data_1_and_data_2_combined.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset 2 - data_1 + data_2 Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import all necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "import statsmodels.api as sm\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the dataset into memory\n",
    "df = pd.read_csv('data_1.csv', header=0)\n",
    "#Dropping rows with null values\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the dataset into memory\n",
    "df_2 = pd.read_csv('data_2.csv', header=0)\n",
    "#Dropping rows with null values\n",
    "df_2 = df_2.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>baseline value</th>\n",
       "      <th>accelerations</th>\n",
       "      <th>fetal_movement</th>\n",
       "      <th>uterine_contractions</th>\n",
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       "      <th>histogram_median</th>\n",
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       "      <th>histogram_tendency</th>\n",
       "      <th>fetal_health</th>\n",
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      "text/plain": [
       "      baseline value  accelerations  fetal_movement  uterine_contractions  \\\n",
       "0                132          0.006           0.000                 0.006   \n",
       "1                133          0.003           0.000                 0.008   \n",
       "2                134          0.003           0.000                 0.008   \n",
       "3                132          0.007           0.000                 0.008   \n",
       "4                131          0.005           0.072                 0.008   \n",
       "...              ...            ...             ...                   ...   \n",
       "1445             150          0.000           0.000                 0.001   \n",
       "1446             150          0.000           0.003                 0.000   \n",
       "1447             150          0.000           0.001                 0.000   \n",
       "1448             150          0.000           0.002                 0.000   \n",
       "1449             148          0.000           0.002                 0.005   \n",
       "\n",
       "      light_decelerations  severe_decelerations  prolongued_decelerations  \\\n",
       "0                   0.003                   0.0                       0.0   \n",
       "1                   0.003                   0.0                       0.0   \n",
       "2                   0.003                   0.0                       0.0   \n",
       "3                   0.000                   0.0                       0.0   \n",
       "4                   0.003                   0.0                       0.0   \n",
       "...                   ...                   ...                       ...   \n",
       "1445                0.000                   0.0                       0.0   \n",
       "1446                0.000                   0.0                       0.0   \n",
       "1447                0.000                   0.0                       0.0   \n",
       "1448                0.000                   0.0                       0.0   \n",
       "1449                0.000                   0.0                       0.0   \n",
       "\n",
       "      abnormal_short_term_variability  mean_value_of_short_term_variability  \\\n",
       "0                                  17                                   2.1   \n",
       "1                                  16                                   2.1   \n",
       "2                                  16                                   2.4   \n",
       "3                                  16                                   2.4   \n",
       "4                                  28                                   1.4   \n",
       "...                               ...                                   ...   \n",
       "1445                               81                                   0.4   \n",
       "1446                               80                                   0.4   \n",
       "1447                               84                                   0.3   \n",
       "1448                               84                                   0.3   \n",
       "1449                               82                                   0.4   \n",
       "\n",
       "      percentage_of_time_with_abnormal_long_term_variability  ...  \\\n",
       "0                                                     0       ...   \n",
       "1                                                     0       ...   \n",
       "2                                                     0       ...   \n",
       "3                                                     0       ...   \n",
       "4                                                     0       ...   \n",
       "...                                                 ...       ...   \n",
       "1445                                                 17       ...   \n",
       "1446                                                 24       ...   \n",
       "1447                                                 32       ...   \n",
       "1448                                                 37       ...   \n",
       "1449                                                  5       ...   \n",
       "\n",
       "      histogram_min  histogram_max  histogram_number_of_peaks  \\\n",
       "0                68            198                          6   \n",
       "1                68            198                          5   \n",
       "2                53            170                         11   \n",
       "3                53            170                          9   \n",
       "4                88            154                          5   \n",
       "...             ...            ...                        ...   \n",
       "1445            130            174                          2   \n",
       "1446            124            176                          1   \n",
       "1447            136            156                          0   \n",
       "1448            140            156                          0   \n",
       "1449            142            160                          0   \n",
       "\n",
       "      histogram_number_of_zeroes  histogram_mode  histogram_mean  \\\n",
       "0                              1             141             136   \n",
       "1                              1             141             135   \n",
       "2                              0             137             134   \n",
       "3                              0             137             136   \n",
       "4                              0             135             134   \n",
       "...                          ...             ...             ...   \n",
       "1445                           0             152             150   \n",
       "1446                           0             150             152   \n",
       "1447                           0             150             150   \n",
       "1448                           0             150             150   \n",
       "1449                           0             150             151   \n",
       "\n",
       "      histogram_median  histogram_variance  histogram_tendency  fetal_health  \n",
       "0                  140                  12                   0             1  \n",
       "1                  138                  13                   0             1  \n",
       "2                  137                  13                   1             1  \n",
       "3                  138                  11                   1             1  \n",
       "4                  137                   7                   1             1  \n",
       "...                ...                 ...                 ...           ...  \n",
       "1445               151                   1                   0             3  \n",
       "1446               153                   2                   0             3  \n",
       "1447               151                   1                   1             3  \n",
       "1448               151                   0                   0             3  \n",
       "1449               151                   0                   0             3  \n",
       "\n",
       "[1450 rows x 22 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display first 5 records \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3], dtype=int64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fetal_health'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>71</td>\n",
       "      <td>143</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>136</td>\n",
       "      <td>131</td>\n",
       "      <td>136</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>132</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>28</td>\n",
       "      <td>1.6</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>153</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>136</td>\n",
       "      <td>130</td>\n",
       "      <td>135</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>132</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>30</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>97</td>\n",
       "      <td>149</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>133</td>\n",
       "      <td>133</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>132</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>25</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>54</td>\n",
       "      <td>153</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>133</td>\n",
       "      <td>127</td>\n",
       "      <td>133</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>128</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>65</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>63</td>\n",
       "      <td>192</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>103</td>\n",
       "      <td>92</td>\n",
       "      <td>114</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>128</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>66</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>63</td>\n",
       "      <td>192</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>89</td>\n",
       "      <td>112</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>128</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>65</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>63</td>\n",
       "      <td>192</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>104</td>\n",
       "      <td>94</td>\n",
       "      <td>115</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>128</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>65</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>63</td>\n",
       "      <td>192</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "      <td>97</td>\n",
       "      <td>116</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>128</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>63</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>66</td>\n",
       "      <td>156</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>73</td>\n",
       "      <td>118</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>676 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     baseline value  accelerations  fetal_movement  uterine_contractions  \\\n",
       "0               132          0.000           0.000                 0.003   \n",
       "1               132          0.000           0.000                 0.004   \n",
       "2               132          0.001           0.000                 0.004   \n",
       "3               132          0.000           0.000                 0.003   \n",
       "4               132          0.001           0.000                 0.005   \n",
       "..              ...            ...             ...                   ...   \n",
       "671             128          0.000           0.002                 0.007   \n",
       "672             128          0.000           0.002                 0.007   \n",
       "673             128          0.000           0.002                 0.007   \n",
       "674             128          0.000           0.003                 0.007   \n",
       "675             128          0.000           0.000                 0.008   \n",
       "\n",
       "     light_decelerations  severe_decelerations  prolongued_decelerations  \\\n",
       "0                  0.003                   0.0                     0.000   \n",
       "1                  0.007                   0.0                     0.000   \n",
       "2                  0.004                   0.0                     0.000   \n",
       "3                  0.000                   0.0                     0.000   \n",
       "4                  0.004                   0.0                     0.000   \n",
       "..                   ...                   ...                       ...   \n",
       "671                0.005                   0.0                     0.002   \n",
       "672                0.005                   0.0                     0.002   \n",
       "673                0.006                   0.0                     0.002   \n",
       "674                0.006                   0.0                     0.002   \n",
       "675                0.010                   0.0                     0.000   \n",
       "\n",
       "     abnormal_short_term_variability  mean_value_of_short_term_variability  \\\n",
       "0                                 34                                   1.0   \n",
       "1                                 35                                   1.2   \n",
       "2                                 28                                   1.6   \n",
       "3                                 30                                   1.1   \n",
       "4                                 25                                   1.9   \n",
       "..                               ...                                   ...   \n",
