main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c0be41b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import dependencies\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9e6fe996",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Demonstrate conversion of the horse-colic.txt file to horse-colic.csv - pandas -- first method\n",
    "horse_colic_raw = pd.read_csv('dataset/horse-colic.txt',header=None,dtype='str')\n",
    "horse_colic_raw.to_csv('dataset/horse-colic.csv',index=False,header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b6b6fd02",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Demonstrate conversion of the horse-colic.txt file to horse-colic.csv - pandas -- second method\n",
    "import csv\n",
    "\n",
    "with open('dataset/horse-colic.txt','r')as in_file:\n",
    "    stripped = (line.strip() for line in in_file)\n",
    "    lines = (line.split(\",\") for line in stripped if line)\n",
    "    with open('dataset/horse-colic2.csv','w')as out_file:\n",
    "        writer = csv.writer(out_file)\n",
    "        writer.writerows(lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "00fd879b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Demonstrate that the csv file can read in Jupyter Notebook - pandas\n",
    "horse_colic = pd.read_csv('dataset/horse-colic.csv',header=None,dtype='str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2868992d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both horse-colic txt and csv dataset remain the same\n"
     ]
    }
   ],
   "source": [
    "#Demonstrate that the dataset remains the same as the raw dataset - dataframe.compare.empty\n",
    "if horse_colic.compare(horse_colic_raw).empty == True:\n",
    "    print('Both horse-colic txt and csv dataset remain the same')\n",
    "else:\n",
    "    print('There is a change of values in datasets. Please recheck.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c9ce4706",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replace the missing values (i.e. with “?”) with a NaN (Not a Number) value in a loaded dataset using Python\n",
    "horse_colic.replace({'?':'NaN'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b3c65f84",
   "metadata": {},
   "outputs": [
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       "  0  1        2      3    4   5    6    7    8  9    10   11   12   13   14    15   16   17     18    19   20    21 22 23     24     25     26 27\n",
       "0  2  1   530101  38.50   66  28    3    3  NaN  2    5    4    4  NaN  NaN   NaN    3    5  45.00  8.40  NaN   NaN  2  2  11300  00000  00000  2\n",
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       "4  2  1   530255  37.30  104  35  NaN  NaN    6  2  NaN  NaN  NaN  NaN  NaN   NaN  NaN  NaN  74.00  7.40  NaN   NaN  2  2  04300  00000  00000  2"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "horse_colic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "784c6ee5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save the latest dataset with NaN as horse-colic-with-NaN.csv and ensure the data frame’s row number is not saved into the csv file.\n",
    "horse_colic.to_csv('dataset/horse-colic-with-NaN.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4a855155",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename the header by following the description file (horse-colic.names) with not more than 10 characters in the header title\n",
    "header_dict = {\n",
    "    0:'surgery',\n",
    "    1:'age',\n",
    "    2:'id',\n",
    "    3:'temp_rect',\n",
    "    4:'pulse',\n",
    "    5:'rr',\n",
    "    6:'peri_circ',\n",
    "    7:'peri_pulse',\n",
    "    8:'mm_clr',\n",
    "    9:'crt',\n",
    "    10:'pain_score',\n",
    "    11:'gut_pst',\n",
    "    12:'abd_dist',\n",
    "    13:'ngt',\n",
    "    14:'ng_rflx',\n",
    "    15:'rflx_ph',\n",
    "    16:'rct_feces',\n",
    "    17:'abd_obs',\n",
    "    18:'pcv',\n",
    "    19:'ttl_prot',\n",
    "    20:'abdct_app',\n",
    "    21:'abdct_prot',\n",
    "    22:'outcome',\n",
    "    23:'surg_ls',\n",
    "    24:'ls_type1',\n",
    "    25:'ls_type2',\n",
    "    26:'ls_type3',\n",
    "    27:'cp_data'    \n",
    "}\n",
    "\n",
    "horse_colic.rename(columns=header_dict,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a25596c5",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "  surgery age       id temp_rect pulse  rr peri_circ peri_pulse mm_clr crt pain_score gut_pst abd_dist  ngt ng_rflx rflx_ph rct_feces abd_obs    pcv ttl_prot abdct_app abdct_prot outcome surg_ls ls_type1 ls_type2 ls_type3 cp_data\n",
       "0       2   1   530101     38.50    66  28         3          3    NaN   2          5       4        4  NaN     NaN     NaN         3       5  45.00     8.40       NaN        NaN       2       2    11300    00000    00000       2\n",
