liver-disease-detection.ipynb

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   "source": [
    "# Analysis and Prediction-Indian Liver Patients."
   ]
  },
  {
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   "execution_count": 1,
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   "source": []
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   "source": [
    "The dataset was downloaded from the UCI ML Repository.\n",
    "\n",
    "Here are the steps I'm going to perform:  \n",
    "1) Data Analysis: This is in general looking at the data to figure out whats going on. Inspect the data: Check whether there is any missing data, irrelevant data and do a cleanup.  \n",
    "2) Data Visualization:   \n",
    "3) Feature selection.  \n",
    "4) Search for any trends, relations & correlations.  \n",
    "5) Draw an inference and predict whether the patient can be identified to be having liver disease or not  "
   ]
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   "source": [
    "#Import all required libraries for reading data, analysing and visualizing data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
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    "_uuid": "3b99a454b4a233394a3fa3c8a5190b198cbc84ba"
   },
   "source": [
    "# Data Analysis"
   ]
  },
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   "execution_count": 2,
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   "outputs": [],
   "source": [
    "#Read the training & test data\n",
    "liver_df = pd.read_csv('../input/indian_liver_patient.csv')"
   ]
  },
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   "source": [
    "This data set contains 416 liver patient records and 167 non liver patient records collected from North East of Andhra Pradesh, India. The \"Dataset\" column is a class label used to divide groups into liver patient (liver disease) or not (no disease). "
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
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       "      <th>1</th>\n",
       "      <td>62</td>\n",
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       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>62</td>\n",
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       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>58</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
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       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0   65  Female              0.7               0.1                   187   \n",
       "1   62    Male             10.9               5.5                   699   \n",
       "2   62    Male              7.3               4.1                   490   \n",
       "3   58    Male              1.0               0.4                   182   \n",
       "4   72    Male              3.9               2.0                   195   \n",
       "\n",
       "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                        16                          18             6.8   \n",
       "1                        64                         100             7.5   \n",
       "2                        60                          68             7.0   \n",
       "3                        14                          20             6.8   \n",
       "4                        27                          59             7.3   \n",
       "\n",
       "   Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "0      3.3                        0.90        1  \n",
       "1      3.2                        0.74        1  \n",
       "2      3.3                        0.89        1  \n",
       "3      3.4                        1.00        1  \n",
       "4      2.4                        0.40        1  "
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   "source": [
    "liver_df.head()"
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 583 entries, 0 to 582\n",
      "Data columns (total 11 columns):\n",
      "Age                           583 non-null int64\n",
      "Gender                        583 non-null object\n",
      "Total_Bilirubin               583 non-null float64\n",
      "Direct_Bilirubin              583 non-null float64\n",
      "Alkaline_Phosphotase          583 non-null int64\n",
      "Alamine_Aminotransferase      583 non-null int64\n",
      "Aspartate_Aminotransferase    583 non-null int64\n",
      "Total_Protiens                583 non-null float64\n",
      "Albumin                       583 non-null float64\n",
      "Albumin_and_Globulin_Ratio    579 non-null float64\n",
      "Dataset                       583 non-null int64\n",
      "dtypes: float64(5), int64(5), object(1)\n",
      "memory usage: 50.2+ KB\n"
     ]
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   "source": [
    "liver_df.info()"
   ]
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   "source": [
    "Here is the observation from the dataset:   \n",
    "1) Only gender is non-numeric veriable. All others are numeric.   \n",
