Data_Preprocessing_task (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mqZnz08dcsCk"
   },
   "source": [
    "# Data Preprocessing\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FSVzvQvDcxJZ"
   },
   "source": [
    "Attached to this notebook is a dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_wAcvfmHc6GS"
   },
   "source": [
    "1. Import the dataset into your notebook, print out the first 10 rows, check the statistical and general information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "dataset=pd.read_csv(\"hepatitis.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>steroid</th>\n",
       "      <th>antivirals</th>\n",
       "      <th>fatigue</th>\n",
       "      <th>malaise</th>\n",
       "      <th>anorexia</th>\n",
       "      <th>liver_big</th>\n",
       "      <th>liver_firm</th>\n",
       "      <th>spleen_palpable</th>\n",
       "      <th>spiders</th>\n",
       "      <th>ascites</th>\n",
       "      <th>varices</th>\n",
       "      <th>bilirubin</th>\n",
       "      <th>alk_phosphate</th>\n",
       "      <th>sgot</th>\n",
       "      <th>albumin</th>\n",
       "      <th>protime</th>\n",
       "      <th>histology</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>male</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>female</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.9</td>\n",
       "      <td>135.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78</td>\n",
       "      <td>female</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>0.7</td>\n",
       "      <td>96.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.7</td>\n",
       "      <td>46.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>False</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34</td>\n",
       "      <td>female</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex steroid  antivirals fatigue malaise anorexia liver_big  \\\n",
       "0   30    male   False       False   False   False    False     False   \n",
       "1   50  female   False       False    True   False    False     False   \n",
       "2   78  female    True       False    True   False    False      True   \n",
       "3   31  female     NaN        True   False   False    False      True   \n",
       "4   34  female    True       False   False   False    False      True   \n",
       "\n",
       "  liver_firm spleen_palpable spiders ascites varices  bilirubin  \\\n",
       "0      False           False   False   False   False        1.0   \n",
       "1      False           False   False   False   False        0.9   \n",
       "2      False           False   False   False   False        0.7   \n",
       "3      False           False   False   False   False        0.7   \n",
       "4      False           False   False   False   False        1.0   \n",
       "\n",
       "   alk_phosphate   sgot  albumin  protime  histology class  \n",
       "0           85.0   18.0      4.0      NaN      False  live  \n",
       "1          135.0   42.0      3.5      NaN      False  live  \n",
       "2           96.0   32.0      4.0      NaN      False  live  \n",
       "3           46.0   52.0      4.0     80.0      False  live  \n",
       "4            NaN  200.0      4.0      NaN      False  live  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>steroid</th>\n",
       "      <th>antivirals</th>\n",
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       "      <th>malaise</th>\n",
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       "      <th>liver_big</th>\n",
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       "      <th>spiders</th>\n",
       "      <th>ascites</th>\n",
       "      <th>varices</th>\n",
       "      <th>bilirubin</th>\n",
       "      <th>alk_phosphate</th>\n",
       "      <th>sgot</th>\n",
       "      <th>albumin</th>\n",
       "      <th>protime</th>\n",
       "      <th>histology</th>\n",
       "      <th>class</th>\n",
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       "  </thead>\n",
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       "      <td>3.5</td>\n",
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       "      <td>96.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>live</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>live</td>\n",
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       "      <th>4</th>\n",
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       "      <td>female</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34</td>\n",
       "      <td>female</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.9</td>\n",
       "      <td>95.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>False</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>51</td>\n",
       "      <td>female</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>die</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23</td>\n",
       "      <td>female</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>39</td>\n",
       "      <td>female</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>0.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>female</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <td>120.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>39</td>\n",
       "      <td>female</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.3</td>\n",
       "      <td>78.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>85.0</td>\n",
       "      <td>False</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age     sex steroid  antivirals fatigue malaise anorexia liver_big  \\\n",
       "0    30    male   False       False   False   False    False     False   \n",
       "1    50  female   False       False    True   False    False     False   \n",
