SPT_predictions_based_on_pre_trained_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, recall_score, confusion_matrix, f1_score, precision_score\n",
    "import seaborn as sn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"Datasets/SPT Dataset - Idriss Boulanger.xlsx\")\n",
    "df['ln(CSR7.5)'] = np.log(df['CSR7.5'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>M</th>\n",
       "      <th>amax</th>\n",
       "      <th>Liq</th>\n",
       "      <th>Avg Depth</th>\n",
       "      <th>Depth GWT</th>\n",
       "      <th>Vv</th>\n",
       "      <th>V'v</th>\n",
       "      <th>avg Nm</th>\n",
       "      <th>(N1)60</th>\n",
       "      <th>CB</th>\n",
       "      <th>...</th>\n",
       "      <th>CR</th>\n",
       "      <th>CS</th>\n",
       "      <th>FC</th>\n",
       "      <th>(N1)60cs</th>\n",
       "      <th>rd</th>\n",
       "      <th>K</th>\n",
       "      <th>MSF</th>\n",
       "      <th>CSR</th>\n",
       "      <th>CSR7.5</th>\n",
       "      <th>ln(CSR7.5)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.1</td>\n",
       "      <td>98</td>\n",
       "      <td>68</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.207</td>\n",
       "      <td>-1.575036</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>80</td>\n",
       "      <td>61</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30</td>\n",
       "      <td>8.7</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.159</td>\n",
       "      <td>-1.838851</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>69</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.245</td>\n",
       "      <td>-1.406497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>75</td>\n",
       "      <td>48</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.318</td>\n",
       "      <td>-1.145704</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.7</td>\n",
       "      <td>141</td>\n",
       "      <td>104</td>\n",
       "      <td>17.3</td>\n",
       "      <td>21.1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.251</td>\n",
       "      <td>-1.382302</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>6.9</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>179</td>\n",
       "      <td>135</td>\n",
       "      <td>30.9</td>\n",
       "      <td>34.6</td>\n",
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       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20</td>\n",
       "      <td>39.1</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.278</td>\n",
       "      <td>-1.280134</td>\n",
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       "      <th>226</th>\n",
       "      <td>6.9</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>192</td>\n",
       "      <td>123</td>\n",
       "      <td>9.7</td>\n",
       "      <td>10.8</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20</td>\n",
       "      <td>15.3</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.258</td>\n",
       "      <td>-1.354796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>6.9</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>141</td>\n",
       "      <td>107</td>\n",
       "      <td>14.8</td>\n",
       "      <td>16.8</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>21.9</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.267</td>\n",
       "      <td>-1.320507</td>\n",
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       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>6.9</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1</td>\n",
       "      <td>11.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>219</td>\n",
       "      <td>146</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0.246</td>\n",
       "      <td>-1.402424</td>\n",
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       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>6.9</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93</td>\n",
       "      <td>46</td>\n",
       "      <td>8.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.308</td>\n",
       "      <td>0.240</td>\n",
       "      <td>-1.427116</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>230 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       M  amax  Liq  Avg Depth  Depth GWT   Vv  V'v  avg Nm  (N1)60  CB  ...  \\\n",
       "0    8.1  0.20    1        5.2        2.1   98   68     5.9     8.2   1  ...   \n",
       "1    8.1  0.20    1        4.3        2.4   80   61     2.3     3.4   1  ...   \n",
       "2    8.1  0.20    1        3.7        2.1   69   39     1.0     1.7   1  ...   \n",
       "3    7.0  0.40    1        4.0        1.2   75   48     8.0    11.8   1  ...   \n",
       "4    7.0  0.35    1        7.5        3.7  141  104    17.3    21.1   1  ...   \n",
       "..   ...   ...  ...        ...        ...  ...  ...     ...     ...  ..  ...   \n",
       "225  6.9  0.40    0        9.5        5.0  179  135    30.9    34.6   1  ...   \n",
       "226  6.9  0.34    1       10.0        3.0  192  123     9.7    10.8   1  ...   \n",
       "227  6.9  0.40    1        7.5        4.0  141  107    14.8    16.8   1  ...   \n",