       "1       1   1   534817      39.2    88  20       NaN        NaN      4   1          3       4        2  NaN     NaN     NaN         4       2     50       85         2          2       3       2    02208    00000    00000       2\n",
       "2       2   1   530334     38.30    40  24         1          1      3   1          3       3        1  NaN     NaN     NaN         1       1  33.00     6.70       NaN        NaN       1       2    00000    00000    00000       1\n",
       "3       1   9  5290409     39.10   164  84         4          1      6   2          2       4        4    1       2    5.00         3     NaN  48.00     7.20         3       5.30       2       1    02208    00000    00000       1\n",
       "4       2   1   530255     37.30   104  35       NaN        NaN      6   2        NaN     NaN      NaN  NaN     NaN     NaN       NaN     NaN  74.00     7.40       NaN        NaN       2       2    04300    00000    00000       2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "horse_colic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "93e58d5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save the latest dataset with header title as horse-colic-with-title.csv and ensure the data frame’s row number is not saved into the csv file.\n",
    "horse_colic.to_csv('dataset/horse-colic-with-title.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "417ff76b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "surgery         1\n",
      "age             0\n",
      "id              0\n",
      "temp_rect      60\n",
      "pulse          24\n",
      "rr             58\n",
      "peri_circ      56\n",
      "peri_pulse     69\n",
      "mm_clr         47\n",
      "crt            32\n",
      "pain_score     55\n",
      "gut_pst        44\n",
      "abd_dist       56\n",
      "ngt           104\n",
      "ng_rflx       106\n",
      "rflx_ph       247\n",
      "rct_feces     102\n",
      "abd_obs       118\n",
      "pcv            29\n",
      "ttl_prot       33\n",
      "abdct_app     165\n",
      "abdct_prot    198\n",
      "outcome         1\n",
      "surg_ls         0\n",
      "ls_type1        0\n",
      "ls_type2        0\n",
      "ls_type3        0\n",
      "cp_data         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Load the horse-colic-with-title.csv file and display a list of total NaN at each column in the dataset.\n",
    "horse_colic_final = pd.read_csv('dataset/horse-colic-with-title.csv')\n",
    "total_null = horse_colic_final.isnull().sum()\n",
    "print(total_null)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "36315028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualise the total NaN at each column in the dataset with a bar chart and set the colour to ‘blue’ using Matplotlib library.\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(15,10))\n",
    "total_null.plot.bar(color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "da8481f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "surgery       float64\n",
       "age             int64\n",
       "id              int64\n",
       "temp_rect     float64\n",
       "pulse         float64\n",
       "rr            float64\n",
       "peri_circ     float64\n",
       "peri_pulse    float64\n",
       "mm_clr        float64\n",
       "crt           float64\n",
       "pain_score    float64\n",
       "gut_pst       float64\n",
       "abd_dist      float64\n",
       "ngt           float64\n",
       "ng_rflx       float64\n",
       "rflx_ph       float64\n",
       "rct_feces     float64\n",
       "abd_obs       float64\n",
       "pcv           float64\n",
       "ttl_prot      float64\n",
       "abdct_app     float64\n",
       "abdct_prot    float64\n",
       "outcome       float64\n",
       "surg_ls         int64\n",
       "ls_type1        int64\n",
       "ls_type2        int64\n",
       "ls_type3        int64\n",
       "cp_data         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Retrieve the datatypes (e.g. float, integer, object, string etc.) for the dataset.\n",
    "horse_colic_final.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6e7feedb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Answer : No - Dropping rows with NaN will definitely make this dataset unusable as \n",
      "98.0% of the rows in this dataset will be removed.\n",
      "\n",
      "There are several ways of handling missing values in python such as fillna(),replace() and interpolate() functions\n"
     ]
    }
   ],
   "source": [
    "#In this dataset, will it be a good idea to delete/drop out the NaN rows? Justify your answer.\n",
    "count_NaN_rows = len(horse_colic_final[horse_colic_final.isnull().any(axis=1)])\n",