       "2    78  female    True       False    True   False    False      True   \n",
       "3    31  female     NaN        True   False   False    False      True   \n",
       "4    34  female    True       False   False   False    False      True   \n",
       "5    34  female    True       False   False   False    False      True   \n",
       "6    51  female   False       False    True   False     True      True   \n",
       "7    23  female    True       False   False   False    False      True   \n",
       "8    39  female    True       False    True   False    False      True   \n",
       "9    30  female    True       False   False   False    False      True   \n",
       "10   39  female   False        True   False   False    False     False   \n",
       "\n",
       "   liver_firm spleen_palpable spiders ascites varices  bilirubin  \\\n",
       "0       False           False   False   False   False        1.0   \n",
       "1       False           False   False   False   False        0.9   \n",
       "2       False           False   False   False   False        0.7   \n",
       "3       False           False   False   False   False        0.7   \n",
       "4       False           False   False   False   False        1.0   \n",
       "5       False           False   False   False   False        0.9   \n",
       "6       False            True    True   False   False        NaN   \n",
       "7       False           False   False   False   False        1.0   \n",
       "8        True           False   False   False   False        0.7   \n",
       "9       False           False   False   False   False        1.0   \n",
       "10       True           False   False   False   False        1.3   \n",
       "\n",
       "    alk_phosphate   sgot  albumin  protime  histology class  \n",
       "0            85.0   18.0      4.0      NaN      False  live  \n",
       "1           135.0   42.0      3.5      NaN      False  live  \n",
       "2            96.0   32.0      4.0      NaN      False  live  \n",
       "3            46.0   52.0      4.0     80.0      False  live  \n",
       "4             NaN  200.0      4.0      NaN      False  live  \n",
       "5            95.0   28.0      4.0     75.0      False  live  \n",
       "6             NaN    NaN      NaN      NaN      False   die  \n",
       "7             NaN    NaN      NaN      NaN      False  live  \n",
       "8             NaN   48.0      4.4      NaN      False  live  \n",
       "9             NaN  120.0      3.9      NaN      False  live  \n",
       "10           78.0   30.0      4.4     85.0      False  live  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.loc[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 155 entries, 0 to 154\n",
      "Data columns (total 20 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   age              155 non-null    int64  \n",
      " 1   sex              155 non-null    object \n",
      " 2   steroid          154 non-null    object \n",
      " 3   antivirals       155 non-null    bool   \n",
      " 4   fatigue          154 non-null    object \n",
      " 5   malaise          154 non-null    object \n",
      " 6   anorexia         154 non-null    object \n",
      " 7   liver_big        145 non-null    object \n",
      " 8   liver_firm       144 non-null    object \n",
      " 9   spleen_palpable  150 non-null    object \n",
      " 10  spiders          150 non-null    object \n",
      " 11  ascites          150 non-null    object \n",
      " 12  varices          150 non-null    object \n",
      " 13  bilirubin        149 non-null    float64\n",
      " 14  alk_phosphate    126 non-null    float64\n",
      " 15  sgot             151 non-null    float64\n",
      " 16  albumin          139 non-null    float64\n",
      " 17  protime          88 non-null     float64\n",
      " 18  histology        155 non-null    bool   \n",
      " 19  class            155 non-null    object \n",
      "dtypes: bool(2), float64(5), int64(1), object(12)\n",
      "memory usage: 22.2+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F0x4K5dydOCs"
   },
   "source": [
    "2. Check for the sum of the missing values and the percentage of missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 0\n",
       "sex                 0\n",
       "steroid             1\n",
       "antivirals          0\n",
       "fatigue             1\n",
       "malaise             1\n",
       "anorexia            1\n",
       "liver_big          10\n",
       "liver_firm         11\n",
       "spleen_palpable     5\n",
       "spiders             5\n",
       "ascites             5\n",
       "varices             5\n",
       "bilirubin           6\n",
       "alk_phosphate      29\n",
       "sgot                4\n",
       "albumin            16\n",
       "protime            67\n",
       "histology           0\n",
       "class               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 0.000000\n",
       "sex                 0.000000\n",
       "steroid             0.645161\n",
       "antivirals          0.000000\n",
       "fatigue             0.645161\n",
       "malaise             0.645161\n",
       "anorexia            0.645161\n",
       "liver_big           6.451613\n",
       "liver_firm          7.096774\n",
       "spleen_palpable     3.225806\n",
       "spiders             3.225806\n",
       "ascites             3.225806\n",
       "varices             3.225806\n",
       "bilirubin           3.870968\n",
       "alk_phosphate      18.709677\n",
       "sgot                2.580645\n",
       "albumin            10.322581\n",
       "protime            43.225806\n",
       "histology           0.000000\n",