       "671                               65                                   2.6   \n",
       "672                               66                                   2.7   \n",
       "673                               65                                   2.5   \n",
       "674                               65                                   2.5   \n",
       "675                               63                                   4.2   \n",
       "\n",
       "     percentage_of_time_with_abnormal_long_term_variability  ...  \\\n",
       "0                                                    7       ...   \n",
       "1                                                   10       ...   \n",
       "2                                                    4       ...   \n",
       "3                                                    0       ...   \n",
       "4                                                    0       ...   \n",
       "..                                                 ...       ...   \n",
       "671                                                  0       ...   \n",
       "672                                                  0       ...   \n",
       "673                                                  0       ...   \n",
       "674                                                  0       ...   \n",
       "675                                                  0       ...   \n",
       "\n",
       "     histogram_min  histogram_max  histogram_number_of_peaks  \\\n",
       "0               83            149                          7   \n",
       "1               71            143                          7   \n",
       "2               56            153                          9   \n",
       "3               97            149                          4   \n",
       "4               54            153                          6   \n",
       "..             ...            ...                        ...   \n",
       "671             63            192                          6   \n",
       "672             63            192                          4   \n",
       "673             63            192                          6   \n",
       "674             63            192                          5   \n",
       "675             66            156                          5   \n",
       "\n",
       "     histogram_number_of_zeroes  histogram_mode  histogram_mean  \\\n",
       "0                             1             136             133   \n",
       "1                             1             136             131   \n",
       "2                             3             136             130   \n",
       "3                             1             133             133   \n",
       "4                             0             133             127   \n",
       "..                          ...             ...             ...   \n",
       "671                           0             103              92   \n",
       "672                           0             105              89   \n",
       "673                           0             104              94   \n",
       "674                           0             114              97   \n",
       "675                           0              69              73   \n",
       "\n",
       "     histogram_median  histogram_variance  histogram_tendency  fetal_health  \n",
       "0                 137                   7                   1             1  \n",
       "1                 136                  11                   1             1  \n",
       "2                 135                  25                   1             1  \n",
       "3                 135                   3                   1             1  \n",
       "4                 133                  34                   1             1  \n",
       "..                ...                 ...                 ...           ...  \n",
       "671               114                  28                   0             3  \n",
       "672               112                  21                   0             3  \n",
       "673               115                  28                   0             3  \n",
       "674               116                  25                   0             3  \n",
       "675               118                 128                   0             3  \n",
       "\n",
       "[676 rows x 22 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>baseline value</th>\n",
       "      <th>accelerations</th>\n",
       "      <th>fetal_movement</th>\n",
       "      <th>uterine_contractions</th>\n",
       "      <th>light_decelerations</th>\n",
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       "      <th>mean_value_of_short_term_variability</th>\n",
       "      <th>percentage_of_time_with_abnormal_long_term_variability</th>\n",
       "      <th>...</th>\n",
       "      <th>histogram_min</th>\n",
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       "      <th>histogram_number_of_zeroes</th>\n",
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       "      <th>histogram_mean</th>\n",
       "      <th>histogram_median</th>\n",
       "      <th>histogram_variance</th>\n",
       "      <th>histogram_tendency</th>\n",
       "      <th>fetal_health</th>\n",
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       "      <td>141</td>\n",
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       "      <td>12</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>133</td>\n",
       "      <td>0.003</td>\n",
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       "      <td>0.008</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>16</td>\n",
       "      <td>2.1</td>\n",
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       "      <td>68</td>\n",
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       "      <th>2</th>\n",
       "      <td>134</td>\n",
       "      <td>0.003</td>\n",
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       "      <td>0.008</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>16</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>53</td>\n",
       "      <td>170</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>134</td>\n",
       "      <td>137</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>132</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>16</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0</td>\n",
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       "      <td>53</td>\n",
       "      <td>170</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>136</td>\n",
       "      <td>138</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>131</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>28</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>154</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "      <td>134</td>\n",
       "      <td>137</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>128</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.002</td>\n",
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       "      <td>2.6</td>\n",
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       "      <td>63</td>\n",
       "      <td>192</td>\n",
       "      <td>6</td>\n",
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       "      <td>114</td>\n",
       "      <td>28</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>128</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>66</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>63</td>\n",
       "      <td>192</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>89</td>\n",
       "      <td>112</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>128</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>65</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>63</td>\n",
       "      <td>192</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>104</td>\n",
       "      <td>94</td>\n",
       "      <td>115</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>128</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>65</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>63</td>\n",
       "      <td>192</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "      <td>97</td>\n",
       "      <td>116</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>128</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>63</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>66</td>\n",
       "      <td>156</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>73</td>\n",
       "      <td>118</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2126 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     baseline value  accelerations  fetal_movement  uterine_contractions  \\\n",
       "0               132          0.006           0.000                 0.006   \n",
       "1               133          0.003           0.000                 0.008   \n",
       "2               134          0.003           0.000                 0.008   \n",
       "3               132          0.007           0.000                 0.008   \n",
       "4               131          0.005           0.072                 0.008   \n",
       "..              ...            ...             ...                   ...   \n",
       "671             128          0.000           0.002                 0.007   \n",
       "672             128          0.000           0.002                 0.007   \n",
       "673             128          0.000           0.002                 0.007   \n",
       "674             128          0.000           0.003                 0.007   \n",
       "675             128          0.000           0.000                 0.008   \n",
       "\n",
       "     light_decelerations  severe_decelerations  prolongued_decelerations  \\\n",
       "0                  0.003                   0.0                     0.000   \n",
       "1                  0.003                   0.0                     0.000   \n",
       "2                  0.003                   0.0                     0.000   \n",
       "3                  0.000                   0.0                     0.000   \n",
       "4                  0.003                   0.0                     0.000   \n",
       "..                   ...                   ...                       ...   \n",
       "671                0.005                   0.0                     0.002   \n",
       "672                0.005                   0.0                     0.002   \n",
       "673                0.006                   0.0                     0.002   \n",
       "674                0.006                   0.0                     0.002   \n",
       "675                0.010                   0.0                     0.000   \n",
       "\n",
       "     abnormal_short_term_variability  mean_value_of_short_term_variability  \\\n",
       "0                                 17                                   2.1   \n",
       "1                                 16                                   2.1   \n",
       "2                                 16                                   2.4   \n",
       "3                                 16                                   2.4   \n",
       "4                                 28                                   1.4   \n",
       "..                               ...                                   ...   \n",
       "671                               65                                   2.6   \n",
       "672                               66                                   2.7   \n",
       "673                               65                                   2.5   \n",
       "674                               65                                   2.5   \n",
       "675                               63                                   4.2   \n",
       "\n",
       "     percentage_of_time_with_abnormal_long_term_variability  ...  \\\n",
       "0                                                    0       ...   \n",
       "1                                                    0       ...   \n",
       "2                                                    0       ...   \n",
       "3                                                    0       ...   \n",
       "4                                                    0       ...   \n",
       "..                                                 ...       ...   \n",
       "671                                                  0       ...   \n",
       "672                                                  0       ...   \n",
       "673                                                  0       ...   \n",
       "674                                                  0       ...   \n",
       "675                                                  0       ...   \n",
       "\n",
       "     histogram_min  histogram_max  histogram_number_of_peaks  \\\n",
       "0               68            198                          6   \n",
       "1               68            198                          5   \n",
       "2               53            170                         11   \n",
       "3               53            170                          9   \n",
       "4               88            154                          5   \n",
       "..             ...            ...                        ...   \n",
       "671             63            192                          6   \n",
       "672             63            192                          4   \n",
       "673             63            192                          6   \n",
       "674             63            192                          5   \n",
       "675             66            156                          5   \n",
       "\n",
       "     histogram_number_of_zeroes  histogram_mode  histogram_mean  \\\n",
       "0                             1             141             136   \n",
       "1                             1             141             135   \n",
       "2                             0             137             134   \n",
       "3                             0             137             136   \n",
