Data Cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exasens Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Importing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Diagnosis</th>\n",
       "      <th>ID</th>\n",
       "      <th>Imaginary Part</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Real Part</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Min</td>\n",
       "      <td>Avg.</td>\n",
       "      <td>Min</td>\n",
       "      <td>Avg.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gender</td>\n",
       "      <td>Somking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>COPD</td>\n",
       "      <td>301-4</td>\n",
       "      <td>-320.61</td>\n",
       "      <td>-300.5635307</td>\n",
       "      <td>-495.26</td>\n",
       "      <td>-464.1719907</td>\n",
       "      <td>1.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male=1</td>\n",
       "      <td>Non-smoker=1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>COPD</td>\n",
       "      <td>302-3</td>\n",
       "      <td>-325.39</td>\n",
       "      <td>-314.7503595</td>\n",
       "      <td>-473.73</td>\n",
       "      <td>-469.2631404</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female=0</td>\n",
       "      <td>Ex-smoker=2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>COPD</td>\n",
       "      <td>303-3</td>\n",
       "      <td>-323</td>\n",
       "      <td>-317.4360556</td>\n",
       "      <td>-476.12</td>\n",
       "      <td>-471.8976667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Active-smoker=3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Diagnosis     ID Imaginary Part    Unnamed: 3 Real Part    Unnamed: 5  \\\n",
       "0       NaN    NaN            NaN           NaN       NaN           NaN   \n",
       "1       NaN    NaN           Min          Avg.       Min          Avg.    \n",
       "2      COPD  301-4        -320.61  -300.5635307   -495.26  -464.1719907   \n",
       "3      COPD  302-3        -325.39  -314.7503595   -473.73  -469.2631404   \n",
       "4      COPD  303-3           -323  -317.4360556   -476.12  -471.8976667   \n",
       "\n",
       "   Gender   Age  Smoking  Unnamed: 9  Unnamed: 10 Unnamed: 11      Unnamed: 12  \n",
       "0     NaN   NaN      NaN         NaN          NaN         NaN              NaN  \n",
       "1     NaN   NaN      NaN         NaN          NaN     Gender           Somking  \n",
       "2     1.0  77.0      2.0         NaN          NaN      Male=1     Non-smoker=1  \n",
       "3     0.0  72.0      2.0         NaN          NaN    Female=0      Ex-smoker=2  \n",
       "4     1.0  73.0      3.0         NaN          NaN         NaN  Active-smoker=3  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('Data/Exasens.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop([0,1],inplace=True)\n",
    "data.drop(['Unnamed: 9','Unnamed: 10','Unnamed: 11','Unnamed: 12'], axis = 1,inplace=True)\n",
    "data.reset_index(drop=True, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.rename(columns={'Imaginary Part':'Imaginary Part: Min','Unnamed: 3':'Imaginary Part: Avg','Real Part':'Real Part: Min','Unnamed: 5':'Real Part: Avg'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Diagnosis</th>\n",
       "      <th>ID</th>\n",
       "      <th>Imaginary Part: Min</th>\n",
       "      <th>Imaginary Part: Avg</th>\n",
       "      <th>Real Part: Min</th>\n",
       "      <th>Real Part: Avg</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Smoking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>COPD</td>\n",
       "      <td>301-4</td>\n",
       "      <td>-320.61</td>\n",
       "      <td>-300.5635307</td>\n",
       "      <td>-495.26</td>\n",
       "      <td>-464.1719907</td>\n",
       "      <td>1.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>COPD</td>\n",
       "      <td>302-3</td>\n",
       "      <td>-325.39</td>\n",
       "      <td>-314.7503595</td>\n",
       "      <td>-473.73</td>\n",
       "      <td>-469.2631404</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>COPD</td>\n",
       "      <td>303-3</td>\n",
       "      <td>-323</td>\n",
       "      <td>-317.4360556</td>\n",
       "      <td>-476.12</td>\n",
       "      <td>-471.8976667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>COPD</td>\n",
       "      <td>304-4</td>\n",
       "      <td>-327.78</td>\n",
       "      <td>-317.3996698</td>\n",
       "      <td>-473.73</td>\n",
       "      <td>-468.856388</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>COPD</td>\n",
       "      <td>305-4</td>\n",
       "      <td>-325.39</td>\n",
       "      <td>-316.1557853</td>\n",
       "      <td>-478.52</td>\n",