       "228  6.9  0.34    1       11.5        4.0  219  146    12.0    12.3   1  ...   \n",
       "229  6.9  0.25    1        4.7        0.0   93   46     8.5    14.0   1  ...   \n",
       "\n",
       "       CR   CS  FC  (N1)60cs    rd     K   MSF    CSR  CSR7.5  ln(CSR7.5)  \n",
       "0    0.95  1.0  10       9.3  0.98  1.04  0.85  0.182   0.207   -1.575036  \n",
       "1    0.95  1.0  30       8.7  0.99  1.07  0.85  0.144   0.159   -1.838851  \n",
       "2    0.85  1.0  27       6.9  0.99  1.08  0.85  0.225   0.245   -1.406497  \n",
       "3    0.85  1.0   0      11.8  0.96  1.07  1.14  0.390   0.318   -1.145704  \n",
       "4    0.95  1.0   4      21.1  0.90  0.99  1.14  0.283   0.251   -1.382302  \n",
       "..    ...  ...  ..       ...   ...   ...   ...    ...     ...         ...  \n",
       "225  1.00  1.0  20      39.1  0.87  0.92  1.17  0.299   0.278   -1.280134  \n",
       "226  1.00  1.0  20      15.3  0.86  0.98  1.17  0.295   0.258   -1.354796  \n",
       "227  0.95  1.0  25      21.9  0.90  0.99  1.17  0.310   0.267   -1.320507  \n",
       "228  1.00  1.0  20      16.8  0.83  0.96  1.17  0.275   0.246   -1.402424  \n",
       "229  0.95  1.0  20      18.5  0.95  1.10  1.17  0.308   0.240   -1.427116  \n",
       "\n",
       "[230 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    118\n",
       "0    112\n",
       "Name: Liq, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Liq.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>(N1)60cs</th>\n",
       "      <th>CSR7.5</th>\n",
       "      <th>Liq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>230.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>230.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18.109130</td>\n",
       "      <td>0.224204</td>\n",
       "      <td>0.513043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.488049</td>\n",
       "      <td>0.103926</td>\n",
       "      <td>0.500920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.700000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.325000</td>\n",
       "      <td>0.159500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15.300000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22.575000</td>\n",
       "      <td>0.277500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>63.700000</td>\n",
       "      <td>0.514000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         (N1)60cs      CSR7.5         Liq\n",
       "count  230.000000  230.000000  230.000000\n",
       "mean    18.109130    0.224204    0.513043\n",
       "std     10.488049    0.103926    0.500920\n",
       "min      4.700000    0.040000    0.000000\n",
       "25%     10.325000    0.159500    0.000000\n",
       "50%     15.300000    0.210000    1.000000\n",
       "75%     22.575000    0.277500    1.000000\n",
       "max     63.700000    0.514000    1.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"(N1)60cs\",\"CSR7.5\",\"Liq\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='(N1)60cs', ylabel='CSR7.5'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.scatterplot(x = df['(N1)60cs'], y = df['CSR7.5'], hue = df['Liq'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>(N1)60cs</th>\n",
       "      <th>CSR7.5</th>\n",
       "      <th>Liq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>13.3</td>\n",
       "      <td>0.381</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>5.4</td>\n",
       "      <td>0.231</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>18.7</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>15.3</td>\n",
       "      <td>0.177</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     (N1)60cs  CSR7.5  Liq\n",
       "104      13.3   0.381    1\n",
       "122       5.4   0.231    1\n",
       "105      18.7   0.159    0\n",
       "160      15.3   0.177    1\n",
       "202      42.0   0.441    0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['(N1)60cs', 'CSR7.5', 'Liq']].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    118\n",
       "0    112\n",
       "Name: Liq, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Liq.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='(N1)60cs', ylabel='CSR7.5'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.scatterplot(x = df['(N1)60cs'], y = df['CSR7.5'], hue = df['Liq'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>(N1)60cs</th>\n",
       "      <th>CSR7.5</th>\n",
       "      <th>Liq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>230.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>230.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18.109130</td>\n",
       "      <td>0.224204</td>\n",
       "      <td>0.513043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.488049</td>\n",
       "      <td>0.103926</td>\n",
       "      <td>0.500920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.700000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.325000</td>\n",
       "      <td>0.159500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15.300000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22.575000</td>\n",