       "4                             0             135             134   \n",
       "..                          ...             ...             ...   \n",
       "671                           0             103              92   \n",
       "672                           0             105              89   \n",
       "673                           0             104              94   \n",
       "674                           0             114              97   \n",
       "675                           0              69              73   \n",
       "\n",
       "     histogram_median  histogram_variance  histogram_tendency  fetal_health  \n",
       "0                 140                  12                   0             1  \n",
       "1                 138                  13                   0             1  \n",
       "2                 137                  13                   1             1  \n",
       "3                 138                  11                   1             1  \n",
       "4                 137                   7                   1             1  \n",
       "..                ...                 ...                 ...           ...  \n",
       "671               114                  28                   0             3  \n",
       "672               112                  21                   0             3  \n",
       "673               115                  28                   0             3  \n",
       "674               116                  25                   0             3  \n",
       "675               118                 128                   0             3  \n",
       "\n",
       "[2126 rows x 22 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.append(df_2)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>baseline value</th>\n",
       "      <th>accelerations</th>\n",
       "      <th>fetal_movement</th>\n",
       "      <th>uterine_contractions</th>\n",
       "      <th>light_decelerations</th>\n",
       "      <th>severe_decelerations</th>\n",
       "      <th>prolongued_decelerations</th>\n",
       "      <th>abnormal_short_term_variability</th>\n",
       "      <th>mean_value_of_short_term_variability</th>\n",
       "      <th>percentage_of_time_with_abnormal_long_term_variability</th>\n",
       "      <th>...</th>\n",
       "      <th>histogram_min</th>\n",
       "      <th>histogram_max</th>\n",
       "      <th>histogram_number_of_peaks</th>\n",
       "      <th>histogram_number_of_zeroes</th>\n",
       "      <th>histogram_mode</th>\n",
       "      <th>histogram_mean</th>\n",
       "      <th>histogram_median</th>\n",
       "      <th>histogram_variance</th>\n",
       "      <th>histogram_tendency</th>\n",
       "      <th>fetal_health</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>132</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>17</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>68</td>\n",
       "      <td>198</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>136</td>\n",
       "      <td>140</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>133</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>16</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>68</td>\n",
       "      <td>198</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>135</td>\n",
       "      <td>138</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>134</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>16</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>53</td>\n",
       "      <td>170</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>134</td>\n",
       "      <td>137</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>132</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>16</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>53</td>\n",
       "      <td>170</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>136</td>\n",
       "      <td>138</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>131</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>28</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>154</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "      <td>134</td>\n",
       "      <td>137</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2121</th>\n",
       "      <td>128</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>65</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>63</td>\n",
       "      <td>192</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>103</td>\n",
       "      <td>92</td>\n",
       "      <td>114</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2122</th>\n",
       "      <td>128</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>66</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>63</td>\n",
       "      <td>192</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>89</td>\n",
       "      <td>112</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2123</th>\n",
       "      <td>128</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>65</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>63</td>\n",
       "      <td>192</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>104</td>\n",
       "      <td>94</td>\n",
       "      <td>115</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2124</th>\n",
       "      <td>128</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>65</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>63</td>\n",
       "      <td>192</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "      <td>97</td>\n",
       "      <td>116</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</th>\n",
       "      <td>128</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>63</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>66</td>\n",
       "      <td>156</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>73</td>\n",
       "      <td>118</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2126 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      baseline value  accelerations  fetal_movement  uterine_contractions  \\\n",
       "0                132          0.006           0.000                 0.006   \n",
       "1                133          0.003           0.000                 0.008   \n",
       "2                134          0.003           0.000                 0.008   \n",
       "3                132          0.007           0.000                 0.008   \n",
       "4                131          0.005           0.072                 0.008   \n",
       "...              ...            ...             ...                   ...   \n",
       "2121             128          0.000           0.002                 0.007   \n",
       "2122             128          0.000           0.002                 0.007   \n",
       "2123             128          0.000           0.002                 0.007   \n",
       "2124             128          0.000           0.003                 0.007   \n",
       "2125             128          0.000           0.000                 0.008   \n",
       "\n",
       "      light_decelerations  severe_decelerations  prolongued_decelerations  \\\n",
       "0                   0.003                   0.0                     0.000   \n",
       "1                   0.003                   0.0                     0.000   \n",
       "2                   0.003                   0.0                     0.000   \n",
       "3                   0.000                   0.0                     0.000   \n",
       "4                   0.003                   0.0                     0.000   \n",
       "...                   ...                   ...                       ...   \n",
       "2121                0.005                   0.0                     0.002   \n",
       "2122                0.005                   0.0                     0.002   \n",
       "2123                0.006                   0.0                     0.002   \n",
       "2124                0.006                   0.0                     0.002   \n",
       "2125                0.010                   0.0                     0.000   \n",
       "\n",
       "      abnormal_short_term_variability  mean_value_of_short_term_variability  \\\n",
       "0                                  17                                   2.1   \n",
       "1                                  16                                   2.1   \n",
       "2                                  16                                   2.4   \n",
       "3                                  16                                   2.4   \n",
       "4                                  28                                   1.4   \n",
       "...                               ...                                   ...   \n",
       "2121                               65                                   2.6   \n",
       "2122                               66                                   2.7   \n",
       "2123                               65                                   2.5   \n",
       "2124                               65                                   2.5   \n",
       "2125                               63                                   4.2   \n",
       "\n",
       "      percentage_of_time_with_abnormal_long_term_variability  ...  \\\n",
       "0                                                     0       ...   \n",
       "1                                                     0       ...   \n",
       "2                                                     0       ...   \n",
       "3                                                     0       ...   \n",
       "4                                                     0       ...   \n",
       "...                                                 ...       ...   \n",
       "2121                                                  0       ...   \n",
       "2122                                                  0       ...   \n",
       "2123                                                  0       ...   \n",
       "2124                                                  0       ...   \n",
       "2125                                                  0       ...   \n",
       "\n",
       "      histogram_min  histogram_max  histogram_number_of_peaks  \\\n",
       "0                68            198                          6   \n",
       "1                68            198                          5   \n",
       "2                53            170                         11   \n",
       "3                53            170                          9   \n",
       "4                88            154                          5   \n",
       "...             ...            ...                        ...   \n",
       "2121             63            192                          6   \n",
       "2122             63            192                          4   \n",
       "2123             63            192                          6   \n",
       "2124             63            192                          5   \n",
       "2125             66            156                          5   \n",
       "\n",
       "      histogram_number_of_zeroes  histogram_mode  histogram_mean  \\\n",
       "0                              1             141             136   \n",
       "1                              1             141             135   \n",
       "2                              0             137             134   \n",
       "3                              0             137             136   \n",
       "4                              0             135             134   \n",
       "...                          ...             ...             ...   \n",
       "2121                           0             103              92   \n",
       "2122                           0             105              89   \n",
       "2123                           0             104              94   \n",
       "2124                           0             114              97   \n",
       "2125                           0              69              73   \n",
       "\n",
       "      histogram_median  histogram_variance  histogram_tendency  fetal_health  \n",
       "0                  140                  12                   0             1  \n",
       "1                  138                  13                   0             1  \n",
       "2                  137                  13                   1             1  \n",
       "3                  138                  11                   1             1  \n",
       "4                  137                   7                   1             1  \n",
       "...                ...                 ...                 ...           ...  \n",
       "2121               114                  28                   0             3  \n",
       "2122               112                  21                   0             3  \n",
       "2123               115                  28                   0             3  \n",
       "2124               116                  25                   0             3  \n",
       "2125               118                 128                   0             3  \n",
       "\n",
       "[2126 rows x 22 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index(drop = True, inplace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2126, 22)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "baseline value                                              int64\n",
       "accelerations                                             float64\n",
       "fetal_movement                                            float64\n",
       "uterine_contractions                                      float64\n",
       "light_decelerations                                       float64\n",
       "severe_decelerations                                      float64\n",
       "prolongued_decelerations                                  float64\n",
       "abnormal_short_term_variability                             int64\n",
       "mean_value_of_short_term_variability                      float64\n",