    "2) There are 10 features and 1 output - dataset. Value 1 indicates that the patient has liver disease and 0 indicates the patient does not have liver disease.  "
   ]
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       "      <td>583.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>441</td>\n",
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       "      <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",
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       "      <th>mean</th>\n",
       "      <td>44.746141</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.298799</td>\n",
       "      <td>1.486106</td>\n",
       "      <td>290.576329</td>\n",
       "      <td>80.713551</td>\n",
       "      <td>109.910806</td>\n",
       "      <td>6.483190</td>\n",
       "      <td>3.141852</td>\n",
       "      <td>0.947064</td>\n",
       "      <td>1.286449</td>\n",
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       "      <th>std</th>\n",
       "      <td>16.189833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.209522</td>\n",
       "      <td>2.808498</td>\n",
       "      <td>242.937989</td>\n",
       "      <td>182.620356</td>\n",
       "      <td>288.918529</td>\n",
       "      <td>1.085451</td>\n",
       "      <td>0.795519</td>\n",
       "      <td>0.319592</td>\n",
       "      <td>0.452490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>175.500000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>298.000000</td>\n",
       "      <td>60.500000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>2.000000</td>\n",
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       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>2110.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>4929.000000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.000000</td>\n",
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      "text/plain": [
       "               Age Gender  Total_Bilirubin  Direct_Bilirubin  \\\n",
       "count   583.000000    583       583.000000        583.000000   \n",
       "unique         NaN      2              NaN               NaN   \n",
       "top            NaN   Male              NaN               NaN   \n",
       "freq           NaN    441              NaN               NaN   \n",
       "mean     44.746141    NaN         3.298799          1.486106   \n",
       "std      16.189833    NaN         6.209522          2.808498   \n",
       "min       4.000000    NaN         0.400000          0.100000   \n",
       "25%      33.000000    NaN         0.800000          0.200000   \n",
       "50%      45.000000    NaN         1.000000          0.300000   \n",
       "75%      58.000000    NaN         2.600000          1.300000   \n",
       "max      90.000000    NaN        75.000000         19.700000   \n",
       "\n",
       "        Alkaline_Phosphotase  Alamine_Aminotransferase  \\\n",
       "count             583.000000                583.000000   \n",
       "unique                   NaN                       NaN   \n",
       "top                      NaN                       NaN   \n",
       "freq                     NaN                       NaN   \n",
       "mean              290.576329                 80.713551   \n",
       "std               242.937989                182.620356   \n",
       "min                63.000000                 10.000000   \n",
       "25%               175.500000                 23.000000   \n",
       "50%               208.000000                 35.000000   \n",
       "75%               298.000000                 60.500000   \n",
       "max              2110.000000               2000.000000   \n",
       "\n",
       "        Aspartate_Aminotransferase  Total_Protiens     Albumin  \\\n",
       "count                   583.000000      583.000000  583.000000   \n",
       "unique                         NaN             NaN         NaN   \n",
       "top                            NaN             NaN         NaN   \n",
       "freq                           NaN             NaN         NaN   \n",
       "mean                    109.910806        6.483190    3.141852   \n",
       "std                     288.918529        1.085451    0.795519   \n",
       "min                      10.000000        2.700000    0.900000   \n",
       "25%                      25.000000        5.800000    2.600000   \n",
       "50%                      42.000000        6.600000    3.100000   \n",
       "75%                      87.000000        7.200000    3.800000   \n",
       "max                    4929.000000        9.600000    5.500000   \n",
       "\n",
       "        Albumin_and_Globulin_Ratio     Dataset  \n",
       "count                   579.000000  583.000000  \n",
       "unique                         NaN         NaN  \n",
       "top                            NaN         NaN  \n",
       "freq                           NaN         NaN  \n",
       "mean                      0.947064    1.286449  \n",
       "std                       0.319592    0.452490  \n",