       "      <td>0.277500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>63.700000</td>\n",
       "      <td>0.514000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         (N1)60cs      CSR7.5         Liq\n",
       "count  230.000000  230.000000  230.000000\n",
       "mean    18.109130    0.224204    0.513043\n",
       "std     10.488049    0.103926    0.500920\n",
       "min      4.700000    0.040000    0.000000\n",
       "25%     10.325000    0.159500    0.000000\n",
       "50%     15.300000    0.210000    1.000000\n",
       "75%     22.575000    0.277500    1.000000\n",
       "max     63.700000    0.514000    1.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['(N1)60cs', 'CSR7.5', 'Liq']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Pre-trained Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MinMaxNormalization(data):\n",
    "    # MinMax Normalization\n",
    "    scaler = MinMaxScaler()\n",
    "    # fit scaler on data\n",
    "    scaler.fit(data)\n",
    "    # apply transform\n",
    "    minmax_normalized = scaler.transform(data)\n",
    "    return minmax_normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = df[['(N1)60cs', 'CSR7.5']]\n",
    "target = df['Liq']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalized_features = MinMaxNormalization(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(normalized_features, target, test_size = 0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "2/2 [==============================] - 1s 4ms/step - loss: 0.8053 - accuracy: 0.5870\n",
      "Epoch 2/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.7967 - accuracy: 0.5870\n",
      "Epoch 3/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 0.7795 - accuracy: 0.5870\n",
      "Epoch 4/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.7595 - accuracy: 0.5870\n",
      "Epoch 5/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.7327 - accuracy: 0.6087\n",
      "Epoch 6/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.7062 - accuracy: 0.6087\n",
      "Epoch 7/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.6819 - accuracy: 0.6087\n",
      "Epoch 8/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.6527 - accuracy: 0.6522\n",
      "Epoch 9/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.6275 - accuracy: 0.6957\n",
      "Epoch 10/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.6040 - accuracy: 0.6957\n",
      "Epoch 11/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.5818 - accuracy: 0.7391\n",
      "Epoch 12/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.5611 - accuracy: 0.7391\n",
      "Epoch 13/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.5404 - accuracy: 0.7826\n",
      "Epoch 14/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.5239 - accuracy: 0.7609\n",
      "Epoch 15/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.5086 - accuracy: 0.7826\n",
      "Epoch 16/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4977 - accuracy: 0.7826\n",
      "Epoch 17/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4880 - accuracy: 0.8043\n",
      "Epoch 18/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4789 - accuracy: 0.8043\n",
      "Epoch 19/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4724 - accuracy: 0.8043\n",
      "Epoch 20/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4684 - accuracy: 0.8043\n",
      "Epoch 21/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4633 - accuracy: 0.8043\n",
      "Epoch 22/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4624 - accuracy: 0.8043\n",
      "Epoch 23/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4585 - accuracy: 0.8043\n",
      "Epoch 24/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4569 - accuracy: 0.8043\n",
      "Epoch 25/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 0.4550 - accuracy: 0.8043\n",
      "Epoch 26/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4543 - accuracy: 0.7826\n",
      "Epoch 27/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4536 - accuracy: 0.7826\n",
      "Epoch 28/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4527 - accuracy: 0.8043\n",
      "Epoch 29/500\n",
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      "2/2 [==============================] - 0s 3ms/step - loss: 0.4243 - accuracy: 0.8043\n",
      "Epoch 380/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4242 - accuracy: 0.8043\n",
      "Epoch 381/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4241 - accuracy: 0.8043\n",
      "Epoch 382/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4240 - accuracy: 0.8043\n",
      "Epoch 383/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 0.4240 - accuracy: 0.8043\n",
      "Epoch 384/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4239 - accuracy: 0.8043\n",
      "Epoch 385/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4239 - accuracy: 0.8043\n",
      "Epoch 386/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4237 - accuracy: 0.8043\n",
      "Epoch 387/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4237 - accuracy: 0.8043\n",