       "percentage_of_time_with_abnormal_long_term_variability      int64\n",
       "mean_value_of_long_term_variability                       float64\n",
       "histogram_width                                             int64\n",
       "histogram_min                                               int64\n",
       "histogram_max                                               int64\n",
       "histogram_number_of_peaks                                   int64\n",
       "histogram_number_of_zeroes                                  int64\n",
       "histogram_mode                                              int64\n",
       "histogram_mean                                              int64\n",
       "histogram_median                                            int64\n",
       "histogram_variance                                          int64\n",
       "histogram_tendency                                          int64\n",
       "fetal_health                                                int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['baseline value', 'accelerations', 'fetal_movement',\n",
       "       'uterine_contractions', 'light_decelerations', 'severe_decelerations',\n",
       "       'prolongued_decelerations', 'abnormal_short_term_variability',\n",
       "       'mean_value_of_short_term_variability',\n",
       "       'percentage_of_time_with_abnormal_long_term_variability',\n",
       "       'mean_value_of_long_term_variability', 'histogram_width',\n",
       "       'histogram_min', 'histogram_max', 'histogram_number_of_peaks',\n",
       "       'histogram_number_of_zeroes', 'histogram_mode', 'histogram_mean',\n",
       "       'histogram_median', 'histogram_variance', 'histogram_tendency',\n",
       "       'fetal_health'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing Continuous variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing baseline value column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in baseline value column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for baseline value column:\n",
      "106 160\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in baseline value column:')\n",
    "print(df['baseline value'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for baseline value column:')\n",
    "print(df['baseline value'].min(), df['baseline value'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "283404"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['baseline value'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "133    136\n",
       "130    111\n",
       "122    109\n",
       "138    103\n",
       "125     91\n",
       "128     85\n",
       "120     78\n",
       "144     77\n",
       "142     77\n",
       "132     76\n",
       "136     72\n",
       "140     70\n",
       "134     67\n",
       "135     65\n",
       "146     62\n",
       "127     60\n",
       "137     59\n",
       "129     57\n",
       "123     56\n",
       "131     56\n",
       "143     56\n",
       "148     52\n",
       "121     44\n",
       "139     39\n",
       "141     39\n",
       "126     38\n",
       "145     37\n",
       "115     28\n",
       "150     26\n",
       "110     21\n",
       "149     18\n",
       "152     17\n",
       "119     17\n",
       "112     16\n",
       "147     14\n",
       "151     14\n",
       "159     12\n",
       "114     11\n",
       "158     10\n",
       "124     10\n",
       "118      9\n",
       "154      8\n",
       "106      7\n",
       "116      5\n",
       "157      4\n",
       "156      4\n",
       "117      2\n",
       "160      1\n",
       "Name: baseline value, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['baseline value'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    286\n",
       "9     271\n",
       "11    242\n",
       "7     203\n",
       "1     194\n",
       "4     183\n",
       "5     168\n",
       "3     157\n",
       "2     153\n",
       "8     137\n",
       "6     132\n",
       "Name: baseline value_bin, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the baseline value column\n",
    "cut_label_baseline_value_bin = ['1', '2', '3', '4', '5','6','7','8','9','10','11']\n",
    "\n",
    "cut_bins = [105, 120, 122, 125, 128, 130, 132, 134, 136, 140, 145, 160]\n",
    "\n",
    "df['baseline value_bin'] = pd.cut(df['baseline value'], bins=cut_bins, labels=cut_label_baseline_value_bin)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['baseline value_bin'].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing accelerations column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in accelerations column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for accelerations column:\n",
      "0.0 0.019\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in accelerations column:')\n",
    "print(df['accelerations'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for accelerations column:')\n",
    "print(df['accelerations'].min(), df['accelerations'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000    894\n",
       "0.003    161\n",
       "0.002    160\n",
       "0.001    143\n",
       "0.004    117\n",
       "0.006    112\n",
       "0.005    110\n",
       "0.008    103\n",
       "0.007     91\n",
       "0.009     60\n",
       "0.010     50\n",
       "0.011     36\n",
       "0.012     24\n",
       "0.013     22\n",
       "0.014     20\n",
       "0.015      9\n",
       "0.016      7\n",
       "0.017      4\n",
       "0.018      2\n",
       "0.019      1\n",
       "Name: accelerations, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "df['accelerations'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    894\n",
       "2    303\n",
       "7    194\n",
       "3    161\n",
       "9    125\n",
       "4    117\n",
       "6    112\n",
       "8    110\n",
       "5    110\n",
       "Name: accelerations_bin, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the accelerations column\n",
    "cut_label_accelerations = ['1', '2', '3', '4', '5', '6', '7','8','9']\n",
    "\n",
    "cut_bins = [-1, 0.000, 0.002, 0.003, 0.004, 0.005, 0.006, 0.008, 0.010, 0.019]\n",
    "\n",
    "df['accelerations_bin'] = pd.cut(df['accelerations'], bins=cut_bins, labels=cut_label_accelerations)\n",
    "\n",
    "df['accelerations_bin'].value_counts()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing fetal_movement column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in fetal_movement column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for fetal_movement column:\n",
      "0.0 0.48100000000000004\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in fetal_movement column:')\n",
    "print(df['fetal_movement'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for fetal_movement column:')\n",
    "print(df['fetal_movement'].min(), df['fetal_movement'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000    1311\n",
       "0.001     164\n",
       "0.002     112\n",
       "0.003      88\n",
       "0.004      49\n",
       "         ... \n",
       "0.092       1\n",
       "0.383       1\n",
       "0.051       1\n",
       "0.037       1\n",
       "0.306       1\n",
       "Name: fetal_movement, Length: 102, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "\n",
    "df['fetal_movement'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1311\n",
       "2     276\n",
       "4     251\n",
       "3     173\n",
       "5     115\n",
       "Name: fetal_movement_bin, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the fetal_movement column\n",
    "cut_label_fetal_movement = ['1', '2', '3', '4', '5']\n",
    "\n",
    "cut_bins = [-1,0.000,0.002,0.005,0.025, 0.482]\n",
    "\n",
    "df['fetal_movement_bin'] = pd.cut(df['fetal_movement'], bins=cut_bins, labels=cut_label_fetal_movement)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['fetal_movement_bin'].value_counts()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing uterine_contractions column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in uterine_contractions column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for uterine_contractions column:\n",
      "0.0 0.015\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in uterine_contractions column:')\n",
    "print(df['uterine_contractions'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for uterine_contractions column:')\n",
    "print(df['uterine_contractions'].min(), df['uterine_contractions'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000    332\n",
       "0.005    290\n",
       "0.004    244\n",
       "0.006    231\n",
       "0.007    216\n",
       "0.003    212\n",
       "0.002    160\n",
       "0.008    160\n",
       "0.001    118\n",
       "0.009     82\n",
       "0.010     49\n",
       "0.011     16\n",
       "0.012     11\n",
       "0.013      2\n",
       "0.014      2\n",
       "0.015      1\n",
       "Name: uterine_contractions, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "df['uterine_contractions'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    332\n",
       "5    290\n",
       "2    278\n",
       "4    244\n",
       "6    231\n",
       "7    216\n",
       "3    212\n",
       "9    163\n",
       "8    160\n",
       "Name: uterine_contractions_bin, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the uterine_contractions column\n",
    "cut_label_uterine_contractions = ['1', '2', '3', '4', '5', '6', '7', '8', '9']\n",
    "\n",
    "cut_bins = [-1, 0.000, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.015]\n",
    "\n",
    "df['uterine_contractions_bin'] = pd.cut(df['uterine_contractions'], bins=cut_bins, labels=cut_label_uterine_contractions)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['uterine_contractions_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing light_decelerations column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in light_decelerations column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for light_decelerations column:\n",
      "0.0 0.015\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in light_decelerations column:')\n",
    "print(df['light_decelerations'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for light_decelerations column:')\n",
    "print(df['light_decelerations'].min(), df['light_decelerations'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000    1231\n",
       "0.001     163\n",
       "0.003     118\n",
       "0.002     115\n",
       "0.004     114\n",
       "0.005     107\n",
       "0.006      74\n",
       "0.008      55\n",
       "0.007      54\n",
       "0.009      37\n",
       "0.010      15\n",
       "0.011      13\n",
       "0.012      12\n",
       "0.013       8\n",
       "0.014       7\n",
       "0.015       3\n",
       "Name: light_decelerations, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "df['light_decelerations'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1231\n",
       "6     278\n",
       "2     278\n",
       "3     118\n",
       "4     114\n",
       "5     107\n",
       "Name: light_decelerations_bin, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the light_decelerations column\n",
    "cut_label_light_decelerations = ['1', '2', '3', '4', '5', '6']\n",
    "\n",
    "cut_bins = [-1, 0.000, 0.002, 0.003, 0.004, 0.005, 0.015]\n",
    "\n",
    "df['light_decelerations_bin'] = pd.cut(df['light_decelerations'], bins=cut_bins, labels=cut_label_light_decelerations)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['light_decelerations_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing severe_decelerations column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in severe_decelerations column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for severe_decelerations column:\n",
      "0.0 0.001\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in severe_decelerations column:')\n",
    "print(df['severe_decelerations'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for severe_decelerations column:')\n",
    "print(df['severe_decelerations'].min(), df['severe_decelerations'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000    2119\n",
       "0.001       7\n",
       "Name: severe_decelerations, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "df['severe_decelerations'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2119\n",
       "2       7\n",
       "Name: severe_decelerations_bin, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the severe_decelerations column\n",
    "cut_label_severe_decelerations = ['1', '2']\n",
    "\n",
    "cut_bins = [-1, 0.0005, 0.001]\n",
    "\n",
    "df['severe_decelerations_bin'] = pd.cut(df['severe_decelerations'], bins=cut_bins, labels=cut_label_severe_decelerations)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['severe_decelerations_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing prolongued_decelerations column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in prolongued_decelerations column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for prolongued_decelerations column:\n",
      "0.0 0.005\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in prolongued_decelerations column:')\n",
    "print(df['prolongued_decelerations'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for prolongued_decelerations column:')\n",
    "print(df['prolongued_decelerations'].min(), df['prolongued_decelerations'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000    1948\n",
       "0.002      72\n",
       "0.001      70\n",
       "0.003      24\n",
       "0.004       9\n",
       "0.005       3\n",
       "Name: prolongued_decelerations, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "df['prolongued_decelerations'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1948\n",
       "2     178\n",
       "Name: prolongued_decelerations_bin, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the prolongued_decelerations column\n",
    "cut_label_prolongued_decelerations = ['1', '2']\n",
    "\n",
    "cut_bins = [-1, 0.0001, 0.005]\n",
    "\n",