    "overall_rows = len(horse_colic_final)\n",
    "NaN_rows_percentage = count_NaN_rows/overall_rows*100\n",
    "print(f'''\n",
    "Answer : No - Dropping rows with NaN will definitely make this dataset unusable as \n",
    "{NaN_rows_percentage}% of the rows in this dataset will be removed.\n",
    "''')\n",
    "\n",
    "print('There are several ways of handling missing values in python such as fillna(),replace() and interpolate() functions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2eb9d2b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method: Mean Imputation of Missing Values (Univariate)\n",
      "Justification : The Scikit Learn - Imputer Module is able to reduce null values to 0 by replacing the null values with a predicted values based on columns and rows pattern of the dataset\n",
      "Missing: 1605\n",
      "Missing: 0\n"
     ]
    }
   ],
   "source": [
    "#Read and evaluate the following code in Figure 1. What method is the code using to reduce the missing value from 1605 to 0? Justify your answer.\n",
    "print ('Method: Mean Imputation of Missing Values (Univariate)')\n",
    "print ('Justification : The Scikit Learn - Imputer Module is able to reduce null values to 0 by replacing the null values with a predicted values based on columns and rows pattern of the dataset')\n",
    "\n",
    "from numpy import isnan\n",
    "from pandas import read_csv\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# load dataset\n",
    "# replace the '?' with NaN\n",
    "\n",
    "#convert dataframe to list-array\n",
    "data = horse_colic_final.values\n",
    "\n",
    "#remove outcome column from dataset\n",
    "totalCol = data.shape[1]\n",
    "ix = [i for i in range(totalCol) if i !=23]\n",
    "X = data[:,ix]\n",
    "y = data[:,23]\n",
    "\n",
    "#count and display number of missing values in pre-imputated dataset\n",
    "print('Missing: %d' % sum(isnan(X).flatten()))\n",
    "\n",
    "#using mean imputation from scikit-lean SimpleImputer - calculate mean within columns and rows of missing values and point the intersection of those values\n",
    "imputer = SimpleImputer(strategy='mean')\n",
    "#predict missing values based on mean of its column - imputer\n",
    "imputer.fit(X)\n",
    "Xtrans = imputer.transform(X)\n",
    "\n",
    "#count and display number of missing values in imputated dataset\n",
    "print('Missing: %d' % sum(isnan(Xtrans).flatten()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "643978a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_trans = pd.DataFrame(Xtrans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9b4b82cb",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>534817.0</td>\n",
       "      <td>39.200000</td>\n",
       "      <td>88.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.348361</td>\n",
       "      <td>2.017316</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.755102</td>\n",
       "      <td>1.582474</td>\n",
       "      <td>4.707547</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2208.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>530334.0</td>\n",
       "      <td>38.300000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.755102</td>\n",
       "      <td>1.582474</td>\n",
       "      <td>4.707547</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>2.037037</td>\n",
       "      <td>3.019608</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5290409.0</td>\n",
       "      <td>39.100000</td>\n",
       "      <td>164.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.692308</td>\n",
       "      <td>48.0</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.300000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2208.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>530255.0</td>\n",
       "      <td>37.300000</td>\n",
       "      <td>104.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.348361</td>\n",
       "      <td>2.017316</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.95102</td>\n",
       "      <td>2.917969</td>\n",
       "      <td>2.266393</td>\n",
       "      <td>1.755102</td>\n",
       "      <td>1.582474</td>\n",
       "      <td>4.707547</td>\n",
       "      <td>2.757576</td>\n",
       "      <td>3.692308</td>\n",
       "      <td>74.0</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>2.037037</td>\n",
       "      <td>3.019608</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4300.0</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>533886.0</td>\n",
       "      <td>38.167917</td>\n",
       "      <td>120.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.017316</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.266393</td>\n",
       "      <td>1.755102</td>\n",
       "      <td>1.582474</td>\n",