      "Epoch 388/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4235 - accuracy: 0.8043\n",
      "Epoch 389/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4234 - accuracy: 0.8043\n",
      "Epoch 390/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4234 - accuracy: 0.8043\n",
      "Epoch 391/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4233 - accuracy: 0.8043\n",
      "Epoch 392/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4232 - accuracy: 0.8043\n",
      "Epoch 393/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4232 - accuracy: 0.8043\n",
      "Epoch 394/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4231 - accuracy: 0.8043\n",
      "Epoch 395/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4230 - accuracy: 0.8043\n",
      "Epoch 396/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4229 - accuracy: 0.8043\n",
      "Epoch 397/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4228 - accuracy: 0.8043\n",
      "Epoch 398/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4228 - accuracy: 0.8043\n",
      "Epoch 399/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4227 - accuracy: 0.8043\n",
      "Epoch 400/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4225 - accuracy: 0.8043\n",
      "Epoch 401/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4225 - accuracy: 0.8043\n",
      "Epoch 402/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4224 - accuracy: 0.8043\n",
      "Epoch 403/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4223 - accuracy: 0.8043\n",
      "Epoch 404/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4221 - accuracy: 0.8043\n",
      "Epoch 405/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4222 - accuracy: 0.8043\n",
      "Epoch 406/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4222 - accuracy: 0.8043\n",
      "Epoch 407/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4221 - accuracy: 0.8043\n",
      "Epoch 408/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4221 - accuracy: 0.8043\n",
      "Epoch 409/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4220 - accuracy: 0.8043\n",
      "Epoch 410/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4219 - accuracy: 0.8043\n",
      "Epoch 411/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4218 - accuracy: 0.8043\n",
      "Epoch 412/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4217 - accuracy: 0.8043\n",
      "Epoch 413/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4215 - accuracy: 0.8043\n",
      "Epoch 414/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4214 - accuracy: 0.8043\n",
      "Epoch 415/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4213 - accuracy: 0.8043\n",
      "Epoch 416/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4213 - accuracy: 0.8043\n",
      "Epoch 417/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4212 - accuracy: 0.8043\n",
      "Epoch 418/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4211 - accuracy: 0.8043\n",
      "Epoch 419/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4210 - accuracy: 0.8043\n",
      "Epoch 420/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4210 - accuracy: 0.8043\n",
      "Epoch 421/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4209 - accuracy: 0.8043\n",
      "Epoch 422/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4208 - accuracy: 0.8043\n",
      "Epoch 423/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4208 - accuracy: 0.8043\n",
      "Epoch 424/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4207 - accuracy: 0.8043\n",
      "Epoch 425/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4205 - accuracy: 0.8043\n",
      "Epoch 426/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4205 - accuracy: 0.8043\n",
      "Epoch 427/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4205 - accuracy: 0.8043\n",
      "Epoch 428/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4204 - accuracy: 0.8043\n",
      "Epoch 429/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4203 - accuracy: 0.8043\n",
      "Epoch 430/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4202 - accuracy: 0.8043\n",
      "Epoch 431/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4202 - accuracy: 0.8043\n",
      "Epoch 432/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4202 - accuracy: 0.8043\n",
      "Epoch 433/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4204 - accuracy: 0.8043\n",
      "Epoch 434/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4201 - accuracy: 0.8043\n",
      "Epoch 435/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4200 - accuracy: 0.8043\n",
      "Epoch 436/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4199 - accuracy: 0.8043\n",
      "Epoch 437/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4198 - accuracy: 0.8043\n",
      "Epoch 438/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4198 - accuracy: 0.8043\n",
      "Epoch 439/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 0.4198 - accuracy: 0.8043\n",
      "Epoch 440/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4196 - accuracy: 0.8043\n",