    "df['prolongued_decelerations_bin'] = pd.cut(df['prolongued_decelerations'], bins=cut_bins, labels=cut_label_prolongued_decelerations)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['prolongued_decelerations_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing abnormal_short_term_variability column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in abnormal_short_term_variability column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for abnormal_short_term_variability column:\n",
      "12 87\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in abnormal_short_term_variability column:')\n",
    "print(df['abnormal_short_term_variability'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for abnormal_short_term_variability column:')\n",
    "print(df['abnormal_short_term_variability'].min(), df['abnormal_short_term_variability'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60    62\n",
       "58    61\n",
       "65    60\n",
       "63    58\n",
       "64    58\n",
       "      ..\n",
       "15     4\n",
       "86     4\n",
       "12     2\n",
       "82     2\n",
       "87     1\n",
       "Name: abnormal_short_term_variability, Length: 75, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "df['abnormal_short_term_variability'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    284\n",
       "9     250\n",
       "8     188\n",
       "5     180\n",
       "1     179\n",
       "4     171\n",
       "12    161\n",
       "2     158\n",
       "6     155\n",
       "3     147\n",
       "7     134\n",
       "11    119\n",
       "Name: abnormal_short_term_variability_bin, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the abnormal_short_term_variability column\n",
    "cut_label_abnormal_short_term_variability = ['1', '2', '3','4','5','6','7', '8', '9', '10', '11', '12']\n",
    "\n",
    "cut_bins = [11, 22, 26, 30, 35, 40, 45, 50, 55, 60, 65, 70, 87]\n",
    "\n",
    "df['abnormal_short_term_variability_bin'] = pd.cut(df['abnormal_short_term_variability'], bins=cut_bins, labels=cut_label_abnormal_short_term_variability)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['abnormal_short_term_variability_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing mean_value_of_short_term_variability column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in mean_value_of_short_term_variability column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for mean_value_of_short_term_variability column:\n",
      "0.2 7.0\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in mean_value_of_short_term_variability column:')\n",
    "print(df['mean_value_of_short_term_variability'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for mean_value_of_short_term_variability column:')\n",
    "print(df['mean_value_of_short_term_variability'].min(), df['mean_value_of_short_term_variability'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting the datatype to float\n",
    "df['mean_value_of_short_term_variability'] = df['mean_value_of_short_term_variability'].apply(lambda x:float(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8    125\n",
       "1.3    121\n",
       "0.5    121\n",
       "0.4    120\n",
       "0.7    117\n",
       "0.9    114\n",
       "0.6    113\n",
       "1.2    107\n",
       "1.5    100\n",
       "1.0     99\n",
       "1.1     97\n",
       "1.4     95\n",
       "0.3     84\n",
       "1.7     78\n",
       "1.6     76\n",
       "1.9     62\n",
       "1.8     51\n",
       "0.2     47\n",
       "2.2     47\n",
       "2.1     44\n",
       "2.0     38\n",
       "2.3     27\n",
       "2.4     27\n",
       "2.5     26\n",
       "2.7     25\n",
       "2.8     22\n",
       "2.6     21\n",
       "3.0     16\n",
       "2.9     13\n",
       "3.2     13\n",
       "3.4     12\n",
       "3.1     10\n",
       "3.3      7\n",
       "3.8      6\n",
       "3.6      4\n",
       "3.5      3\n",
       "4.2      3\n",
       "4.9      3\n",
       "4.1      3\n",
       "3.7      3\n",
       "5.4      2\n",
       "4.5      2\n",
       "5.0      2\n",
       "4.0      2\n",
       "4.3      2\n",
       "4.4      2\n",
       "6.3      2\n",
       "3.9      2\n",
       "4.8      2\n",
       "5.7      1\n",
       "5.9      1\n",
       "5.2      1\n",
       "4.7      1\n",
       "5.3      1\n",
       "7.0      1\n",
       "6.0      1\n",
       "6.9      1\n",
       "Name: mean_value_of_short_term_variability, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "df['mean_value_of_short_term_variability'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9    361\n",
       "8    305\n",
       "2    241\n",
       "4    239\n",
       "3    230\n",
       "6    228\n",
       "5    196\n",
       "7    195\n",
       "1    131\n",
       "Name: mean_value_of_short_term_variability_bin, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the cut_label_mean_value_of_short_term_variability column\n",
    "cut_label_mean_value_of_short_term_variability = ['1', '2', '3', '4', '5', '6', '7', '8', '9']\n",
    "\n",
    "cut_bins = [0, 0.3, 0.5, 0.7, 0.9, 1.15, 1.3, 1.5, 2.0, 7.0]\n",
    "\n",
    "df['mean_value_of_short_term_variability_bin'] = pd.cut(df['mean_value_of_short_term_variability'], bins=cut_bins, labels=cut_label_mean_value_of_short_term_variability)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['mean_value_of_short_term_variability_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing percentage_of_time_with_abnormal_long_term_variability column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in percentage_of_time_with_abnormal_long_term_variability column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for percentage_of_time_with_abnormal_long_term_variability column:\n",
      "0 91\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in percentage_of_time_with_abnormal_long_term_variability column:')\n",
    "print(df['percentage_of_time_with_abnormal_long_term_variability'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for percentage_of_time_with_abnormal_long_term_variability column:')\n",
    "print(df['percentage_of_time_with_abnormal_long_term_variability'].min(), df['percentage_of_time_with_abnormal_long_term_variability'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting the datatype to float\n",
    "df['percentage_of_time_with_abnormal_long_term_variability'] = df['percentage_of_time_with_abnormal_long_term_variability'].apply(lambda x:float(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     1240\n",
       "1.0       52\n",
       "2.0       45\n",
       "5.0       43\n",
       "4.0       40\n",
       "        ... \n",
       "88.0       1\n",
       "65.0       1\n",
       "82.0       1\n",
       "86.0       1\n",
       "63.0       1\n",
       "Name: percentage_of_time_with_abnormal_long_term_variability, Length: 87, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "df['percentage_of_time_with_abnormal_long_term_variability'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1240\n",
       "2     886\n",
       "Name: percentage_of_time_with_abnormal_long_term_variability_bin, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the percentage_of_time_with_abnormal_long_term_variability column\n",
    "cut_label_percentage_of_time_with_abnormal_long_term_variability = ['1', '2']\n",
    "\n",
    "cut_bins = [-1, 0, 91]\n",
    "\n",
    "df['percentage_of_time_with_abnormal_long_term_variability_bin'] = pd.cut(df['percentage_of_time_with_abnormal_long_term_variability'], bins=cut_bins, labels=cut_label_percentage_of_time_with_abnormal_long_term_variability)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['percentage_of_time_with_abnormal_long_term_variability_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing of mean_value_of_long_term_variability column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in mean_value_of_long_term_variability column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for mean_value_of_long_term_variability column:\n",
      "0.0 50.7\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in mean_value_of_long_term_variability column:')\n",
    "print(df['mean_value_of_long_term_variability'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for mean_value_of_long_term_variability column:')\n",
    "print(df['mean_value_of_long_term_variability'].min(), df['mean_value_of_long_term_variability'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     137\n",
       "6.7      29\n",
       "7.1      29\n",
       "5.2      25\n",
       "6.5      25\n",
       "       ... \n",
       "29.5      1\n",
       "25.3      1\n",
       "27.0      1\n",
       "29.1      1\n",
       "23.0      1\n",
       "Name: mean_value_of_long_term_variability, Length: 249, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "df['mean_value_of_long_term_variability'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9     301\n",
       "8     275\n",
       "1     264\n",
       "10    210\n",
       "5     202\n",
       "6     193\n",
       "2     191\n",
       "4     169\n",
       "7     165\n",
       "3     156\n",
       "Name: mean_value_of_long_term_variability_bin, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the mean_value_of_long_term_variability column\n",
    "cut_label_mean_value_of_long_term_variability = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']\n",
    "\n",
    "cut_bins = [-1, 2, 4, 5, 6, 7, 8, 9, 11, 15, 50.7]\n",
    "\n",
    "df['mean_value_of_long_term_variability_bin'] = pd.cut(df['mean_value_of_long_term_variability'], bins=cut_bins, labels=cut_label_mean_value_of_long_term_variability)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['mean_value_of_long_term_variability_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing of histogram_width column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in histogram_width column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for histogram_width column:\n",
      "3 180\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in histogram_width column:')\n",
    "print(df['histogram_width'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for histogram_width column:')\n",
    "print(df['histogram_width'].min(), df['histogram_width'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39     42\n",
       "102    35\n",
       "27     30\n",
       "31     29\n",
       "98     28\n",
       "       ..\n",
       "180     1\n",
       "146     1\n",
       "162     1\n",
       "131     1\n",
       "6       1\n",
       "Name: histogram_width, Length: 154, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "df['histogram_width'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    370\n",
       "9     337\n",
       "2     207\n",
       "6     205\n",
       "1     185\n",
       "3     179\n",
       "8     178\n",
       "5     165\n",
       "4     163\n",
       "7     137\n",
       "Name: histogram_width_bin, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the histogram_width column\n",
    "cut_label_histogram_width = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']\n",
    "\n",
    "cut_bins = [0, 20, 30, 38, 45, 55, 70, 80, 90, 110, 180]\n",
    "\n",
    "df['histogram_width_bin'] = pd.cut(df['histogram_width'], bins=cut_bins, labels=cut_label_histogram_width)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['histogram_width_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing of histogram_min column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in histogram_min column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for histogram_min column:\n",
      "50 159\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in histogram_min column:')\n",
    "print(df['histogram_min'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for histogram_min column:')\n",
    "print(df['histogram_min'].min(), df['histogram_min'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50     77\n",
       "52     50\n",
       "71     49\n",
       "120    48\n",
       "60     45\n",
       "       ..\n",
       "155     2\n",
       "149     2\n",
       "159     1\n",
       "156     1\n",
       "158     1\n",
       "Name: histogram_min, Length: 109, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "df['histogram_min'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    358\n",
       "6    329\n",
       "3    298\n",
       "5    292\n",
       "8    276\n",
       "4    206\n",
       "2    204\n",
       "1    163\n",
       "Name: histogram_min_bin, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the histogram_min column\n",
    "cut_label_histogram_min= ['1', '2', '3', '4', '5', '6', '7', '8']\n",
    "\n",
    "cut_bins = [0, 52, 60, 70, 80, 100, 115, 130, 159]\n",
    "\n",
    "df['histogram_min_bin'] = pd.cut(df['histogram_min'], bins=cut_bins, labels=cut_label_histogram_min)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['histogram_min_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing of histogram_max column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in histogram_max column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for histogram_max column:\n",
      "122 238\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in histogram_max column:')\n",
    "print(df['histogram_max'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for histogram_max column:')\n",
    "print(df['histogram_max'].min(), df['histogram_max'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "157    71\n",
       "171    66\n",
       "158    62\n",
       "156    60\n",
       "159    58\n",
       "       ..\n",
       "134     2\n",
       "123     2\n",
       "127     2\n",
       "205     1\n",
       "213     1\n",
       "Name: histogram_max, Length: 86, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "df['histogram_max'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     349\n",
       "5     296\n",
       "4     243\n",
       "7     210\n",
       "6     199\n",
       "10    198\n",
       "3     172\n",
       "9     158\n",
       "1     153\n",
       "2     148\n",
       "Name: histogram_max_bin, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the histogram_max column\n",