       "class               0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isna().sum() *100/ len(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HT72LLKIdVZ9"
   },
   "source": [
    "3. Choose one option: Drop the columns with missing values or fill the columns with missing values. Give your reasons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>True</td>\n",
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       "      <td>95.0</td>\n",
       "      <td>200.0</td>\n",
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       "      <td>75.0</td>\n",
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       "      <td>20.0</td>\n",
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       "      <td>live</td>\n",
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       "      <td>53</td>\n",
       "      <td>male</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>81.0</td>\n",
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       "      <td>48.0</td>\n",
       "      <td>True</td>\n",
       "      <td>live</td>\n",
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       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>43</td>\n",
       "      <td>female</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>True</td>\n",
       "      <td>die</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age     sex  steroid  antivirals  fatigue  malaise  anorexia  liver_big  \\\n",
       "0     30    male    False       False    False    False     False      False   \n",
       "1     50  female    False       False     True    False     False      False   \n",
       "2     78  female     True       False     True    False     False       True   \n",
       "3     31  female     True        True    False    False     False       True   \n",
       "4     34  female     True       False    False    False     False       True   \n",
       "..   ...     ...      ...         ...      ...      ...       ...        ...   \n",
       "150   46  female     True       False     True     True      True       True   \n",
       "151   44  female     True       False     True    False     False       True   \n",
       "152   61  female    False       False     True     True     False      False   \n",
       "153   53    male    False       False     True    False     False       True   \n",
       "154   43  female     True       False     True    False     False       True   \n",
       "\n",
       "     liver_firm  spleen_palpable  spiders  ascites  varices  bilirubin  \\\n",
       "0         False            False    False    False    False        1.0   \n",
       "1         False            False    False    False    False        0.9   \n",
       "2         False            False    False    False    False        0.7   \n",
       "3         False            False    False    False    False        0.7   \n",
       "4         False            False    False    False    False        1.0   \n",
       "..          ...              ...      ...      ...      ...        ...   \n",
       "150       False            False     True     True     True        7.6   \n",
       "151        True            False    False    False    False        0.9   \n",
       "152        True            False     True    False    False        0.8   \n",
       "153       False             True     True    False     True        1.5   \n",
       "154       False             True     True     True    False        1.2   \n",
       "\n",
       "     alk_phosphate   sgot  albumin  protime  histology class  \n",
       "0             85.0   18.0      4.0     80.0      False  live  \n",
       "1            135.0   42.0      3.5     80.0      False  live  \n",
       "2             96.0   32.0      4.0     80.0      False  live  \n",
       "3             46.0   52.0      4.0     80.0      False  live  \n",
       "4             95.0  200.0      4.0     75.0      False  live  \n",
       "..             ...    ...      ...      ...        ...   ...  \n",
       "150          126.0  242.0      3.3     50.0       True   die  \n",
       "151          126.0  142.0      4.3     48.0       True  live  \n",
       "152           75.0   20.0      4.1     48.0       True  live  \n",
       "153           81.0   19.0      4.1     48.0       True  live  \n",
       "154          100.0   19.0      3.1     42.0       True   die  \n",
       "\n",
       "[155 rows x 20 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "work1=dataset.fillna(method=\"bfill\")\n",
    "work1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#I choose to fill it because there is alot of missing data so dropping the missing values will leave me with few data which will not give me a good information  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oa1Ic98AdqF0"
   },
   "source": [
    "4. Choose a column and rename it. Give your reasons for picking that column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.9</td>\n",
       "      <td>135.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>False</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78</td>\n",
       "      <td>female</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.7</td>\n",
       "      <td>96.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>False</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31</td>\n",
       "      <td>female</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.7</td>\n",
       "      <td>46.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>False</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34</td>\n",
       "      <td>female</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>False</td>\n",
       "      <td>live</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>46</td>\n",
       "      <td>female</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>7.6</td>\n",