       "min                       0.300000    1.000000  \n",
       "25%                       0.700000    1.000000  \n",
       "50%                       0.930000    1.000000  \n",
       "75%                       1.100000    2.000000  \n",
       "max                       2.800000    2.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Describe gives statistical information about NUMERICAL columns in the dataset\n",
    "liver_df.describe(include='all')\n",
    "#We can see that there are missing values for Albumin_and_Globulin_Ratio as only 579 entries have valid values indicating 4 missing values.\n",
    "#Gender has only 2 values - Male/Female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "e379fc6e-e467-47bd-978c-c8eae32f3193",
    "_uuid": "a371d92f66a0641259d6053f2aaf33e3cfb64994",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:32.944296Z",
     "iopub.status.busy": "2023-05-27T13:28:32.943981Z",
     "iopub.status.idle": "2023-05-27T13:28:32.951424Z",
     "shell.execute_reply": "2023-05-27T13:28:32.950493Z",
     "shell.execute_reply.started": "2023-05-27T13:28:32.944239Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Gender', 'Total_Bilirubin', 'Direct_Bilirubin',\n",
       "       'Alkaline_Phosphotase', 'Alamine_Aminotransferase',\n",
       "       'Aspartate_Aminotransferase', 'Total_Protiens', 'Albumin',\n",
       "       'Albumin_and_Globulin_Ratio', 'Dataset'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Which features are available in the dataset?\n",
    "liver_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "b6d201e0-a049-462a-a4c3-e66fdd3c1ff1",
    "_uuid": "0ab1acc33cd91286825dba1d5d77fcbf171348d5",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:32.953519Z",
     "iopub.status.busy": "2023-05-27T13:28:32.953212Z",
     "iopub.status.idle": "2023-05-27T13:28:32.968048Z",
     "shell.execute_reply": "2023-05-27T13:28:32.967299Z",
     "shell.execute_reply.started": "2023-05-27T13:28:32.953466Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                           0\n",
       "Gender                        0\n",
       "Total_Bilirubin               0\n",
       "Direct_Bilirubin              0\n",
       "Alkaline_Phosphotase          0\n",
       "Alamine_Aminotransferase      0\n",
       "Aspartate_Aminotransferase    0\n",
       "Total_Protiens                0\n",
       "Albumin                       0\n",
       "Albumin_and_Globulin_Ratio    4\n",
       "Dataset                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for any null values\n",
    "liver_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1d6972b0-3916-4ec4-9c4e-050b4be86f0d",
    "_uuid": "5d2737231e0a088725b3403778a48e9c0ba2c629"
   },
   "source": [
    "The only data that is null is the Albumin_and_Globulin_Ratio - Only 4 rows are null. Lets see whether this is an important feature    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "888444b8-b4a9-45b6-b2c2-11b826ff9da7",
    "_uuid": "2d518d8a9f73f3ef58ebef6cd7c918a15178a656"
   },
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "544cd4dc-5cb5-45ac-ba26-c6c2be5d3db1",
    "_uuid": "0f20a9162e6c41a4f602f2bbcdfd21c012e035da",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:32.969255Z",
     "iopub.status.busy": "2023-05-27T13:28:32.969019Z",
     "iopub.status.idle": "2023-05-27T13:28:33.251325Z",
     "shell.execute_reply": "2023-05-27T13:28:33.250344Z",
     "shell.execute_reply.started": "2023-05-27T13:28:32.969219Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of patients diagnosed with liver disease:  416\n",
      "Number of patients not diagnosed with liver disease:  167\n"
     ]
    },
    {
     "data": {
      "image/png": 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kdRkQkqQuA0KS1GVASJK6DAhJUpcBIUnqMiAkSV0GhCSpa7AXBi0kyQPAY8CTwIGqmk7y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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x79302988def0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=liver_df, x = 'Dataset', label='Count')\n",
    "\n",
    "LD, NLD = liver_df['Dataset'].value_counts()\n",
    "print('Number of patients diagnosed with liver disease: ',LD)\n",
    "print('Number of patients not diagnosed with liver disease: ',NLD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "3e8bf602-3b81-4a53-b190-8ba363295904",
    "_uuid": "d0e41e3d9a88577572fe5fea081de4de19f6ad6a",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:33.253377Z",
     "iopub.status.busy": "2023-05-27T13:28:33.253010Z",
     "iopub.status.idle": "2023-05-27T13:28:33.380048Z",
     "shell.execute_reply": "2023-05-27T13:28:33.378839Z",
     "shell.execute_reply.started": "2023-05-27T13:28:33.253303Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of patients that are male:  441\n",
      "Number of patients that are female:  142\n"
     ]
    },
    {
     "data": {