      "Epoch 441/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4195 - accuracy: 0.8043\n",
      "Epoch 442/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4195 - accuracy: 0.8043\n",
      "Epoch 443/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4193 - accuracy: 0.8043\n",
      "Epoch 444/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4195 - accuracy: 0.8043\n",
      "Epoch 445/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4192 - accuracy: 0.8043\n",
      "Epoch 446/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4191 - accuracy: 0.8043\n",
      "Epoch 447/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4190 - accuracy: 0.8043\n",
      "Epoch 448/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4189 - accuracy: 0.8043\n",
      "Epoch 449/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4188 - accuracy: 0.8043\n",
      "Epoch 450/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4188 - accuracy: 0.8043\n",
      "Epoch 451/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4187 - accuracy: 0.8043\n",
      "Epoch 452/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4186 - accuracy: 0.8043\n",
      "Epoch 453/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4187 - accuracy: 0.8043\n",
      "Epoch 454/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4185 - accuracy: 0.8043\n",
      "Epoch 455/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4184 - accuracy: 0.8043\n",
      "Epoch 456/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4183 - accuracy: 0.8043\n",
      "Epoch 457/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4184 - accuracy: 0.8043\n",
      "Epoch 458/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4182 - accuracy: 0.8043\n",
      "Epoch 459/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4181 - accuracy: 0.8043\n",
      "Epoch 460/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 0.4182 - accuracy: 0.8043\n",
      "Epoch 461/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4181 - accuracy: 0.8043\n",
      "Epoch 462/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4180 - accuracy: 0.8043\n",
      "Epoch 463/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4180 - accuracy: 0.8043\n",
      "Epoch 464/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4180 - accuracy: 0.8261\n",
      "Epoch 465/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4179 - accuracy: 0.8261\n",
      "Epoch 466/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4179 - accuracy: 0.8261\n",
      "Epoch 467/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4179 - accuracy: 0.8261\n",
      "Epoch 468/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4177 - accuracy: 0.8261\n",
      "Epoch 469/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4178 - accuracy: 0.8261\n",
      "Epoch 470/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4175 - accuracy: 0.8261\n",
      "Epoch 471/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4174 - accuracy: 0.8261\n",
      "Epoch 472/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4174 - accuracy: 0.8261\n",
      "Epoch 473/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4173 - accuracy: 0.8261\n",
      "Epoch 474/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4172 - accuracy: 0.8261\n",
      "Epoch 475/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4171 - accuracy: 0.8261\n",
      "Epoch 476/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4171 - accuracy: 0.8261\n",
      "Epoch 477/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4170 - accuracy: 0.8261\n",
      "Epoch 478/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4170 - accuracy: 0.8261\n",
      "Epoch 479/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4170 - accuracy: 0.8043\n",
      "Epoch 480/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4169 - accuracy: 0.8043\n",
      "Epoch 481/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4168 - accuracy: 0.8043\n",
      "Epoch 482/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4167 - accuracy: 0.8043\n",
      "Epoch 483/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4170 - accuracy: 0.8043\n",
      "Epoch 484/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4167 - accuracy: 0.8043\n",
      "Epoch 485/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4166 - accuracy: 0.8043\n",
      "Epoch 486/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4166 - accuracy: 0.8043\n",
      "Epoch 487/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4164 - accuracy: 0.8043\n",
      "Epoch 488/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4163 - accuracy: 0.8043\n",
      "Epoch 489/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4164 - accuracy: 0.8261\n",
      "Epoch 490/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4162 - accuracy: 0.8261\n",
      "Epoch 491/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4161 - accuracy: 0.8261\n",
      "Epoch 492/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4161 - accuracy: 0.8261\n",