    "cut_label_histogram_max = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']\n",
    "\n",
    "cut_bins = [0, 140, 145, 150, 155, 160, 165, 170, 180, 190, 238]\n",
    "\n",
    "df['histogram_max_bin'] = pd.cut(df['histogram_max'], bins=cut_bins, labels=cut_label_histogram_max)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['histogram_max_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing of histogram_number_of_peaks column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in histogram_number_of_peaks column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for histogram_number_of_peaks column:\n",
      "0 18\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in histogram_number_of_peaks column:')\n",
    "print(df['histogram_number_of_peaks'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for histogram_number_of_peaks column:')\n",
    "print(df['histogram_number_of_peaks'].min(), df['histogram_number_of_peaks'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     357\n",
       "2     331\n",
       "3     269\n",
       "4     258\n",
       "5     210\n",
       "6     158\n",
       "7     145\n",
       "0     107\n",
       "8     106\n",
       "9      67\n",
       "10     49\n",
       "11     28\n",
       "12     22\n",
       "13     10\n",
       "14      5\n",
       "16      2\n",
       "18      1\n",
       "15      1\n",
       "Name: histogram_number_of_peaks, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "df['histogram_number_of_peaks'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     357\n",
       "3     331\n",
       "4     269\n",
       "5     258\n",
       "6     210\n",
       "10    185\n",
       "7     158\n",
       "8     145\n",
       "1     107\n",
       "9     106\n",
       "Name: histogram_number_of_peaks_bin, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the histogram_number_of_peaks column\n",
    "cut_label_histogram_number_of_peaks = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']\n",
    "\n",
    "cut_bins = [-1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 18]\n",
    "\n",
    "df['histogram_number_of_peaks_bin'] = pd.cut(df['histogram_number_of_peaks'], bins=cut_bins, labels=cut_label_histogram_number_of_peaks)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['histogram_number_of_peaks_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing of histogram_number_of_zeroes column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in histogram_number_of_zeroes column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for histogram_number_of_zeroes column:\n",
      "0 10\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in histogram_number_of_zeroes column:')\n",
    "print(df['histogram_number_of_zeroes'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for histogram_number_of_zeroes column:')\n",
    "print(df['histogram_number_of_zeroes'].min(), df['histogram_number_of_zeroes'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1624\n",
       "1      366\n",
       "2      108\n",
       "3       21\n",
       "5        2\n",
       "4        2\n",
       "7        1\n",
       "10       1\n",
       "8        1\n",
       "Name: histogram_number_of_zeroes, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "df['histogram_number_of_zeroes'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1624\n",
       "2     502\n",
       "Name: histogram_number_of_zeroes_bin, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the histogram_number_of_zeroes column\n",
    "cut_label_histogram_number_of_zeroes = ['1', '2']\n",
    "\n",
    "cut_bins = [-1, 0, 10]\n",
    "\n",
    "df['histogram_number_of_zeroes_bin'] = pd.cut(df['histogram_number_of_zeroes'], bins=cut_bins, labels=cut_label_histogram_number_of_zeroes)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['histogram_number_of_zeroes_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing of histogram_mode column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in histogram_mode column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for histogram_mode column:\n",
      "60 187\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in histogram_mode column:')\n",
    "print(df['histogram_mode'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for histogram_mode column:')\n",
    "print(df['histogram_mode'].min(), df['histogram_mode'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "133    140\n",
       "150     89\n",
       "136     89\n",
       "142     87\n",
       "148     79\n",
       "      ... \n",
       "179      1\n",
       "77       1\n",
       "95       1\n",
       "97       1\n",
       "71       1\n",
       "Name: histogram_mode, Length: 88, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "df['histogram_mode'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    385\n",
       "7    337\n",
       "5    298\n",
       "4    289\n",
       "2    269\n",
       "1    236\n",
       "3    166\n",
       "8    146\n",
       "Name: histogram_mode_bin, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the histogram_mode column\n",
    "cut_label_histogram_mode = ['1', '2', '3', '4', '5', '6', '7', '8']\n",
    "\n",
    "cut_bins = [0, 120, 127, 132, 136, 142, 148, 158, 187]\n",
    "\n",
    "df['histogram_mode_bin'] = pd.cut(df['histogram_mode'], bins=cut_bins, labels=cut_label_histogram_mode)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['histogram_mode_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing of histogram_mean column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in histogram_mean column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for histogram_mean column:\n",
      "73 182\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in histogram_mean column:')\n",
    "print(df['histogram_mean'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for histogram_mean column:')\n",
    "print(df['histogram_mean'].min(), df['histogram_mean'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "143    65\n",
       "144    64\n",
       "135    63\n",
       "141    61\n",
       "140    60\n",
       "       ..\n",
       "79      1\n",
       "81      1\n",
       "165     1\n",
       "169     1\n",
       "76      1\n",
       "Name: histogram_mean, Length: 103, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "df['histogram_mean'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9    371\n",
       "8    344\n",
       "3    230\n",
       "7    223\n",
       "1    216\n",
       "4    212\n",
       "5    207\n",
       "6    206\n",
       "2    117\n",
       "Name: histogram_mean_bin, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the histogram_mean column\n",
    "cut_label_histogram_mean= ['1', '2', '3', '4', '5', '6', '7', '8', '9']\n",
    "\n",
    "cut_bins = [0, 115, 120, 125, 130, 134, 138, 142, 148, 182]\n",
    "\n",
    "df['histogram_mean_bin'] = pd.cut(df['histogram_mean'], bins=cut_bins, labels=cut_label_histogram_mean)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['histogram_mean_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing of histogram_median column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in histogram_median column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for histogram_median column:\n",
      "77 186\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in histogram_median column:')\n",
    "print(df['histogram_median'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for histogram_median column:')\n",
    "print(df['histogram_median'].min(), df['histogram_median'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "146    69\n",
       "142    68\n",
       "137    68\n",
       "145    67\n",
       "147    65\n",
       "       ..\n",
       "91      1\n",
       "93      1\n",
       "99      1\n",
       "171     1\n",
       "78      1\n",
       "Name: histogram_median, Length: 95, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "df['histogram_median'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    353\n",
       "8    301\n",
       "5    285\n",
       "6    239\n",
       "3    216\n",
       "9    212\n",
       "2    197\n",
       "4    178\n",
       "1    145\n",
       "Name: histogram_median_bin, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the histogram_median column\n",
    "cut_label_histogram_median = ['1', '2', '3', '4', '5', '6', '7', '8', '9']\n",
    "\n",
    "cut_bins = [0, 115, 124, 129, 133, 138, 142, 148, 155, 186]\n",
    "\n",
    "df['histogram_median_bin'] = pd.cut(df['histogram_median'], bins=cut_bins, labels=cut_label_histogram_median)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['histogram_median_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing of histogram_variance column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in histogram_variance column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for histogram_variance column:\n",
      "0 269\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in histogram_variance column:')\n",
    "print(df['histogram_variance'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for histogram_variance column:')\n",
    "print(df['histogram_variance'].min(), df['histogram_variance'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      248\n",
       "0      187\n",
       "2      166\n",
       "3      161\n",
       "4      108\n",
       "      ... \n",
       "110      1\n",
       "106      1\n",
       "104      1\n",
       "96       1\n",
       "250      1\n",
       "Name: histogram_variance, Length: 133, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "df['histogram_variance'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8    382\n",
       "6    284\n",
       "7    282\n",
       "2    248\n",
       "9    223\n",
       "5    193\n",
       "1    187\n",
       "3    166\n",
       "4    161\n",
       "Name: histogram_variance_bin, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the histogram_variance column\n",
    "cut_label_histogram_variance = ['1', '2', '3', '4', '5', '6', '7', '8', '9']\n",
    "\n",
    "cut_bins = [-1, 0, 1, 2, 3, 5, 10, 20, 50, 269]\n",
    "\n",
    "df['histogram_variance_bin'] = pd.cut(df['histogram_variance'], bins=cut_bins, labels=cut_label_histogram_variance)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['histogram_variance_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing of histogram_tendency column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Null Values in histogram_tendency column:\n",
      "0\n",
      "\n",
      "Minimum and Maximum values for histogram_tendency column:\n",
      "-1 1\n"
     ]
    }
   ],
   "source": [
    "#Display total null values\n",
    "print('Total Null Values in histogram_tendency column:')\n",
    "print(df['histogram_tendency'].isna().sum())\n",
    "\n",
    "# Display minimum and maximum values\n",
    "print('\\nMinimum and Maximum values for histogram_tendency column:')\n",
    "print(df['histogram_tendency'].min(), df['histogram_tendency'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    1115\n",
       " 1     846\n",
       "-1     165\n",
       "Name: histogram_tendency, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding counts of unique values\n",
    "df['histogram_tendency'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    1115\n",
       "3     846\n",
       "1     165\n",
       "Name: histogram_tendency_bin, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretizing the histogram_tendency column\n",
    "cut_label_histogram_tendency = ['1', '2', '3']\n",
    "\n",
    "cut_bins = [-2, -1, 0, 1]\n",
    "\n",
    "df['histogram_tendency_bin'] = pd.cut(df['histogram_tendency'], bins=cut_bins, labels=cut_label_histogram_tendency)\n",
    "\n",
    "# Finding counts of unique values\n",
    "df['histogram_tendency_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1655\n",
       "2     295\n",
       "3     176\n",
       "Name: fetal_health, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fetal_health'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q1:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a node class for the decision tree\n",
    "class Node():\n",
    "    def __init__(self, feature_index=None,left=None,right=None,middle=None, value=None):\n",
    "       #constructor  \n",
    "        \n",
    "        # decision node\n",
    "        self.feature_index = feature_index\n",
    "        self.left = left\n",
    "        self.middle=middle\n",
    "        self.right = right\n",
    "        \n",
    "        # leaf node\n",
    "        self.value = value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree classifier using Weight of Evidence(WOE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This decision tree classifier is being designed by us to be used for one attribute based split or two attribute per node based split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# class for decision tree classifier\n",
    "\n",
    "class DecisionTreeClassifier(): \n",
    "    def __init__(self, min_samples_split=3, max_depth=3, no_of_IV=0):\n",
    "        # constructor \n",
    "        \n",
    "        # root of the tree \n",
    "        self.root = None\n",
    "        \n",
    "        # Stopping Criteria\n",
    "        self.min_samples_split = min_samples_split\n",
    "        self.max_depth = max_depth\n",
    "        self.no_of_IV = no_of_IV \n",
    "        \n",
    "    def build_tree(self, dataset, curr_depth=0):  \n",
    "        # function to build the tree \n",
    "\n",
    "        X, Y = dataset.iloc[:,:-1].values, dataset.iloc[:,-1].values\n",
    "        num_samples, num_features = np.shape(X)\n",
    "        \n",
    "        # check the stopping criteria to split the node\n",
    "        if num_samples>=self.min_samples_split and curr_depth<=self.max_depth:\n",
    "            \n",
    "            best_split = self.get_best_split(dataset, num_samples, num_features)\n",
    "        \n",
    "            # building left subtree using recursion\n",
    "            left_subtree = self.build_tree(best_split[\"dataset_left\"], curr_depth+1)\n",