      "image/png": 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HbubQe1fuvWC4sZnrPgW8anYliRfKtSVOB95cVdes1z7+GXzpRvY9Hzg+bRibZP9BKpyn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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x79302989d390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=liver_df, x = 'Gender', label='Count')\n",
    "\n",
    "M, F = liver_df['Gender'].value_counts()\n",
    "print('Number of patients that are male: ',M)\n",
    "print('Number of patients that are female: ',F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "dff35f12-ee87-43d2-a4f3-67ad7a12de76",
    "_uuid": "8b1e4c43e56c9eb7f571709bc896f3a9806dc1cd",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:33.382102Z",
     "iopub.status.busy": "2023-05-27T13:28:33.381728Z",
     "iopub.status.idle": "2023-05-27T13:28:33.722922Z",
     "shell.execute_reply": "2023-05-27T13:28:33.721915Z",
     "shell.execute_reply.started": "2023-05-27T13:28:33.382029Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MmuoqWPom/OcnsD18fWv2KZCc6m9dAkBaShI3TPKm/3QOrntuAXe+voSnP1lF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\nQSgigff/Aatfv4OADuiAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7930226fa4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"Age\", y=\"Gender\", hue=\"Dataset\", data=liver_df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d1f71ba8-caa8-47e2-b675-e234abfab05c",
    "_uuid": "059e213bb14b66c18d67a022679d485e00003e25"
   },
   "source": [
    "Age seems to be a factor for liver disease for both male and female genders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "8201c43b-3d29-4076-a385-d46f4d016670",
    "_uuid": "e39a78707778302672d3e058a2b386b53630c062",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:33.724524Z",
     "iopub.status.busy": "2023-05-27T13:28:33.724257Z",
     "iopub.status.idle": "2023-05-27T13:28:33.746363Z",
     "shell.execute_reply": "2023-05-27T13:28:33.745299Z",
     "shell.execute_reply.started": "2023-05-27T13:28:33.724476Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dataset  Gender  Age\n",
       "2        2  Female   50\n",
       "3        2    Male  117\n",
       "0        1  Female   92\n",
       "1        1    Male  324"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liver_df[['Gender', 'Dataset','Age']].groupby(['Dataset','Gender'], as_index=False).count().sort_values(by='Dataset', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "b0a4a24b-f24f-4e5e-9511-ef3a99965192",
    "_uuid": "258fc14a5fcf4a33f7560dba890ddcf6b7eab0e7",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:33.748355Z",
     "iopub.status.busy": "2023-05-27T13:28:33.747954Z",
     "iopub.status.idle": "2023-05-27T13:28:33.770827Z",
     "shell.execute_reply": "2023-05-27T13:28:33.769816Z",
     "shell.execute_reply.started": "2023-05-27T13:28:33.748278Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>42.740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>40.598291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>43.347826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>46.950617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dataset  Gender        Age\n",
       "2        2  Female  42.740000\n",
       "3        2    Male  40.598291\n",
       "0        1  Female  43.347826\n",
       "1        1    Male  46.950617"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liver_df[['Gender', 'Dataset','Age']].groupby(['Dataset','Gender'], as_index=False).mean().sort_values(by='Dataset', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "d5bd9ed4-6d96-4893-bbf4-795ab25d2c3a",
    "_uuid": "6c3f4fefb376a5f538b90e1cf47ee6bc6abb5e70",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:33.772881Z",
     "iopub.status.busy": "2023-05-27T13:28:33.772502Z",
     "iopub.status.idle": "2023-05-27T13:28:34.335264Z",
     "shell.execute_reply": "2023-05-27T13:28:34.334298Z",
     "shell.execute_reply.started": "2023-05-27T13:28:33.772808Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5HJjUWKxmPcd5ZFbbDGBtWv57YFlE/L5ag5o9MkmHAisjYnHp5jK7RoX2MyUtkrRoYGCg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\nQmZmZoXmQmZmZoX2/wErW1wTG0iqugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x79302268cd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(liver_df, col=\"Dataset\", row=\"Gender\", margin_titles=True)\n",
    "g.map(plt.hist, \"Age\", color=\"red\")\n",
    "plt.subplots_adjust(top=0.9)\n",
    "g.fig.suptitle('Disease by Gender and Age');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "67d473ed-ea9c-41f2-a3f0-204ef4f0ec97",
    "_uuid": "c49b4d8c56d507f819b413686e38560c26310d9f",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:34.337658Z",
     "iopub.status.busy": "2023-05-27T13:28:34.337255Z",
     "iopub.status.idle": "2023-05-27T13:28:34.826764Z",
     "shell.execute_reply": "2023-05-27T13:28:34.825531Z",
     "shell.execute_reply.started": "2023-05-27T13:28:34.337589Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cfcJ4Fj37BlPGtvDq5nZ15JAhJR5Psq0zzraOOBfdtYJ9dmvgms9M7FWsUotX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7930225d42e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(liver_df, col=\"Gender\", row=\"Dataset\", margin_titles=True)\n",