      "Epoch 493/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 0.4160 - accuracy: 0.8261\n",
      "Epoch 494/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4160 - accuracy: 0.8261\n",
      "Epoch 495/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4159 - accuracy: 0.8261\n",
      "Epoch 496/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4159 - accuracy: 0.8261\n",
      "Epoch 497/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.4159 - accuracy: 0.8261\n",
      "Epoch 498/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4160 - accuracy: 0.8261\n",
      "Epoch 499/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4156 - accuracy: 0.8261\n",
      "Epoch 500/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4156 - accuracy: 0.8261\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1bde23b1df0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading the previously saved model.\n",
    "pre_trained_model = tf.keras.models.load_model('main_model.h5')\n",
    "#pre_trained_model.compile(loss='binary_crossentropy', optimizer='adam', metrics = ['accuracy'])\n",
    "pre_trained_model.fit(X_train, y_train, epochs = 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8/8 [==============================] - 0s 1ms/step - loss: 0.4283 - accuracy: 0.8217\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_accuracy = pre_trained_model.evaluate(normalized_features, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predictions = pre_trained_model.predict(normalized_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(y_predictions)):\n",
    "    if y_predictions[i] < 0.5:\n",
    "        y_predictions[i] = 0\n",
    "    else:\n",
    "        y_predictions[i] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCURACY =  0.8217391304347826\n",
      "RECALL =  0.8898305084745762\n",
      "F1-Score =  0.8366533864541832\n",
      "Precision =  0.7894736842105263\n"
     ]
    }
   ],
   "source": [
    "print(\"ACCURACY = \",accuracy_score(target, y_predictions))\n",
    "print(\"RECALL = \",recall_score(target, y_predictions))\n",
    "print(\"F1-Score = \",f1_score(target, y_predictions))\n",
    "print(\"Precision = \",precision_score(target, y_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.heatmap(confusion_matrix(target, y_predictions), annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.9105 - accuracy: 0.5435\n",
      "Epoch 2/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.8973 - accuracy: 0.5652\n",
      "Epoch 3/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.8725 - accuracy: 0.5652\n",
      "Epoch 4/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.8400 - accuracy: 0.5652\n",
      "Epoch 5/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.8011 - accuracy: 0.5652\n",
      "Epoch 6/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.7668 - accuracy: 0.5652\n",
      "Epoch 7/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.7217 - accuracy: 0.5870\n",
      "Epoch 8/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.6862 - accuracy: 0.5870\n",
      "Epoch 9/500\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.6461 - accuracy: 0.6087\n",
      "Epoch 10/500\n",
      "2/2 [==============================] - 0s 994us/step - loss: 0.6119 - accuracy: 0.6739\n",
      "Epoch 11/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.5788 - accuracy: 0.6957\n",
      "Epoch 12/500\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.5576 - accuracy: 0.6957\n",
      "Epoch 13/500\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.5298 - accuracy: 0.7174\n",
      "Epoch 14/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.5093 - accuracy: 0.7174\n",
      "Epoch 15/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4906 - accuracy: 0.6957\n",
      "Epoch 16/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4801 - accuracy: 0.7174\n",
      "Epoch 17/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4687 - accuracy: 0.7391\n",
      "Epoch 18/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4611 - accuracy: 0.7391\n",
      "Epoch 19/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4561 - accuracy: 0.7391\n",
      "Epoch 20/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4511 - accuracy: 0.7391\n",
      "Epoch 21/500\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.4469 - accuracy: 0.7174\n",
      "Epoch 22/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4451 - accuracy: 0.7391\n",
      "Epoch 23/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4430 - accuracy: 0.7391\n",
      "Epoch 24/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4396 - accuracy: 0.7609\n",
      "Epoch 25/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4381 - accuracy: 0.8043\n",
      "Epoch 26/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4364 - accuracy: 0.8043\n",
      "Epoch 27/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4345 - accuracy: 0.8261\n",
      "Epoch 28/500\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.4335 - accuracy: 0.8478\n",
      "Epoch 29/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4315 - accuracy: 0.8478\n",