       "      <td>-472.8697828</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Diagnosis     ID Imaginary Part: Min Imaginary Part: Avg Real Part: Min  \\\n",
       "0      COPD  301-4             -320.61        -300.5635307        -495.26   \n",
       "1      COPD  302-3             -325.39        -314.7503595        -473.73   \n",
       "2      COPD  303-3                -323        -317.4360556        -476.12   \n",
       "3      COPD  304-4             -327.78        -317.3996698        -473.73   \n",
       "4      COPD  305-4             -325.39        -316.1557853        -478.52   \n",
       "\n",
       "  Real Part: Avg  Gender   Age  Smoking  \n",
       "0   -464.1719907     1.0  77.0      2.0  \n",
       "1   -469.2631404     0.0  72.0      2.0  \n",
       "2   -471.8976667     1.0  73.0      3.0  \n",
       "3    -468.856388     1.0  76.0      2.0  \n",
       "4   -472.8697828     0.0  65.0      2.0  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning and Balancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 399 entries, 0 to 398\n",
      "Data columns (total 9 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Diagnosis            399 non-null    object \n",
      " 1   ID                   399 non-null    object \n",
      " 2   Imaginary Part: Min  100 non-null    object \n",
      " 3   Imaginary Part: Avg  100 non-null    object \n",
      " 4   Real Part: Min       100 non-null    object \n",
      " 5   Real Part: Avg       100 non-null    object \n",
      " 6   Gender               399 non-null    float64\n",
      " 7   Age                  399 non-null    float64\n",
      " 8   Smoking              399 non-null    float64\n",
      "dtypes: float64(3), object(6)\n",
      "memory usage: 28.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[['Imaginary Part: Min','Imaginary Part: Avg','Real Part: Min','Real Part: Avg']] = data[['Imaginary Part: Min','Imaginary Part: Avg','Real Part: Min','Real Part: Avg']].astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 399 entries, 0 to 398\n",
      "Data columns (total 9 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Diagnosis            399 non-null    object \n",
      " 1   ID                   399 non-null    object \n",
      " 2   Imaginary Part: Min  100 non-null    float64\n",
      " 3   Imaginary Part: Avg  100 non-null    float64\n",
      " 4   Real Part: Min       100 non-null    float64\n",
      " 5   Real Part: Avg       100 non-null    float64\n",
      " 6   Gender               399 non-null    float64\n",
      " 7   Age                  399 non-null    float64\n",
      " 8   Smoking              399 non-null    float64\n",
      "dtypes: float64(7), object(2)\n",
      "memory usage: 28.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Imaginary Part: Min</th>\n",
       "      <th>Imaginary Part: Avg</th>\n",
       "      <th>Real Part: Min</th>\n",
       "      <th>Real Part: Avg</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Smoking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>399.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-314.941800</td>\n",
       "      <td>-304.779677</td>\n",
       "      <td>-473.002200</td>\n",
       "      <td>-458.701666</td>\n",
       "      <td>0.398496</td>\n",
       "      <td>48.736842</td>\n",
       "      <td>1.726817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>28.422753</td>\n",
       "      <td>25.834316</td>\n",
       "      <td>49.009819</td>\n",
       "      <td>43.725345</td>\n",
       "      <td>0.490203</td>\n",
       "      <td>18.566962</td>\n",
       "      <td>0.742167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-337.350000</td>\n",
       "      <td>-328.280985</td>\n",
       "      <td>-626.860000</td>\n",
       "      <td>-473.928698</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-327.780000</td>\n",
       "      <td>-318.476799</td>\n",
       "      <td>-476.120000</td>\n",
       "      <td>-470.036232</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-323.000000</td>\n",
       "      <td>-314.327176</td>\n",
       "      <td>-473.730000</td>\n",
       "      <td>-467.136002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-320.610000</td>\n",
       "      <td>-305.072319</td>\n",
       "      <td>-468.950000</td>\n",
       "      <td>-462.804774</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-225.000000</td>\n",
       "      <td>-225.000000</td>\n",
       "      <td>-44.000000</td>\n",
       "      <td>-44.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Imaginary Part: Min  Imaginary Part: Avg  Real Part: Min  \\\n",
       "count           100.000000           100.000000      100.000000   \n",
       "mean           -314.941800          -304.779677     -473.002200   \n",