       "      <td>4.707547</td>\n",
       "      <td>2.757576</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>55.0</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>2.037037</td>\n",
       "      <td>3.019608</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3205.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>527702.0</td>\n",
       "      <td>37.200000</td>\n",
       "      <td>72.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.707547</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>44.0</td>\n",
       "      <td>24.456929</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2208.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>529386.0</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>72.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.707547</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>2.037037</td>\n",
       "      <td>3.019608</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3205.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>530612.0</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.707547</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2208.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>534618.0</td>\n",
       "      <td>37.200000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.348361</td>\n",
       "      <td>2.017316</td>\n",
       "      <td>2.853755</td>\n",
       "      <td>1.30597</td>\n",
       "      <td>2.95102</td>\n",
       "      <td>2.917969</td>\n",
       "      <td>2.266393</td>\n",
       "      <td>1.755102</td>\n",
       "      <td>1.582474</td>\n",
       "      <td>4.707547</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.0</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6112.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0    1          2          3      4     5         6         7         8        9        10        11        12        13        14        15        16        17    18         19        20        21   22       23   24   25   26\n",
       "0    2.0  1.0   530101.0  38.500000   66.0  28.0  3.000000  3.000000  2.853755  2.00000  5.00000  4.000000  4.000000  1.755102  1.582474  4.707547  3.000000  5.000000  45.0   8.400000  2.037037  3.019608  2.0  11300.0  0.0  0.0  2.0\n",
       "1    1.0  1.0   534817.0  39.200000   88.0  20.0  2.348361  2.017316  4.000000  1.00000  3.00000  4.000000  2.000000  1.755102  1.582474  4.707547  4.000000  2.000000  50.0  85.000000  2.000000  2.000000  3.0   2208.0  0.0  0.0  2.0\n",
       "2    2.0  1.0   530334.0  38.300000   40.0  24.0  1.000000  1.000000  3.000000  1.00000  3.00000  3.000000  1.000000  1.755102  1.582474  4.707547  1.000000  1.000000  33.0   6.700000  2.037037  3.019608  1.0      0.0  0.0  0.0  1.0\n",
       "3    1.0  9.0  5290409.0  39.100000  164.0  84.0  4.000000  1.000000  6.000000  2.00000  2.00000  4.000000  4.000000  1.000000  2.000000  5.000000  3.000000  3.692308  48.0   7.200000  3.000000  5.300000  2.0   2208.0  0.0  0.0  1.0\n",
       "4    2.0  1.0   530255.0  37.300000  104.0  35.0  2.348361  2.017316  6.000000  2.00000  2.95102  2.917969  2.266393  1.755102  1.582474  4.707547  2.757576  3.692308  74.0   7.400000  2.037037  3.019608  2.0   4300.0  0.0  0.0  2.0\n",
       "..   ...  ...        ...        ...    ...   ...       ...       ...       ...      ...      ...       ...       ...       ...       ...       ...       ...       ...   ...        ...       ...       ...  ...      ...  ...  ...  ...\n",
       "295  1.0  1.0   533886.0  38.167917  120.0  70.0  4.000000  2.017316  4.000000  2.00000  2.00000  4.000000  2.266393  1.755102  1.582474  4.707547  2.757576  5.000000  55.0  65.000000  2.037037  3.019608  3.0   3205.0  0.0  0.0  2.0\n",
       "296  2.0  1.0   527702.0  37.200000   72.0  24.0  3.000000  2.000000  4.000000  2.00000  4.00000  3.000000  3.000000  3.000000  1.000000  4.707547  4.000000  4.000000  44.0  24.456929  3.000000  3.300000  3.0   2208.0  0.0  0.0  1.0\n",
       "297  1.0  1.0   529386.0  37.500000   72.0  30.0  4.000000  3.000000  4.000000  1.00000  4.00000  4.000000  3.000000  2.000000  1.000000  4.707547  3.000000  5.000000  60.0   6.800000  2.037037  3.019608  2.0   3205.0  0.0  0.0  2.0\n",
       "298  1.0  1.0   530612.0  36.500000  100.0  24.0  3.000000  3.000000  3.000000  1.00000  3.00000  3.000000  3.000000  3.000000  1.000000  4.707547  4.000000  4.000000  50.0   6.000000  3.000000  3.400000  1.0   2208.0  0.0  0.0  1.0\n",
       "299  1.0  1.0   534618.0  37.200000   40.0  20.0  2.348361  2.017316  2.853755  1.30597  2.95102  2.917969  2.266393  1.755102  1.582474  4.707547  4.000000  1.000000  36.0  62.000000  1.000000  1.000000  3.0   6112.0  0.0  0.0  2.0\n",
       "\n",
       "[300 rows x 27 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bdaf0f36",