    "g.map(plt.scatter,\"Direct_Bilirubin\", \"Total_Bilirubin\", edgecolor=\"w\")\n",
    "plt.subplots_adjust(top=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "bed3776c-37d8-46a5-a0e2-c9a3aca3b873",
    "_uuid": "79b749d653ba9554007543a8d72f9f74ed5e3332"
   },
   "source": [
    "There seems to be direct relationship between Total_Bilirubin and Direct_Bilirubin. We have the possibility of removing one of this feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "698b86f0-9f90-44e4-a455-4e544665e0de",
    "_uuid": "46347f3bba50466b2398940b3c975188474723d6",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:34.829044Z",
     "iopub.status.busy": "2023-05-27T13:28:34.828637Z",
     "iopub.status.idle": "2023-05-27T13:28:35.553325Z",
     "shell.execute_reply": "2023-05-27T13:28:35.552425Z",
     "shell.execute_reply.started": "2023-05-27T13:28:34.828962Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7930226edef0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aGouCi4+fxFePmyCl3iYlSUmILJJLfexq24Lc9mwV79e0AlBW7OaGlZXMLPWm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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7930226edb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(\"Total_Bilirubin\", \"Direct_Bilirubin\", data=liver_df, kind=\"reg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "0e313f91-b5e0-437c-8496-583dc84b86df",
    "_uuid": "2f0eef12382e1bdfb8f25881a8e91e6017e13e94",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:35.554940Z",
     "iopub.status.busy": "2023-05-27T13:28:35.554633Z",
     "iopub.status.idle": "2023-05-27T13:28:36.127211Z",
     "shell.execute_reply": "2023-05-27T13:28:36.126291Z",
     "shell.execute_reply.started": "2023-05-27T13:28:35.554891Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JgllT2Gu3cXzyPXOZMaWR7W2dNNbXUltbw5SmBs549ywm1Ndw1WmH0DSursfn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\nyUdsv+uyYpQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7930219020b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(liver_df, col=\"Gender\", row=\"Dataset\", margin_titles=True)\n",
    "g.map(plt.scatter,\"Aspartate_Aminotransferase\", \"Alamine_Aminotransferase\",  edgecolor=\"w\")\n",
    "plt.subplots_adjust(top=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ff7fe013-f0a1-4489-ba53-576807d6feed",
    "_uuid": "e501b5d2dd39ff930376992e9d97c3b991a38d2a"
   },
   "source": [
    "There is linear relationship between Aspartate_Aminotransferase and Alamine_Aminotransferase and the gender. We have the possibility of removing one of this feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "7d71e81c-f7e3-48f6-aba7-fce8c071df3b",
    "_uuid": "065ebcad6c036771378419b1d4a4e92767eaeaa8",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:36.129200Z",
     "iopub.status.busy": "2023-05-27T13:28:36.128848Z",
     "iopub.status.idle": "2023-05-27T13:28:36.802520Z",
     "shell.execute_reply": "2023-05-27T13:28:36.801631Z",
     "shell.execute_reply.started": "2023-05-27T13:28:36.129138Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7930226c22e8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l4hUnzKZifEkGR5a/kg2yasALKMhERpH6JuaOupYAv3hhO3/ZpErE0ZZskHUA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7930226c2518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(\"Aspartate_Aminotransferase\", \"Alamine_Aminotransferase\", data=liver_df, kind=\"reg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_cell_guid": "dc59414a-bbea-4dfd-b160-9c4b9bc441b5",
    "_uuid": "913a7c6227545ad9f1802df2d74ab2dac51574c0",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:36.804040Z",
     "iopub.status.busy": "2023-05-27T13:28:36.803777Z",
     "iopub.status.idle": "2023-05-27T13:28:37.301094Z",
     "shell.execute_reply": "2023-05-27T13:28:37.300332Z",
     "shell.execute_reply.started": "2023-05-27T13:28:36.803994Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UllFV5gcwrvsi/ceQNAr4KjAyeR0zOzs/YfVd3gzmXHsdWyluf7q+3TWzSSOH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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7930226a55c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(liver_df, col=\"Gender\", row=\"Dataset\", margin_titles=True)\n",
    "g.map(plt.scatter,\"Alkaline_Phosphotase\", \"Alamine_Aminotransferase\",  edgecolor=\"w\")\n",
    "plt.subplots_adjust(top=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "da7eef26-8d4a-4c05-9c80-570b11499bc9",
    "_uuid": "21739cb6eaf4ac688108a1806ac71cb940071493",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:37.302596Z",
     "iopub.status.busy": "2023-05-27T13:28:37.302202Z",
     "iopub.status.idle": "2023-05-27T13:28:37.979811Z",
     "shell.execute_reply": "2023-05-27T13:28:37.978869Z",