       "std              28.422753            25.834316       49.009819   \n",
       "min            -337.350000          -328.280985     -626.860000   \n",
       "25%            -327.780000          -318.476799     -476.120000   \n",
       "50%            -323.000000          -314.327176     -473.730000   \n",
       "75%            -320.610000          -305.072319     -468.950000   \n",
       "max            -225.000000          -225.000000      -44.000000   \n",
       "\n",
       "       Real Part: Avg      Gender         Age     Smoking  \n",
       "count      100.000000  399.000000  399.000000  399.000000  \n",
       "mean      -458.701666    0.398496   48.736842    1.726817  \n",
       "std         43.725345    0.490203   18.566962    0.742167  \n",
       "min       -473.928698    0.000000   17.000000    1.000000  \n",
       "25%       -470.036232    0.000000   31.000000    1.000000  \n",
       "50%       -467.136002    0.000000   49.000000    2.000000  \n",
       "75%       -462.804774    1.000000   64.000000    2.000000  \n",
       "max        -44.000000    1.000000   93.000000    3.000000  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull(),cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is this Imaginary Part/Real Part?\n",
    "6- Saliva Permittivity :\n",
    "\n",
    "some technical crap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "COPD        40.0\n",
       "HC          40.0\n",
       "Asthma      10.0\n",
       "Infected    10.0\n",
       "Name: Diagnosis, dtype: float64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for class imbalance?\n",
    "data['Diagnosis'].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(['Diagnosis','ID'], axis = 1)\n",
    "y = data['Diagnosis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Balancing the classes using smote\n",
    "# smoter = SMOTE()\n",
    "# X_smoted, y_smoted = smoter.fit_resample(X,y)\n",
    "# smoted_data = pd.concat([pd.DataFrame(X_smoted, columns=X.columns), pd.Series(y_smoted, name='Diagnosis')], axis=1)\n",
    "\n",
    "# # Display the resampled DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Now our data is balanced\n",
    "# smoted_data['Diagnosis'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# smoted_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "data['Diagnosis'] = label_encoder.fit_transform(data['Diagnosis'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Imaginary Part: Min</th>\n",
       "      <th>Imaginary Part: Avg</th>\n",
       "      <th>Real Part: Min</th>\n",
       "      <th>Real Part: Avg</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>Diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-320.61</td>\n",
       "      <td>-300.563531</td>\n",
       "      <td>-495.26</td>\n",
       "      <td>-464.171991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-325.39</td>\n",
       "      <td>-314.750360</td>\n",
       "      <td>-473.73</td>\n",
       "      <td>-469.263140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-323.00</td>\n",
       "      <td>-317.436056</td>\n",
       "      <td>-476.12</td>\n",
       "      <td>-471.897667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-327.78</td>\n",
       "      <td>-317.399670</td>\n",
       "      <td>-473.73</td>\n",
       "      <td>-468.856388</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-325.39</td>\n",
       "      <td>-316.155785</td>\n",
       "      <td>-478.52</td>\n",
       "      <td>-472.869783</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Imaginary Part: Min  Imaginary Part: Avg  Real Part: Min  Real Part: Avg  \\\n",
       "0              -320.61          -300.563531         -495.26     -464.171991   \n",
       "1              -325.39          -314.750360         -473.73     -469.263140   \n",
       "2              -323.00          -317.436056         -476.12     -471.897667   \n",
       "3              -327.78          -317.399670         -473.73     -468.856388   \n",
       "4              -325.39          -316.155785         -478.52     -472.869783   \n",
       "\n",
       "   Gender   Age  Smoking  Diagnosis  \n",
       "0     1.0  77.0      2.0          1  \n",
       "1     0.0  72.0      2.0          1  \n",
       "2     1.0  73.0      3.0          1  \n",
       "3     1.0  76.0      2.0          1  \n",
       "4     0.0  65.0      2.0          1  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = data['Diagnosis'].copy()\n",
    "data.drop(['Diagnosis','ID'], axis = 1,inplace=True)\n",
    "data['Diagnosis'] = temp\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('Data/cleaned_data.csv', index=False)"
   ]
  }
 ],
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