   "metadata": {},
   "outputs": [
    {
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       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>5290409</td>\n",
       "      <td>39.1</td>\n",
       "      <td>164.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2208</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>530255</td>\n",
       "      <td>37.3</td>\n",
       "      <td>104.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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",
       "      <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>295</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>533886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3205</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>527702</td>\n",
       "      <td>37.2</td>\n",
       "      <td>72.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2208</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>529386</td>\n",
       "      <td>37.5</td>\n",
       "      <td>72.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3205</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>530612</td>\n",
       "      <td>36.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2208</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>534618</td>\n",
       "      <td>37.2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6112</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     surgery  age       id  temp_rect  pulse    rr  peri_circ  peri_pulse  mm_clr  crt  pain_score  gut_pst  abd_dist  ngt  ng_rflx  rflx_ph  rct_feces  abd_obs   pcv  ttl_prot  abdct_app  abdct_prot  outcome  surg_ls  ls_type1  ls_type2  ls_type3  cp_data\n",
       "0        2.0    1   530101       38.5   66.0  28.0        3.0         3.0     NaN  2.0         5.0      4.0       4.0  NaN      NaN      NaN        3.0      5.0  45.0       8.4        NaN         NaN      2.0        2     11300         0         0        2\n",
       "1        1.0    1   534817       39.2   88.0  20.0        NaN         NaN     4.0  1.0         3.0      4.0       2.0  NaN      NaN      NaN        4.0      2.0  50.0      85.0        2.0         2.0      3.0        2      2208         0         0        2\n",
       "2        2.0    1   530334       38.3   40.0  24.0        1.0         1.0     3.0  1.0         3.0      3.0       1.0  NaN      NaN      NaN        1.0      1.0  33.0       6.7        NaN         NaN      1.0        2         0         0         0        1\n",
       "3        1.0    9  5290409       39.1  164.0  84.0        4.0         1.0     6.0  2.0         2.0      4.0       4.0  1.0      2.0      5.0        3.0      NaN  48.0       7.2        3.0         5.3      2.0        1      2208         0         0        1\n",
       "4        2.0    1   530255       37.3  104.0  35.0        NaN         NaN     6.0  2.0         NaN      NaN       NaN  NaN      NaN      NaN        NaN      NaN  74.0       7.4        NaN         NaN      2.0        2      4300         0         0        2\n",
       "..       ...  ...      ...        ...    ...   ...        ...         ...     ...  ...         ...      ...       ...  ...      ...      ...        ...      ...   ...       ...        ...         ...      ...      ...       ...       ...       ...      ...\n",
       "295      1.0    1   533886        NaN  120.0  70.0        4.0         NaN     4.0  2.0         2.0      4.0       NaN  NaN      NaN      NaN        NaN      5.0  55.0      65.0        NaN         NaN      3.0        2      3205         0         0        2\n",
       "296      2.0    1   527702       37.2   72.0  24.0        3.0         2.0     4.0  2.0         4.0      3.0       3.0  3.0      1.0      NaN        4.0      4.0  44.0       NaN        3.0         3.3      3.0        1      2208         0         0        1\n",
       "297      1.0    1   529386       37.5   72.0  30.0        4.0         3.0     4.0  1.0         4.0      4.0       3.0  2.0      1.0      NaN        3.0      5.0  60.0       6.8        NaN         NaN      2.0        1      3205         0         0        2\n",
       "298      1.0    1   530612       36.5  100.0  24.0        3.0         3.0     3.0  1.0         3.0      3.0       3.0  3.0      1.0      NaN        4.0      4.0  50.0       6.0        3.0         3.4      1.0        1      2208         0         0        1\n",
       "299      1.0    1   534618       37.2   40.0  20.0        NaN         NaN     NaN  NaN         NaN      NaN       NaN  NaN      NaN      NaN        4.0      1.0  36.0      62.0        1.0         1.0      3.0        2      6112         0         0        2\n",
       "\n",
       "[300 rows x 28 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "horse_colic_final"
   ]
  }
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