     "shell.execute_reply.started": "2023-05-27T13:28:37.302550Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x793022677518>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QEZGxzdz7nW7qe5DZ+4HvASXufmgqEd3o7uflOsChmjdvnq9YsSLfYYiI7I/+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\nHA5HVeOEzOFwOBxVzf8H8fOBaQCe+UEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7930226caeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(\"Alkaline_Phosphotase\", \"Alamine_Aminotransferase\", data=liver_df, kind=\"reg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8269092e-f173-446f-aaaa-257b75d15633",
    "_uuid": "2b63345e2bb199ede233e177754ee5347d0350db"
   },
   "source": [
    "No linear correlation between Alkaline_Phosphotase and Alamine_Aminotransferase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "9d767027-3dda-43aa-8cc1-f908d631d992",
    "_uuid": "716befce65e13a87415987a9885eb36f977be73e",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:37.981240Z",
     "iopub.status.busy": "2023-05-27T13:28:37.980982Z",
     "iopub.status.idle": "2023-05-27T13:28:38.440185Z",
     "shell.execute_reply": "2023-05-27T13:28:38.439453Z",
     "shell.execute_reply.started": "2023-05-27T13:28:37.981194Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CQ1EkkuxjR3bQNjqPi7Bo/c6kax9acCkUInzrSZMcbzc0oy+UeQLTbEmhRYLI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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x793021557cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(liver_df, col=\"Gender\", row=\"Dataset\", margin_titles=True)\n",
    "g.map(plt.scatter,\"Total_Protiens\", \"Albumin\",  edgecolor=\"w\")\n",
    "plt.subplots_adjust(top=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "cd3415c5-e60a-45dc-a9ce-6e33561715ea",
    "_uuid": "2d09fb0a3ec7978315959d87667cd068b2e10e02"
   },
   "source": [
    "There is linear relationship between Total_Protiens and Albumin and the gender. We have the possibility of removing one of this feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "b18f4c6f-7742-4edd-a0d3-be517b953d35",
    "_uuid": "58a74e764ac2bb213ef0701846ca66123a89b946",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:38.441780Z",
     "iopub.status.busy": "2023-05-27T13:28:38.441490Z",
     "iopub.status.idle": "2023-05-27T13:28:39.160246Z",
     "shell.execute_reply": "2023-05-27T13:28:39.159375Z",
     "shell.execute_reply.started": "2023-05-27T13:28:38.441728Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7930213ff3c8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZIAiDXyI9XofdKPgQQsa8lu7EK9r8soaH36zBlhQTRxljyHOYkR23KGMwBlOG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\nEISqI85HEARBqDr/H7pPdLs1M65TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x793021820cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(\"Total_Protiens\", \"Albumin\", data=liver_df, kind=\"reg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_cell_guid": "c2f9376c-218b-458c-9ef9-0246f7f30c6f",
    "_uuid": "d64847416d1a611e041aed536b5391565a1e247b",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:39.161898Z",
     "iopub.status.busy": "2023-05-27T13:28:39.161556Z",
     "iopub.status.idle": "2023-05-27T13:28:39.600634Z",
     "shell.execute_reply": "2023-05-27T13:28:39.599847Z",
     "shell.execute_reply.started": "2023-05-27T13:28:39.161846Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EYyeM8VQazjpsnG+bA/dp9hyeBwKpFgu53lrzIRFpEZExftuDqhZ/LSJ7A4uB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\nIYQkmv8HVIlZRv8gstkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x793021418e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(liver_df, col=\"Gender\", row=\"Dataset\", margin_titles=True)\n",
    "g.map(plt.scatter,\"Albumin\", \"Albumin_and_Globulin_Ratio\",  edgecolor=\"w\")\n",
    "plt.subplots_adjust(top=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b82e75f5-f87c-4788-bd96-3608b9066cb0",
    "_uuid": "e1acbd8a2f18bad8f794a2a08b300a6c1aae935a"
   },
   "source": [
    "There is linear relationship between Albumin_and_Globulin_Ratio and Albumin. We have the possibility of removing one of this feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "7d80b0d8-1fa0-47ef-9667-9e1621752d9b",
    "_uuid": "84126004350eca1fad4180dd154d301bdc2c1c9e",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:39.602305Z",
     "iopub.status.busy": "2023-05-27T13:28:39.602004Z",
     "iopub.status.idle": "2023-05-27T13:28:40.216774Z",
     "shell.execute_reply": "2023-05-27T13:28:40.215897Z",
     "shell.execute_reply.started": "2023-05-27T13:28:39.602246Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7930212e6198>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I0WexKnWtteafO8r53YaDOFrv9fnTx/BvSydglQ2jA5IEHyFEn8Sq1HV1k5MH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\nGE2E5mrKXfC6nYmb5mBEK2mOq9oxjJBY31AMB9r7UW+h7r4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WKYmZz5GuIO56uhn7TvQD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\nQRCEgiPORxAEQSg44nwEQRCEgvO/O7teqFlot54AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7930212e6160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(\"Albumin_and_Globulin_Ratio\", \"Albumin\", data=liver_df, kind=\"reg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_cell_guid": "820ae8a1-03b8-4cc5-8135-84d631f49fad",
