "2/2 [==============================] - 0s 14ms/step - loss: 0.3569 - accuracy: 0.8593 - val_loss: 0.4098 - val_accuracy: 0.8168\n",
      "Epoch 236/300\n",
      "2/2 [==============================] - 0s 15ms/step - loss: 0.3580 - accuracy: 0.8721 - val_loss: 0.4095 - val_accuracy: 0.8168\n",
      "Epoch 237/300\n",
      "2/2 [==============================] - 0s 14ms/step - loss: 0.3783 - accuracy: 0.8486 - val_loss: 0.4091 - val_accuracy: 0.8168\n",
      "Epoch 238/300\n",
      "2/2 [==============================] - 0s 13ms/step - loss: 0.4146 - accuracy: 0.8550 - val_loss: 0.4095 - val_accuracy: 0.8168\n",
      "Epoch 239/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.3657 - accuracy: 0.8657 - val_loss: 0.4096 - val_accuracy: 0.8218\n",
      "Epoch 240/300\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 0.3626 - accuracy: 0.8614 - val_loss: 0.4098 - val_accuracy: 0.8168\n",
      "Epoch 240: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2bb623be0>"
      ]
     },
     "execution_count": 715,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train,batch_size=256,epochs=300,\n",
    "          verbose=1,validation_data=(X_test,y_test),callbacks = [early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 716,
   "id": "aabe5c34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 716,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_loss = pd.DataFrame(model.history.history).plot()\n",
    "model_loss.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 717,
   "id": "6272e555",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 601us/step\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "predictions = predictions.reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 718,
   "id": "86d01dbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(predictions)):\n",
    "    if predictions[i] > 0.5:\n",
    "        predictions[i] = 1\n",
    "    else:\n",
    "        predictions[i] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 719,
   "id": "269a2e92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.82\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Predicted  0.0  1.0\n",
       " Actual             \n",
       " 0          103   14\n",
       " 1           23   62,\n",
       " 0.8168316831683168)"
      ]
     },
     "execution_count": 719,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_classes(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 728,
   "id": "794564de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/15 [==============================] - 0s 558us/step - loss: 0.2615 - accuracy: 0.9168\n",
      "score on train: 0.916844367980957\n",
      "7/7 [==============================] - 0s 487us/step - loss: 0.4098 - accuracy: 0.8168\n",
      "score on test: 0.8168317079544067\n"
     ]
    }
   ],
   "source": [
    "print(\"score on train:\", model.evaluate(X_train,y_train)[1])\n",
    "print(\"score on test:\", model.evaluate(X_test,y_test)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 740,
   "id": "97e96507",
   "metadata": {},
   "outputs": [],
   "source": [
    "classification_model = ['Random Forest','KNN','Naive-Bayes','SVM','Neural Network']\n",
    "column = ['Algorithm','Train','Test']\n",
    "classification_rows = [['Random Forest',0.9133960440622495,0.865891132572432],\n",
    "                      ['KNN',0.8226530152768168,0.8331650570676032],\n",
    "                      ['Naive-Bayes',0.6749461851888461,0.6766022827041265],\n",
    "                       ['SVM',0.8837567131231261,0.8733757682177348],\n",
    "                       ['Neural Network',0.916844367980957,0.8168317079544067]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 744,
   "id": "a7848bb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_result = pd.DataFrame(classification_rows,columns=column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 746,
   "id": "def7550d",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_result['OF'] = final_result['Train'] - final_result['Test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 751,
   "id": "70a872da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Train</th>\n",
       "      <th>Test</th>\n",
       "      <th>OF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.883757</td>\n",
       "      <td>0.873376</td>\n",
       "      <td>0.010381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.913396</td>\n",
       "      <td>0.865891</td>\n",
       "      <td>0.047505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.822653</td>\n",
       "      <td>0.833165</td>\n",
       "      <td>-0.010512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Neural Network</td>\n",
       "      <td>0.916844</td>\n",
       "      <td>0.816832</td>\n",
       "      <td>0.100013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Naive-Bayes</td>\n",
       "      <td>0.674946</td>\n",
       "      <td>0.676602</td>\n",
       "      <td>-0.001656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Algorithm     Train      Test        OF\n",
       "3             SVM  0.883757  0.873376  0.010381\n",
       "0   Random Forest  0.913396  0.865891  0.047505\n",
       "1             KNN  0.822653  0.833165 -0.010512\n",
       "4  Neural Network  0.916844  0.816832  0.100013\n",
       "2     Naive-Bayes  0.674946  0.676602 -0.001656"
      ]
     },
     "execution_count": 751,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_result.sort_values('Test',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ac974d9",
   "metadata": {},
   "source": [
    "## Conclusion: Linear SVM performs best with about 87% accuracy without much overfitting."
   ]
  }
 ],
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