    "            \n",
    "            # building middle subtree using recursion\n",
    "            middle_subtree = self.build_tree(best_split[\"dataset_middle\"], curr_depth+1)\n",
    "\n",
    "            # building right subtree using recursion\n",
    "            right_subtree = self.build_tree(best_split[\"dataset_right\"], curr_depth+1)\n",
    "\n",
    "            # return the decision node\n",
    "            return Node(best_split[\"feature_index\"],left_subtree, middle_subtree, right_subtree)\n",
    "        \n",
    "       \n",
    "        # Finding the class leaf node\n",
    "        leaf_value = self.calculate_leaf_value(Y)\n",
    "        \n",
    "        # return the leaf node\n",
    "        return Node(value=leaf_value)\n",
    "      \n",
    "    def get_best_split(self, dataset, num_samples, num_features):\n",
    "        # Function to get the best split based on the IV value\n",
    "        \n",
    "        dataset = pd.DataFrame(dataset)\n",
    "        \n",
    "        best_split = {}\n",
    "        \n",
    "       \n",
    "        # dataframe for the continuous variables\n",
    "        df_con = dataset[['baseline value', 'accelerations', 'fetal_movement',\n",
    "       'uterine_contractions', 'light_decelerations', 'severe_decelerations',\n",
    "       'prolongued_decelerations', 'abnormal_short_term_variability',\n",
    "       'mean_value_of_short_term_variability',\n",
    "       'percentage_of_time_with_abnormal_long_term_variability',\n",
    "       'mean_value_of_long_term_variability', 'histogram_width',\n",
    "       'histogram_min', 'histogram_max', 'histogram_number_of_peaks',\n",
    "       'histogram_number_of_zeroes', 'histogram_mode', 'histogram_mean',\n",
    "       'histogram_median', 'histogram_variance', 'histogram_tendency']]\n",
    "        \n",
    "        # ataframe for the continuous variables including the bin variables \n",
    "        data = dataset[['baseline value', 'accelerations', 'fetal_movement',\n",
    "       'uterine_contractions', 'light_decelerations', 'severe_decelerations',\n",
    "       'prolongued_decelerations', 'abnormal_short_term_variability',\n",
    "       'mean_value_of_short_term_variability',\n",
    "       'percentage_of_time_with_abnormal_long_term_variability',\n",
    "       'mean_value_of_long_term_variability', 'histogram_width',\n",
    "       'histogram_min', 'histogram_max', 'histogram_number_of_peaks',\n",
    "       'histogram_number_of_zeroes', 'histogram_mode', 'histogram_mean',\n",
    "       'histogram_median', 'histogram_variance', 'histogram_tendency',\n",
    "       'baseline value_bin', 'accelerations_bin', 'fetal_movement_bin',\n",
    "       'uterine_contractions_bin', 'light_decelerations_bin', 'severe_decelerations_bin',\n",
    "       'prolongued_decelerations_bin', 'abnormal_short_term_variability_bin',\n",
    "       'mean_value_of_short_term_variability_bin',\n",
    "       'percentage_of_time_with_abnormal_long_term_variability_bin',\n",
    "       'mean_value_of_long_term_variability_bin', 'histogram_width_bin',\n",
    "       'histogram_min_bin', 'histogram_max_bin', 'histogram_number_of_peaks_bin',\n",
    "       'histogram_number_of_zeroes_bin', 'histogram_mode_bin', 'histogram_mean_bin',\n",
    "       'histogram_median_bin', 'histogram_variance_bin', 'histogram_tendency_bin']]\n",
    "\n",
    "        #Target Class\n",
    "        target = 'fetal_health' \n",
    "    \n",
    "        #lists containing IV values for continuous variables\n",
    "        \n",
    "        iv_con=[]\n",
    "        iv_con_col_name =[]\n",
    "        \n",
    "        # column names \n",
    "       \n",
    "        feature_con = df_con.columns\n",
    "      \n",
    "            \n",
    "        #lists containing Information Value(IV) values for continuous variables\n",
    "        for col in feature_con:\n",
    "            df2, iv2 = self.calculate_woe_iv_con(df_con,data, col, target)\n",
    "            iv_con.append(iv2)\n",
    "            iv_con_col_name.append(col)\n",
    "            \n",
    "        #concatenating the IV values and their corresponding attributes into one list\n",
    "        iv_col = iv_con_col_name\n",
    "        iv=iv_con\n",
    "\n",
    "        my_dict = {\"col\":iv_col,\"iv\":iv};\n",
    "        new = pd.DataFrame.from_dict(my_dict)\n",
    "        \n",
    "        #sorting the dataframe containg IV values in decreasing order \n",
    "#       and filtering only relevant IV values\n",
    "        new_df = new.sort_values(by=['iv'], ascending=False)\n",
    "        new_df1 =new_df[new_df['iv']<=0.5]\n",
    "\n",
    "        #creating list of best one or two feature numbers/index  \n",
    "        a=[]\n",
    "        for i in range(self.no_of_IV):\n",
    "            l=new_df1.index[new_df1['iv']==new_df1.iloc[i]['iv']] #returns list\n",
    "            a.append(l)\n",
    "\n",
    "        dataset1 =dataset.iloc[:,:43]\n",
    "        feature_ind = []\n",
    "        for s in a:\n",
    "            feature_ind.append(s.item()+1)\n",
    "            \n",
    "        y = dataset.iloc[:, 42].values\n",
    "        z=dataset.iloc[:,feature_ind].values\n",
    "        y=y.reshape(y.shape[0], 1)\n",
    "        z=z.reshape(z.shape[0], len(a))\n",
    "        \n",
    "        #calling logistic regression to calculate new feature for splitting the node\n",
    "        result= self.logistic_split(y, z, dataset1)\n",
    "        \n",
    "        dataset_right = pd.DataFrame()\n",
    "        dataset_middle = pd.DataFrame()\n",
    "        dataset_left = pd.DataFrame()\n",
    "        \n",
    "        #splitting the node\n",
    "        dataset_left,dataset_middle, dataset_right = self.split(result)\n",
    "        \n",
    "        # decision node values on best split\n",
    "        best_split[\"feature_index\"] = feature_ind\n",
    "        best_split[\"dataset_left\"] = dataset_left\n",
    "        best_split[\"dataset_middle\"] = dataset_middle\n",
    "        best_split[\"dataset_right\"] = dataset_right\n",
    "        \n",
    "        \n",
    "        return best_split\n",
    "    \n",
    "    def split(self, dataset): \n",
    "        \n",
    "        # function to split the data \n",
    "        dataset_left = dataset[dataset.iloc[:,-1] == 1]\n",
    "        dataset_middle = dataset[dataset.iloc[:,-1] == 2]\n",
    "        dataset_right = dataset[dataset.iloc[:,-1] == 3]\n",
    "        \n",
    "        return dataset_left, dataset_middle, dataset_right\n",
    "    \n",
    "    def logistic_split(self, y_train, X_train, dataset):\n",
    "\n",
    "        # create logistic regression classifier using IV values\n",
    "        LR=LogisticRegression(C=0.1,max_iter=1000,solver='newton-cg',random_state=1)\n",
    "      \n",
    "        y_train=y_train.astype('int')\n",
    "        LR.fit(X_train,y_train)\n",
    "          \n",
    "        #compute probabilities\n",
    "        prob=LR.predict_proba(X_train)\n",
    "        \n",
    "        #create list with predicted values from logistic regression classifier\n",
    "        LRPredict_train=[]\n",
    "        for i in range(len(y_train)):\n",
    "            if(prob[i][0]>0.333):\n",
    "                LRPredict_train.append(1)\n",
    "            elif(prob[i][0]>0.333):\n",
    "                LRPredict_train.append(2)\n",
    "            else : \n",
    "                LRPredict_train.append(3)\n",
    "\n",
    "        LRPredict_train=np.array(LRPredict_train)     \n",
    "       \n",
    "        a=pd.DataFrame(dataset.iloc[:,:43])\n",
    "        c=pd.DataFrame(LRPredict_train)\n",
    "\n",
    "        a.reset_index(drop=True, inplace=True)\n",
    "        c.reset_index(drop=True, inplace=True)\n",
    "    \n",
    "        # Create concatenated dataframe containing given class label and predicted class label\n",
    "        frames = [a,c]  \n",
    "        result = pd.concat(frames, axis=1) \n",
    "\n",
    "        return result\n",
    "    \n",
    "    def calculate_leaf_value(self, Y):\n",
    "        # function to compute the leaf node \n",
    "        Y = list(Y)\n",
    "        \n",
    "        # the most frequent value is returned as the class label and in case of an empty sequence return nan \n",
    "        return max(Y, key=Y.count, default=np.nan)\n",
    "    \n",
    "    def print_tree(self, tree=None, indent=\" \"):\n",
    "        # function to print the tree \n",
    "        threshold=0.333\n",
    "        if not tree:\n",
    "            tree = self.root\n",
    "\n",
    "        if tree.value is not None:\n",
    "            print(tree.value)\n",
    "\n",
    "        else:\n",
    "            print(\"X_\"+str(tree.feature_index), \"<=\", threshold)\n",
    "            print(\"%sleft:\" % (indent), end=\"\")\n",
    "            self.print_tree(tree.left, indent + indent)\n",
    "            print(\"%smiddle:\" % (indent), end=\"\")\n",
    "            self.print_tree(tree.middle, indent + indent)\n",
    "            print(\"%sright:\" % (indent), end=\"\")\n",
    "            self.print_tree(tree.right, indent + indent)\n",
    "    \n",
    "    def fit(self, X, Y):\n",
    "        # function to train the tree \n",
    "        \n",
    "        dataset = np.concatenate((X, Y), axis=1)\n",
    "        dataset = pd.DataFrame(dataset)\n",
    "        dataset.columns = ['baseline value', 'accelerations', 'fetal_movement',\n",
    "       'uterine_contractions', 'light_decelerations', 'severe_decelerations',\n",
    "       'prolongued_decelerations', 'abnormal_short_term_variability',\n",
    "       'mean_value_of_short_term_variability',\n",
    "       'percentage_of_time_with_abnormal_long_term_variability',\n",
    "       'mean_value_of_long_term_variability', 'histogram_width',\n",
    "       'histogram_min', 'histogram_max', 'histogram_number_of_peaks',\n",
    "       'histogram_number_of_zeroes', 'histogram_mode', 'histogram_mean',\n",
    "       'histogram_median', 'histogram_variance', 'histogram_tendency',\n",
    "       'baseline value_bin', 'accelerations_bin', 'fetal_movement_bin',\n",
    "       'uterine_contractions_bin', 'light_decelerations_bin', 'severe_decelerations_bin',\n",
    "       'prolongued_decelerations_bin', 'abnormal_short_term_variability_bin',\n",
    "       'mean_value_of_short_term_variability_bin',\n",
    "       'percentage_of_time_with_abnormal_long_term_variability_bin',\n",
    "       'mean_value_of_long_term_variability_bin', 'histogram_width_bin',\n",
    "       'histogram_min_bin', 'histogram_max_bin', 'histogram_number_of_peaks_bin',\n",
    "       'histogram_number_of_zeroes_bin', 'histogram_mode_bin', 'histogram_mean_bin',\n",
    "       'histogram_median_bin', 'histogram_variance_bin', 'histogram_tendency_bin','fetal_health']\n",
    "      \n",
    "        self.root = self.build_tree(dataset, 0)\n",
    "    \n",
    "       \n",
    "    def calculate_woe_iv_con(self, df,data, feature, target):\n",
    "    #function to calculate WOE and IV for continuous variables\n",
    "    \n",
    "        #creating column names for features whose bins are created \n",
    "        feat_bin = feature + '_bin'\n",
    "\n",
    "        grouped_df = data.groupby(feat_bin).sum()\n",
    "        grouped_df.reset_index(level=0, inplace=True)\n",
    "\n",
    "        a=data[feat_bin].value_counts().to_dict()\n",
    "        d=grouped_df[[feat_bin,feature]]\n",
    "        d.columns = ['bin','sum']\n",
    "        d['obs'] = d['bin'].map(a)\n",
    "        d = d.astype(float)\n",
    "        d['percent_obs'] = d['obs']/(d['obs'].sum()) * 100\n",
    "        d['percent_Y'] = (d['sum']/d['sum'].sum()) * 100\n",
    "        d['WOE'] = np.log(d['percent_Y']/d['percent_obs'])\n",
    "        d['IV'] = (d['percent_Y']/100 - d['percent_obs']/100) * d['WOE']\n",
    "        sum_of_IV=d['IV'].sum()\n",
    "\n",
    "        return d, sum_of_IV\n",
    "    \n",
    "    def predict(self, X):\n",
    "        # function to predict new dataset \n",
    "        predictions = [self.make_prediction(x, self.root) for x in X]\n",
    "        return predictions\n",
    "    \n",
    "    def make_prediction(self, x, tree):\n",
    "        threshold1 = 2\n",
    "        threshold2 = 3\n",
    "        # function to predict a single data point \n",
    "        if tree.value!=None: return tree.value\n",
    "        feature_val = x[-1]\n",
    "        if int(feature_val)<threshold1:\n",
    "            return self.make_prediction(x, tree.left)\n",
    "        elif int(feature_val)>=threshold1 & int(feature_val)<threshold2:\n",
    "            return self.make_prediction(x, tree.middle)\n",
    "        else:\n",
    "            return self.make_prediction(x, tree.right)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train-Test split using 80:20 ratio as given in question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create target variable dataframe\n",
    "target = df[['fetal_health']]\n",
    "#create dataframe with independent variables\n",
    "df = df.drop(columns = 'fetal_health')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#concat the dataframe\n",
    "df = pd.concat([df,target],axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the dataset into input variables and target variables\n",
    "\n",
    "X = df.iloc[:, :-1].values\n",
    "Y = df.iloc[:, -1].values.reshape(-1,1)\n",
    "\n",
    "#split the data into train and test part\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.20, random_state=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit the model using one attribute for the split and visualize it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_[9] <= 0.333\n",
      " left:X_[9] <= 0.333\n",
      "  left:X_[9] <= 0.333\n",
      "    left:X_[9] <= 0.333\n",
      "        left:X_[9] <= 0.333\n",
      "                left:X_[9] <= 0.333\n",
      "                                left:X_[9] <= 0.333\n",
      "                                                                left:1\n",