       "      <td>126.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>50.0</td>\n",
       "      <td>True</td>\n",
       "      <td>die</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>44</td>\n",
       "      <td>female</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.9</td>\n",
       "      <td>126.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>48.0</td>\n",
       "      <td>True</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>61</td>\n",
       "      <td>female</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8</td>\n",
       "      <td>75.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>True</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>53</td>\n",
       "      <td>male</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.5</td>\n",
       "      <td>81.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>True</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>43</td>\n",
       "      <td>female</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>True</td>\n",
       "      <td>die</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age     sex  steroid  antivirals  fatigue  malaise  anorexia  liver_big  \\\n",
       "0     30    male    False       False    False    False     False      False   \n",
       "1     50  female    False       False     True    False     False      False   \n",
       "2     78  female     True       False     True    False     False       True   \n",
       "3     31  female     True        True    False    False     False       True   \n",
       "4     34  female     True       False    False    False     False       True   \n",
       "..   ...     ...      ...         ...      ...      ...       ...        ...   \n",
       "150   46  female     True       False     True     True      True       True   \n",
       "151   44  female     True       False     True    False     False       True   \n",
       "152   61  female    False       False     True     True     False      False   \n",
       "153   53    male    False       False     True    False     False       True   \n",
       "154   43  female     True       False     True    False     False       True   \n",
       "\n",
       "     liver_firm  spleen_palpable  spiders  ascites  varices  bilirubin  \\\n",
       "0         False            False    False    False    False        1.0   \n",
       "1         False            False    False    False    False        0.9   \n",
       "2         False            False    False    False    False        0.7   \n",
       "3         False            False    False    False    False        0.7   \n",
       "4         False            False    False    False    False        1.0   \n",
       "..          ...              ...      ...      ...      ...        ...   \n",
       "150       False            False     True     True     True        7.6   \n",
       "151        True            False    False    False    False        0.9   \n",
       "152        True            False     True    False    False        0.8   \n",
       "153       False             True     True    False     True        1.5   \n",
       "154       False             True     True     True    False        1.2   \n",
       "\n",
       "     alk_phosphate   sgot  albumin  protime  histology status  \n",
       "0             85.0   18.0      4.0     80.0      False   live  \n",
       "1            135.0   42.0      3.5     80.0      False   live  \n",
       "2             96.0   32.0      4.0     80.0      False   live  \n",
       "3             46.0   52.0      4.0     80.0      False   live  \n",
       "4             95.0  200.0      4.0     75.0      False   live  \n",
       "..             ...    ...      ...      ...        ...    ...  \n",
       "150          126.0  242.0      3.3     50.0       True    die  \n",
       "151          126.0  142.0      4.3     48.0       True   live  \n",
       "152           75.0   20.0      4.1     48.0       True   live  \n",
       "153           81.0   19.0      4.1     48.0       True   live  \n",
       "154          100.0   19.0      3.1     42.0       True    die  \n",
       "\n",
       "[155 rows x 20 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "work1.rename(columns={\"class\":\"status\"},inplace=True)\n",
    "work1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# looking at the file, the \"class\" doesnt really explain if the person is alive or dead so Status is a more suitable name. Meanwhile other columns has the name of a particular thing present in the patient and changing their name will alter the dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tP20rNjJe92B"
   },
   "source": [
    "5. Check the number of missing values after cleaning and state the impact on data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                0\n",
       "sex                0\n",
       "steroid            0\n",
       "antivirals         0\n",
       "fatigue            0\n",
       "malaise            0\n",
       "anorexia           0\n",
       "liver_big          0\n",
       "liver_firm         0\n",
       "spleen_palpable    0\n",
       "spiders            0\n",
       "ascites            0\n",
       "varices            0\n",
       "bilirubin          0\n",
       "alk_phosphate      0\n",
       "sgot               0\n",
       "albumin            0\n",
       "protime            0\n",
       "histology          0\n",
       "status             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "work1.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#After cleaning the datase, the data is neater now and can be fully analysed. The dataset now shows good information which the analyst needs "
   ]
  }
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