    "_uuid": "2f564e383e82994d7710f13b5cf94023486af391",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:40.218503Z",
     "iopub.status.busy": "2023-05-27T13:28:40.218157Z",
     "iopub.status.idle": "2023-05-27T13:28:40.697198Z",
     "shell.execute_reply": "2023-05-27T13:28:40.696050Z",
     "shell.execute_reply.started": "2023-05-27T13:28:40.218436Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BqDqeSCQRjiWyZCsSTyprM8ocN6dWaqhaSogmok3M3EZEMwBcBOAHAJ5j5lNKPUCdakuI\nTjJj9C1PIp7hzPZ5CFtvPx+d3dbt8pXyVYmPrCRUxRvnlKxUW8UPPVI413fWGgrgQCRf5o4O+tEZ\nNj7fJ3malcZRWbG6kL3FzOOIaAmAlcz8hL64OTkYM6ptIevqiTlSqcPJKgl2kSoGjlMVb56TsmJ3\njpRyTpnJXFPAl/fc7mgcv33lPUwdd3zaarL6rY8wZ/JwtDT6HRmTUDSOyorVT8oniegtAAkAVxPR\nMQB6nRyI2wj5jSOrzOzyRkJeSV+VVLMXnIaZoW+OmRnReAIBv3pRUy18RibEpgYfjuvXgNXXnZle\nmO5/cQeaMnxawJG5nBtBPP2UQbj67JFoavDhcIHGtlbGKFQPlj69mPkGIroLQCczx4koAuDC0g6t\nuiEihAJe3H/JRPQL+nEoEoPPQwir7PK9cURiyTzzR9DvFTu+UJXYNRWqTH9PrvsAT731cd61qvur\nTILNDT78cOoY3PCHI+H6d108Hj3RBEIGspLpI5t+yiD88EtjskL9rZg9xQTvDiwXDSai0wAMQ8bi\n15dgD7tUm2lRZRL8zaXt6AxH84StNRTA5Ubnf7tdC1velCEobRjQ1FB1giI704JUxZvhlKyYmb2D\nPk+exhSOJQxNf/dfMhFttz2TZzJ3rMD27EloNjAVJhJJdPXGcTAcw5ABIezeH8bCZ7Zh4pD+mDHh\nBPQL+rOCsey+B7LR7BOGskJEMQA9SBXIzqWJmQ1NVVajFh8G8FkAm5AyL0J7UNkWsmpDZRJsDHhx\n9//ZmhXNePfqrVj4jTbj8/1exOJJ3HHhyTixNYQPOsMIKISqksjOtP4wM3t/0tWbpzEd09JgeH6/\noD/r2kL3Nwv7NxyPYkEhIsQSSdz0+JvpcS78xikI+r246rENWWMf0BQwXMwkTaXsvMnME41+QUQb\nVBdZ3VJ8DsBnmVkq2WqEownMO2dkniO5uzeOjw/1Yuqil9LnnjFigDIUuLs3ju8+tqHqd3yZyaWA\ntJmvNeyGtetFfQGki/oumT3J8Pyunnjar9XdG0dTIJWfpQrvN5MVlRneSEOMxJOYtyx7nNf/7g3c\nceHJGNjSgD/Pm4KRxzbjg84wmhuSCBksZJKmUnYCxfzO6tZ/C4BjbA2nxgn6PJh52pCsZM6Zpw1B\n0GczIdolOz63jFOwj+7bykxA1osD587Zuy4ej6DfeC4E/cZFAgiclhP93skkI+hTFR9WtTYyPt7o\n9RiOP+j3GI7zxNYQfvilMekx3fT4m+iOGteFrJWEYRcRI6K8YqFagGFMdZHV8PtnAUwA8BoyohWZ\nuWwBH27xkan8B16vR9lE8OVt+3DGScekg0ZeffcTTBl9bFXt+MRXYImqsLHalRWzsHZ9jjY1+HAo\nEsPKjR/ijJOOweq3PjIMa39le0feXP7H44/Cwme24eqzR6Y1oGP7NSDJwEMv78y7z2VTRqDR60Ek\nfkSGgj4vehJJPPTyTnxt4mCccHQQ4d4EuqNxtDT4DP3PKp/a/ZdMxFU2rCBGcgtA/MV9Q+Uj+wGA\n8wFcxcw7tWPDkapm8wQz32N0ndVPoDuKGGhNY6ahmBVOzQ0RbvR6MGloa57NvrHK/GT1WFC1XjBL\nGclN7r/zovHY2dGFmacNyS/q6/di7KD+WXP5zovGY1D/RoOIwTa0NgUMiw9/7+yR6DRKfG4KYOcn\n3WAGvvngmqx7HdevIW/8eimt7DnbhpZGe6WrcuVW/MWlg5nvIaIWAGu0/ntJpOIx/gPAItV1dqIW\nBwMYxcwvEFEjAC8zd/d96NYoRiMzsvuropPsYjfiSrXbM9sNV1vSpkQtFqQq3oxiNDJV4rAdjUY1\n9x/41iRc+ajxHDfS4CaPGqiUif2Ho7jp8TfzfrdoZhsOhmN5489NlNatJaGADzv2HcZ9L+zAqjf2\n2rIuiHXCEQrKim5i1Ds/mGE1avEyAN8HcBSAkwAMAfBLpFpMVCWqnBZVdJJdlBqKTV+SU00Ey4Ek\nUNcmQZ/XUMNSzc1+Qb+tqMJmhQYU9HsNrRFm0YxGzziuXwM8BMxftSWv7UvmnA35vXma1J0XjcfI\ngU3pYsVWEH9x6SCikQC+B+BTAPcAiBLRUGbeZXad1U/0eUhFLh4CAGbeBuC44odbesIx43bpxbQp\nSSSS6OqJIcmMrp4YEomksmlfJJa01TbeqSaCZkgDTcGMnkTSUFbM5ua8c0Zi9XVn4t2ffBmrrzsT\n884ZaXrcuIWRQkZN2sTsP9yb97vrzhudjk7MvE8kni1z3dH8NjM3rtiMOZOHp82CVmSlVlqfVCkr\nAHwAoAkpc2ISwKOFLrK6kPUwc1T/gYiqfuvhlKajiuiKx5PoDMeyjneGYwj68iO9zNrGqyKxVLtD\nu63Ja6WVuVA6zDSM3Ll550XjsWNflzJiV3X8rovH581xMxlVtUjyez159xoyIKS8T+bcDwXUz9MX\nMSuyIpGMJaWbme9h5h8BaNPWnVChi6xGLf4cwMcA5iCl9l0NYDsz39S3MVvHyUgsO76nYooD65Fe\nudGJVzyyDgNbGvKitxq8xlGORpjZ582eK/b8suBKH9nh3rhh9OCcycPx21few+zPD0NLoz/tU7r6\n7JGYv2qLoUwY+dpmf34Y/v//3oLrvzgaQwaEsP1jcx/cktmTEIsnEfB5EWrwoqsnjr/t6MDkUQOx\nY18XRh/XD8xAqMGLg+EY/F6yJKOrrztTOW7dl2YoK7PbAUKWT1j8xfnYfE9UUYsLAOwAsBTAOgAX\nA1jOzO1mz7aqkf0IQBeAdwBcC+A5ALdYvLYihBQ5LXZ3TbarDASMG2WGAl4c168hP3+lNw4iQkuj\nHx7tq5kPzzRa0mA3KfZ8oRDKnEi/F4uf34GWRj/G3Pokpi56Cave2IuRxzYr59SMCYOz7jNjwmC0\nNPiw6o29OG/hX8AMTF30EhY/v0NpjSAAV//XRrTd9jS++eAadPXE0D60FUGfFyf0D+E7j6xL/y6e\nSJo2uc0c530v7MCdF43P0zB/+8p7prISDHjztLN6a8BZCActP9cA+DWACIBRAJYhFZ9hitWiwQkA\n92svV6A3sjRqcGmHYqoMqDSjWy74x7xKA/OWbbKlHZlVQzCqvKFXW8jVBHu0yC2hvjDaNUfiSdNK\nHTv2Hc6ac109MeXcv3HF5qz73LhiM5bMnpQ+JxJNYPV1Z2L1Wx8hEk9g/a7OrMLbDOCKpesxdexx\neQW5I/GEcpytoWxZD/q8iMTyq+9s2XswlR+nRS3e/fRWrHpjL17d2amsTLJj32GpZFMApyr/MHO/\nYp5v+gQiWsbMs4hoIwyKOKpqYlULXq8HLdrCVWwou6pdi74LzD2uVxkwqugdajauRWdHOyomWvJX\nl0xEV288q5Dx4lltaPSJOaSeUOY/NQdMfVXL1+7GnReNT+eBNTf4sn7Wo//MrBd6VZBbV76Jjw/1\npn1k1yzblNWc9t2ffBnnjzsO08YdnxfN2NqkHqeHCC2+bFkPEpT5bmNuzW6Kq8tKrmzdedF43P30\n1qxzhHycsvxouWPjAbRkHP4pgJsAvKeKXiy0VN6gff26rdHUEF6vx3i3F0/gw4NhPPCtSWhu9OFw\nTxzvdnQhGPBi2ZpdWUWDl63ZhTmTh2P/waiixUsiZYPPyHWJxJOGtmZVa3JVm/lILAmvx4Mb/rC5\nT5qg4H5Uu2aVJhKOJjCgKYA5k4cjFPBmycDKjXuy5vjKjXswZ/JwpbVg0cw2RONJ/Px/t2HHvsNY\nvna34fkfdIbx1bYTsvLUCtVy7O6NG25UM6MirdwnEktmydbu/eG0xpb5nojM5ONgTcqnAHiRcmXp\nfAbAPyNVpN5wITO1szHzHu3by5n53cwXgMvtjM6tJJOMA5Hs6MQDkVjaXn/lo6njVz66Hif0DyHo\nM/YTNDX4sOjZbXk2+sWz2pBIJtO25Yde3lnQ1mzogzOJpAo1iJ9MMN81q+aO1+tJ+W09nrQftyng\nw6zTh2bN8VmnDzWtkdgbS+JHf9ycJRNGtRn7h/ymeWqq+xthOyrS5zmSd8ap6zu6eiUy0QIORnIO\nYOazmXm6/kJKE/tfzLxMdZHVqMUNuWZEInqDmU+xO8piqVStRad6JunH86IWWxqy6sSpIqusaE+q\nqCGpRFA2qsJOq5IVOxGvulUg6PcgHE1F1GbOKaO51h2NY8e+Lpw0sCXLSjHy2BalTOjWjVxrh+r8\njq4eDGxpzDtf9/Xljkd1H6PoysumjMiSB4lMtIdDUYvXMvO9hY7lYqqREdGVmn9sDBFtyHhtB/B3\ns2trBcd6Jmk5OR1dvbhg8cu45NdrEAp4EfBmV+g2iwgrhCqSSvJeBMB8HmTOnZDfi85wDA+9vBMf\nHuhJWyMyrQNGc01lpTCTFZ/PkxWx6/N5TDW75gZ/lnVk76c96ajDh17emR5nV08MoYDXMEoxFEhF\nY05d9BJOuvmJdBRlroxJZKI9HHq//kREC4noX4mon1YOcWWhiwptx3+PVKj9HQB+nHG8i5n3FTNK\nt+Fkz6TcCC29rlzm+bkRYpnXF6s9qfxqIpj1