      "Epoch 30/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4303 - accuracy: 0.8478\n",
      "Epoch 31/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4292 - accuracy: 0.8478\n",
      "Epoch 32/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4280 - accuracy: 0.8478\n",
      "Epoch 33/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4269 - accuracy: 0.8478\n",
      "Epoch 34/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4260 - accuracy: 0.8478\n",
      "Epoch 35/500\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.4253 - accuracy: 0.8478\n",
      "Epoch 36/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4250 - accuracy: 0.8478\n",
      "Epoch 37/500\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.4243 - accuracy: 0.8478\n",
      "Epoch 38/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4241 - accuracy: 0.8478\n",
      "Epoch 39/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4236 - accuracy: 0.8478\n",
      "Epoch 40/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4233 - accuracy: 0.8478\n",
      "Epoch 41/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4231 - accuracy: 0.8478\n",
      "Epoch 42/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4226 - accuracy: 0.8478\n",
      "Epoch 43/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4225 - accuracy: 0.8478\n",
      "Epoch 44/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4220 - accuracy: 0.8478\n",
      "Epoch 45/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4218 - accuracy: 0.8478\n",
      "Epoch 46/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4213 - accuracy: 0.8478\n",
      "Epoch 47/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4209 - accuracy: 0.8478\n",
      "Epoch 48/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4205 - accuracy: 0.8478\n",
      "Epoch 49/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4201 - accuracy: 0.8478\n",
      "Epoch 50/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4198 - accuracy: 0.8478\n",
      "Epoch 51/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4195 - accuracy: 0.8478\n",
      "Epoch 52/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4192 - accuracy: 0.8478\n",
      "Epoch 53/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4189 - accuracy: 0.8478\n",
      "Epoch 54/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4186 - accuracy: 0.8478\n",
      "Epoch 55/500\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.4184 - accuracy: 0.8478\n",
      "Epoch 56/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.4181 - accuracy: 0.8478\n",
      "Epoch 57/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4179 - accuracy: 0.8478\n",
      "Epoch 58/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4180 - accuracy: 0.8478\n",
      "Epoch 59/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4174 - accuracy: 0.8478\n",
      "Epoch 60/500\n",
      "2/2 [==============================] - 0s 992us/step - loss: 0.4172 - accuracy: 0.8478\n",
      "Epoch 61/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4169 - accuracy: 0.8478\n",
      "Epoch 62/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4167 - accuracy: 0.8478\n",
      "Epoch 63/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4162 - accuracy: 0.8478\n",
      "Epoch 64/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4158 - accuracy: 0.8478\n",
      "Epoch 65/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4154 - accuracy: 0.8478\n",
      "Epoch 66/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.4150 - accuracy: 0.8478\n",
      "Epoch 67/500\n",
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      "Epoch 388/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 2ms/step - loss: 0.3091 - accuracy: 0.8261\n",
      "Epoch 389/500\n",
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      "Epoch 470/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 2ms/step - loss: 0.3060 - accuracy: 0.8261\n",
      "Epoch 471/500\n",
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      "Epoch 477/500\n",
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      "Epoch 478/500\n",
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      "Epoch 479/500\n",
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      "Epoch 480/500\n",
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      "Epoch 481/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 0.3057 - accuracy: 0.8261\n",
      "Epoch 482/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.3056 - accuracy: 0.8261\n",
      "Epoch 483/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.3055 - accuracy: 0.8261\n",
      "Epoch 484/500\n",
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      "Epoch 485/500\n",
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      "Epoch 486/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.3055 - accuracy: 0.8261\n",
      "Epoch 487/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.3055 - accuracy: 0.8261\n",
      "Epoch 488/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.3055 - accuracy: 0.8261\n",