      "                                                                middle:3\n",
      "                                                                right:nan\n",
      "                                middle:3\n",
      "                                right:nan\n",
      "                middle:3\n",
      "                right:nan\n",
      "        middle:3\n",
      "        right:nan\n",
      "    middle:3\n",
      "    right:nan\n",
      "  middle:3\n",
      "  right:nan\n",
      " middle:3\n",
      " right:nan\n"
     ]
    }
   ],
   "source": [
    "#train the model \n",
    "classifier1 = DecisionTreeClassifier(min_samples_split=140, max_depth=6, no_of_IV=1)\n",
    "classifier1.fit(X_train,Y_train)\n",
    "\n",
    "# #Print the tree\n",
    "classifier1.print_tree()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions on the test data for model with one attribute for the split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07746478873239436"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make predictions\n",
    "Y_pred1 = classifier1.predict(X_test) \n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "#print the accuracy score\n",
    "accuracy_score(Y_test, Y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.57      0.05      0.09       337\n",
      "           2       0.00      0.00      0.00        63\n",
      "           3       0.04      0.65      0.08        26\n",
      "\n",
      "    accuracy                           0.08       426\n",
      "   macro avg       0.20      0.23      0.06       426\n",
      "weighted avg       0.45      0.08      0.07       426\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the classification report containing precision, recall, f1-score, support\n",
    "print(metrics.classification_report(Y_test, Y_pred1))\n",
    "\n",
    "# #print the ROC_AUC score\n",
    "# print (roc_auc_score(Y_test, Y_pred1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit the model using two attributes for the split and visualize it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_[9, 12] <= 0.333\n",
      " left:X_[9, 12] <= 0.333\n",
      "  left:X_[9, 12] <= 0.333\n",
      "    left:X_[9, 12] <= 0.333\n",
      "        left:X_[9, 12] <= 0.333\n",
      "                left:X_[9, 12] <= 0.333\n",
      "                                left:X_[9, 12] <= 0.333\n",
      "                                                                left:1\n",
      "                                                                middle:3\n",
      "                                                                right:nan\n",
      "                                middle:3\n",
      "                                right:nan\n",
      "                middle:3\n",
      "                right:nan\n",
      "        middle:3\n",
      "        right:nan\n",
      "    middle:3\n",
      "    right:nan\n",
      "  middle:3\n",
      "  right:nan\n",
      " middle:3\n",
      " right:nan\n"
     ]
    }
   ],
   "source": [
    "#train the model\n",
    "classifier2 = DecisionTreeClassifier(min_samples_split=150, max_depth=6, no_of_IV=2)\n",
    "classifier2.fit(X_train,Y_train)\n",
    "\n",
    "# #Print the tree\n",
    "classifier2.print_tree()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions on the test data for model with two attributes for the split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07746478873239436"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make predictions\n",
    "Y_pred2 = classifier2.predict(X_test) \n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "#print the accuracy score\n",
    "accuracy_score(Y_test, Y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.57      0.05      0.09       337\n",
      "           2       0.00      0.00      0.00        63\n",
      "           3       0.04      0.65      0.08        26\n",
      "\n",
      "    accuracy                           0.08       426\n",
      "   macro avg       0.20      0.23      0.06       426\n",
      "weighted avg       0.45      0.08      0.07       426\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the classification report containing precision, recall, f1-score, support\n",
    "print(metrics.classification_report(Y_test, Y_pred2))\n",
    "\n",
    "#You can't use roc_auc as a single summary metric for multiclass models.\n",
    "# print (roc_auc_score(Y_test, Y_pred2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q2:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize custom made decision tree using one attribute "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_[9] <= 0.333\n",
      " left:X_[9] <= 0.333\n",
      "  left:X_[9] <= 0.333\n",
      "    left:X_[9] <= 0.333\n",
      "        left:X_[9] <= 0.333\n",
      "                left:X_[9] <= 0.333\n",
      "                                left:X_[9] <= 0.333\n",
      "                                                                left:1\n",
      "                                                                middle:3\n",
      "                                                                right:nan\n",
      "                                middle:3\n",
      "                                right:nan\n",
      "                middle:3\n",
      "                right:nan\n",
      "        middle:3\n",
      "        right:nan\n",
      "    middle:3\n",
      "    right:nan\n",
      "  middle:3\n",
      "  right:nan\n",
      " middle:3\n",
      " right:nan\n"
     ]
    }
   ],
   "source": [
    "# #Print the tree\n",
    "classifier1.print_tree()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# column 9 is percentage_of_time_with_abnormal_long_term_variability \n",
    "\n",
    "# The rules can be visualized using tree generated\n",
    "# if probability (percentage_of_time_with_abnormal_long_term_variability) < =0.333  then class = 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize custom made decision tree using two attribute "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_[9, 12] <= 0.333\n",
      " left:X_[9, 12] <= 0.333\n",
      "  left:X_[9, 12] <= 0.333\n",
      "    left:X_[9, 12] <= 0.333\n",
      "        left:X_[9, 12] <= 0.333\n",
      "                left:X_[9, 12] <= 0.333\n",
      "                                left:X_[9, 12] <= 0.333\n",
      "                                                                left:1\n",
      "                                                                middle:3\n",
      "                                                                right:nan\n",
      "                                middle:3\n",
      "                                right:nan\n",
      "                middle:3\n",
      "                right:nan\n",
      "        middle:3\n",
      "        right:nan\n",
      "    middle:3\n",
      "    right:nan\n",
      "  middle:3\n",
      "  right:nan\n",
      " middle:3\n",
      " right:nan\n"
     ]
    }
   ],
   "source": [
    "# #Print the tree\n",
    "classifier2.print_tree()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['baseline value', 'accelerations', 'fetal_movement',\n",
       "       'uterine_contractions', 'light_decelerations', 'severe_decelerations',\n",
       "       'prolongued_decelerations', 'abnormal_short_term_variability',\n",
       "       'mean_value_of_short_term_variability',\n",
       "       'percentage_of_time_with_abnormal_long_term_variability',\n",
       "       'mean_value_of_long_term_variability', 'histogram_width',\n",
       "       'histogram_min', 'histogram_max', 'histogram_number_of_peaks',\n",
       "       'histogram_number_of_zeroes', 'histogram_mode', 'histogram_mean',\n",
       "       'histogram_median', 'histogram_variance', 'histogram_tendency',\n",
       "       'baseline value_bin', 'accelerations_bin', 'fetal_movement_bin',\n",
       "       'uterine_contractions_bin', 'light_decelerations_bin',\n",
       "       'severe_decelerations_bin', 'prolongued_decelerations_bin',\n",
       "       'abnormal_short_term_variability_bin',\n",
       "       'mean_value_of_short_term_variability_bin',\n",
       "       'percentage_of_time_with_abnormal_long_term_variability_bin',\n",
       "       'mean_value_of_long_term_variability_bin', 'histogram_width_bin',\n",
       "       'histogram_min_bin', 'histogram_max_bin',\n",
       "       'histogram_number_of_peaks_bin', 'histogram_number_of_zeroes_bin',\n",
       "       'histogram_mode_bin', 'histogram_mean_bin', 'histogram_median_bin',\n",
       "       'histogram_variance_bin', 'histogram_tendency_bin', 'fetal_health'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# column 9 is percentage_of_time_with_abnormal_long_term_variability and column 12 is histogram_min\n",
    "\n",
    "# The rules can be visualized using the tree generated\n",
    "# if probability (percentage_of_time_with_abnormal_long_term_variability and histogram_min) < =0.333 then class =1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normal Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9295774647887324\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.94      0.98      0.96       337\n",
      "           2       0.91      0.65      0.76        63\n",
      "           3       0.86      0.96      0.91        26\n",
      "\n",
      "    accuracy                           0.93       426\n",
      "   macro avg       0.90      0.86      0.88       426\n",
      "weighted avg       0.93      0.93      0.93       426\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Train the decision tree classifier using scikit learn\n",
    "dt = DecisionTreeClassifier(criterion='gini', min_samples_split=140,min_samples_leaf=1, max_depth=6)\n",
    "dt.fit(X_train,Y_train)\n",
    "\n",
    "#Make Predictions\n",
    "Y_pred = dt.predict(X_test)\n",
    "\n",
    "#print the accuracy score\n",
    "print(accuracy_score(Y_test, Y_pred))\n",
    "\n",
    "# Print the classification report containing precision, recall, f1-score, support\n",
    "print(metrics.classification_report(Y_test, Y_pred))\n",
    "\n",
    "# You can't use roc_auc as a single summary metric for multiclass models.\n",
    "# print(roc_auc_score(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "plt.figure(figsize=(20,10))\n",
    "tree.plot_tree(dt);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparision and observations from visualizing the splits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The custom made decision tree using WOE and logistic regression generates rules based on probabilities as thresholds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### However, the normal decision tree generates rules using attribute values as thresholds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This is evident from the visualizations provided above"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q3:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model using 5 fold Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.90      0.93      0.92      1655\n",
      "           2       0.65      0.61      0.63       295\n",
      "           3       0.84      0.61      0.71       176\n",
      "\n",
      "    accuracy                           0.86      2126\n",
      "   macro avg       0.80      0.72      0.75      2126\n",
      "weighted avg       0.86      0.86      0.86      2126\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "y_pred_cv=cross_val_predict(dt,X,Y,cv=5)\n",
    "\n",
    "#print the accuracy score\n",
    "accuracy_score(Y, y_pred_cv)\n",
    "\n",
    "# Print the classification report containing precision, recall, f1-score, support\n",
    "print(metrics.classification_report(Y, y_pred_cv))\n",
    "\n",
    "# You can't use roc_auc as a single summary metric for multiclass models.\n",
    "# print(roc_auc_score(Y, y_pred_cv))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Student t test for single attribute "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=array([-43.85251182]), pvalue=array([1.83173296e-220]))"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    " \n",
    "# Perform the two sample t-test with equal variances\n",
    "stats.ttest_ind(a=Y_test, b=Y_pred1, equal_var=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two sample t-test has the following hypothesis:\n",
    "\n",
    "H0 => µ1 = µ2 (population mean of given class labels is equal to the mean of the predicted class labels)\n",
    "\n",
    "HA => µ1 ≠µ2 (population mean of given class labels is different from the mean of the predicted class labels)\n",
    "\n",
    "Here, since the p-value (1.83173296e-220) is less than alpha = 0.05 so we reject the null hypothesis (H0) of the test."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Student t test for two attributes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=array([-43.85251182]), pvalue=array([1.83173296e-220]))"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    " \n",
    "# Perform the two sample t-test with equal variances\n",
    "stats.ttest_ind(a=Y_test, b=Y_pred2, equal_var=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two sample t-test has the following hypothesis:\n",
    "\n",
    "H0 => µ1 = µ2 (population mean of given class labels is equal to the mean of the predicted class labels)\n",
    "\n",
    "HA => µ1 ≠µ2 (population mean of given class labels is different from the mean of the predicted class labels)\n",
    "\n",
    "Here, since the p-value (1.83173296e-220) is less than alpha = 0.05 so we reject the null hypothesis (H0) of the test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since the accuracy of both the models is similar ,the possible reason for this may be that when we are taking two \n",
    "# attributes for making the model both of them are possibly correlated with each other.\n",
    "\n",
    "#Hence, the presence of two attributes per node versus one attribute per node both are generating similar results in terms\n",
    "# of precision, recall, F1 score and accuracy ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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