      "Epoch 489/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.3054 - accuracy: 0.8261\n",
      "Epoch 490/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.3053 - accuracy: 0.8261\n",
      "Epoch 491/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.3053 - accuracy: 0.8261\n",
      "Epoch 492/500\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.3053 - accuracy: 0.8261\n",
      "Epoch 493/500\n",
      "2/2 [==============================] - 0s 991us/step - loss: 0.3053 - accuracy: 0.8261\n",
      "Epoch 494/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.3052 - accuracy: 0.8261\n",
      "Epoch 495/500\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.3052 - accuracy: 0.8261\n",
      "Epoch 496/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 0.3052 - accuracy: 0.8261\n",
      "Epoch 497/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.3052 - accuracy: 0.8261\n",
      "Epoch 498/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.3052 - accuracy: 0.8261\n",
      "Epoch 499/500\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 0.3051 - accuracy: 0.8261\n",
      "Epoch 500/500\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 0.3051 - accuracy: 0.8261\n",
      "Accuracies: [0.8260869565217391, 0.8206521739130435, 0.8260869565217391, 0.7336956521739131, 0.782608695652174]\n",
      "Recalls: [0.7755102040816326, 0.7142857142857143, 0.75, 0.989010989010989, 0.9887640449438202]\n",
      "f1scores: [0.826086956521739, 0.809248554913295, 0.8181818181818182, 0.7860262008733625, 0.8148148148148149]\n",
      "precisions: [0.8837209302325582, 0.9333333333333333, 0.9, 0.6521739130434783, 0.6929133858267716]\n"
     ]
    }
   ],
   "source": [
    "#Cross Validation Score\n",
    "\n",
    "kf = KFold(n_splits = 5, shuffle = True)\n",
    "accuracies = []\n",
    "recalls = []\n",
    "f1scores = []\n",
    "precisions = []\n",
    "for train, test in kf.split(df[['(N1)60cs', 'CSR7.5','Liq']]):\n",
    "    train_df = df.loc[train]\n",
    "    test_df = df.loc[test]\n",
    "    \n",
    "    pre_trained_model = tf.keras.models.load_model('main_model.h5')\n",
    "    pre_trained_model.fit(MinMaxNormalization(test_df[['(N1)60cs', 'CSR7.5']]), test_df['Liq'], epochs = 500)\n",
    "\n",
    "    y_predictions = pre_trained_model.predict(MinMaxNormalization(train_df[['(N1)60cs', 'CSR7.5']]))\n",
    "    for i in range(len(y_predictions)):\n",
    "        if y_predictions[i] < 0.5:\n",
    "            y_predictions[i] = 0\n",
    "        else:\n",
    "            y_predictions[i] = 1\n",
    "\n",
    "    accuracies.append(accuracy_score(train_df['Liq'], y_predictions))\n",
    "    recalls.append(recall_score(train_df['Liq'], y_predictions))\n",
    "    f1scores.append(f1_score(train_df['Liq'], y_predictions))\n",
    "    precisions.append(precision_score(train_df['Liq'], y_predictions))\n",
    "\n",
    "\n",
    "    \n",
    "print(\"Accuracies:\", accuracies)\n",
    "print(\"Recalls:\", recalls)\n",
    "print(\"f1scores:\", f1scores)\n",
    "print(\"precisions:\", precisions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Juang 2002 SPT-based Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Assessing Probability-based Methods for Liquefaction Potential Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "PL_Juang = pd.DataFrame(1 / (1 + np.exp(-(10.1129 - (0.2572 * df['(N1)60cs']) + (3.4825 * df['ln(CSR7.5)']) ))), columns = ['PL'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(PL_Juang)):\n",
    "    if PL_Juang.loc[i,'PL'] > 0.5:\n",
    "        PL_Juang.loc[i,'Liq'] = 1\n",
    "    else:\n",
    "        PL_Juang.loc[i,'Liq'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>PL</th>\n",
       "      <th>Liq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>0.391251</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.169805</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>0.021639</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>0.948959</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>0.425310</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PL  Liq\n",
       "199  0.391251  0.0\n",
       "55   0.169805  0.0\n",
       "138  0.021639  0.0\n",
       "102  0.948959  1.0\n",
       "189  0.425310  0.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PL_Juang.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Juang's accuracy:  0.8521739130434782\n",
      "Juang's recall:  0.8559322033898306\n",
      "Juang's F1-score:  0.8559322033898306\n",
      "Juang's precision:  0.8559322033898306\n"
     ]
    }
   ],
   "source": [
    "print(\"Juang's accuracy: \", accuracy_score(target, PL_Juang['Liq']))\n",
    "print(\"Juang's recall: \", recall_score(target, PL_Juang['Liq']))\n",
    "print(\"Juang's F1-score: \", f1_score(target, PL_Juang['Liq']))\n",
    "print(\"Juang's precision: \", precision_score(target, PL_Juang['Liq']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.heatmap(confusion_matrix(target, PL_Juang['Liq']), annot = True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
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 "nbformat_minor": 4
}