hdk8yNxN6xUw5k8fmxeFqEeiGUXm9iSSyjmu6t2X\nWwtRv8/h3liWT66jqwfBgDfP53Xjis2YP30srl2+CfOnj8XCZ7fj1Z37cSAcw4FwDKs2fWjZl6eK\nOFZpoCI/JeFxAA8DOB6pyh7fQaqyx5lmF5l+MjLzAQAHAFxMROMATNZ+9TKAuljIVFGCQb/XMHIr\naBJVmBuh5fMQti2YlnX+6rc+youGdEJ7kjqJAmA8D3KjGbfePg2vv99pah0w6vDQ2uQ3rH6vquUY\n9OXXPtTv4/N4MXfp+qz7qKwj+jhHHtucPn5iawj//PtNuP6L+VX3lTLtM444bg350RmOSbX78tCt\nt2ohos3MHCWigpU9rBYNvhqpah66ivd7IrqPmX9Z9HAriGp3pTquihI0ity6bMoIw/O7o2oNLvf8\noM9TlPYku0ahGHKjGXWrgMo6kJuzOLClITWPmwNo8Hvws6+Px6D+wSwNSBU9qIqiVEVF/mJWW161\n/Mzx6nzQGcbHh3px99Nb0/f5oDOMpgafFonsz8sxTXWUNh6PdEcvGy8S0RykKnsktCLCBbH6X7gS\nwGnMfBgAiOgnAP6GVAV8V6HKpSm068rdxYb8Xsw6fahhfy7V+UZ5Z6rzm7X8N6uCIj2ShGLJ1XT0\nChgrN+7Jszrk5ixOP2VQutfYcf0a8MOpY/CjP1rPLzOrEJJr7QgFjKvlf3gwnM5383kIpw5rxdEh\nPxbPasO8ZZtwweKX02Nv9HmRTLKxrJvk00nUb9m4GkAzgAcARGGxsofVqMU3AbQzc6/2cwOAdcx8\ncl9GbAenohad6iMG2NfsKtEfTXaNZaUqdgzF1FrMnTvXnzcqnT+mRy1mai56bcYT+gdxxdLUtWa1\nDFXRg3b7kVmNfgz5vSAiZRSvqrak3bqqIltF46isWP0UfRTAa0R0KxHdipQ29oiTAykXdqMQzXZd\nRlE6ZjXH9JwcKzUVnfq7ZNcoFMIomlHvz9XZnd/hodF7pDZjMGPemfnUVNGDuoY1+pYncdVjGzBp\naKttDa6pwZeX69kZjgGAYRRd0O8xzPU0i5aUqN/yQESziWiI9v0UIrqeiP6h0HWWPkmZ+WcA5gII\nI1XM8bvMfHdfBlwpzHod2e0xZNS7KNPfoPc8mrdsI8KxRF5fs3g86VifMOmRJBSLx0M4OpjdW+/o\noD/LZ5Q5lzNrHur+KQBZ3+ucOqwVXT1HKoFsvX0a5k8fi5Ub9yAcNe5HZtYHTXVcr6aj33/Zml3K\n3oOZNSEz+5JF4gksX7s76z7L1+5GT+JIxY9tC6bhwUvbxWRfOm4A8KG2eP0GQAOA3xW6qOBCRkRe\nLfn5dWZeyMw/Z+bX+z7eymCWS2Nn16XSvIJ+j+GusVGLiMreNWbnvvSlT5jkignFkkgk0RnOn5uq\nuZypGWVWlL//xR15vcJSvrYP8zSgmacNUVebN9GMVMcNNSy/8cebmWanyi+TnLKyEdOK1F8A4DFm\nvgPZdRcNKWjgZeYEEf2diE5g5g8dGGjZsBOFqDoOpHwIuefarVunqvuWmfvSl0goyRUTiiVssyZh\nZq6kXofwjgtPxpABIfREE+lowMxahRt2H0yfo8qh1O+fqRnpPiy9NqMqT01Vdb+lMX8xs5sbKvUV\ny0oXEV0L4DIAs4mIYGGdsuoTHuusAAAgAElEQVSkOQbA20S0moge1199GCyIqD8R/ZGI3iGit4no\njL7cLxczX5Vqd5V7HIDyHma+NiPNSLULzMx96atPS3aNtUmpZcVUQzHqdu7P9nl1dPXCQyn/cHc0\nVV7q+t9tgs9L6VqFHV298HkJ1/9uE656bAPGDuqv1LBUmpFeLb/ttmcw4qYn0HbbM7hm2Sbb3eBV\n1gu1xuecL1soyGwAQwHcxcxvINUd+qpCF1ndZvy0DwNTcS+Ap5j560QUgIV21nZwoj+Ofo+BLQ34\n87wpGbkoXiQZyt2bUY6KareXmfsiuz9BQUllxaxKTaaW351hmdjf3ZOnGR3dNDBL5pJ8RFPbvT+M\nnz11pJK8rjHpGlZLow/dvam5bzYeVYUQO5qUWW6okSZ42ZQRaGTk1YT0yQLnOMy8S0vv+hwRXQDg\nNWb+a6HrCobfE9FXAIwE8CYzP+fEYImoH4A3AIxgK/H/sB9SnGTG6FueNKyk4SFrmkqSGdf/zrg6\nQGsoYJiLospHOzroR2c4mpdHtnztbix+fofkfdUGjv/jyiEr8UQSnd35c7O1KQCf15OXozjvnJGG\nlToGNAcw5tanDGVOJYujb3kSXz75+HQu2uvvd+Kdfz8few/25OWRDerfaDzOUAAHIn2vvKH6zHjn\n389XPlcWs6JRFQ0+C6kSVX8F8CWkavrezszPmN7MTDaI6BcAJgB4FcA5AFYw80+KGXXOfdsALNEG\neQqA9QCuZeZu1TVO5MbYzf043BvHJ129uOnxNy1VDNd/VuWp5ebMvPfJYYw8tiWvsrjgWkqxkJVc\nVrp6YobV4OdMHo6WRn+eLJnlixWTFzZ/+tis++kdpK3mealk0a4sFdPpotiGvYJyIVsLYBYzv0tE\nGwB8AcBzzPx5s5sV+kQ/G0AbM8eJqAnAXwD0eSHTnjsRwDXMvIaI7kWqKPG/ZJ5ERHORCvvHkCFD\nbD1A2UnZRhRfyO/FkAEhZW5Mpi8tXcHDxHemqrWYeR9ByKHksqL7pBY+u/3IQz2E7587CkD+nDbL\nFzOqvKHqpq7nZ+V2fr7vhR1ZGlqhCiEqWbSL6jPDrg9O6BMNWr9LIKVoRTRzuimF9OIoM8cBQNsB\nOqVH7wGwh5nXaD//ESlhzYKZlzBzOzO3Dxw40NYDMu3gxeZ+eDxkmrtihN08NdV9isEor01wPSWX\nFbMcxHg8mTd3VfliqrywSPxI54dtC6bh/ksmYv2uTs3n5EU4mn3/VW/sxZa9B/HAtyalz9+y92DJ\nZUj1mVEO2RXSMBEFte/9RPQjAO+aXQAUXpg+k9GDbGPGzxs1ta+4kTL/D4APiGiMduhclKC/mRNR\nfGZVCQzPtxkR5VSel1mUpuBeyiErqjnb6PWgMxzFb195L0sG9A4NmeffdbF5TUW9Kr5ewWPsoP4I\nBby4/OF1+JeVb2Xln11/3ihMGtqa7mumV/xQRhU6mCup6rNmpzO10CduBTBY+/5vAAJIheKbUshH\ndpLZxRkqoG002/+vkRroTgBztLYxhlSyQ7RRXbbLJo8ACIa2eKm1WLeUxMFZKlnJnKc9sQSSSSDU\nkN2xIdN31tUTQ3ODD5FYAn4P4X8O9eLE1lDWcTsdmWd/fhjabkv58KefMgi3fXUsWhr96OqJ4arH\nNli+j+7LK/Q39sUPHY8nJWrRWSz/E4joGmb+hdk5hfqRWVqoiOgVZp5c+Myse28C0G7nmkoQ9Hny\nIrTuung8bl35Jj4+1GsYHaXq/eX1etCiLVxOO4ml1mLtUgpZUXVLCAW8Wf5eoyr0JxzdCCLCome3\nZUX0vnPb+UpfmFGU46YPjqzFq97YCw8Bt1zwjxjQ1GBaeUPly7P6NxYTGezzedDiK43sCkcgou8D\nuAJAv4zDg4joegCLmPleo+uc2qo3OXSfklLM7iwST6ZzS0Yd15yXD2MnN62U/cJU1QokL00wwkqe\nZTiawJa9Bw3zxYgI1503OquixrufdMPvhWFn5w8PhvHAtyahudGHwz1xvNvRhWEDmvHwt9sxcWhr\n+nijl0x796mOGy0uTuSS1gNV1sfwagDTABzSfmYALyIVeBhWXeSUblz1jphifUihgDddZYAZOG/h\nX9KLGGBd6ym1D0tqLQp2sKLBB/3G1emDfq9hRO9HB8NobvBn1WxsbvAj6PNiQFNj2ud15aPrMaCp\nESe2BvHZQUdlHT/YE1f6pYN+ez4ysVIUpgp963uZ+X1m7tReBwB8on3fo7rIUj+yQhDRBmbOi6Ry\nkmJ8ZJk7je7eeFE9hTJ9T7n5M9NPGYTrvzgaQwaECu5kivFh2d0pVdnOqh6pijfbiqxYmY9m+WVN\nAR+6o3GEAj7s2HcY972wA7d9dazSt2XVd7azowuTRw1E0JfKC2tu9GXlo9nxkYnfuDAVfI8clRWn\nRloVApxJrn186+3TitqdZeaW6NW9b/jDkW64N/xhsyX7u93dYTH2fZVvThByKZRnqdcTVXVqzp2b\nd140Hv2C/j53gr53Zhte2d6BsYP65x0P+r22fGRO5JLWOrWitTr1afdth+7jGLn2cT33xa4PKbcu\nW08sgQdnp/zuemdcoLD93a4PS+z7Qikp1C0hHEsgmWRlVXm9/5euGa3cuAdzJg+3XVXeyAd3xknH\nZGl2harxZ1bVCQW8CPcm4PEAjX4vmhq8eHB2e1Y0plgpjlArvnVTHxkRHSCiToPXASJKL+NaleKq\nInenkdk3ya4PKTO3JBTwobnRh1CDvZ2MXR9WreyUhOrFLM9Sj15UaVh2OyyrfFtGPjiz5xrnaHqy\n/TxL16GzO4rrf7cJlz+8DpFYAmBIRwgDasW3XmjJPaYsoygBuTuNVW/sxciBTemIqr7uzuzuZOz2\nC6uVnZLgTsLRBOKJpFLDUkUzqvqFqfqL2e2DZtRZIrOTtX6fG/6wGfOnj8XURS+JJcOEau1jSEQL\nmfl6/Wuh8001MmZOZL4AHAXguIxX1WK005h1+lA0BZzp11XMTsZOpZFa2SkJ7iTk96Kl0WccPegz\n1qRCAa+ygoeqv5hK81JV0+kMx3I6WceU1gu9159YMsyp0j6GZ2tfz7JysqUtitYX5h6kSofsB3AC\ngG0APmN/fOWh1DsNJ+9vt5O1IDiN0RwMRxNYuXGPoS9MpUmpfGp288IG9W/U+pT5s55rpyu73utP\nLBm1j9U8sgVIldPfyswnApiKVJJaVVPqnYYT9y+mk7UgOIlqDuoVOTJ9YTNPG2KqSdnzbal9agfC\nqTJV+nNnnT7U1G+ce/+7Lh6P+1/cIZaMOsHqFiXOzB1E5CEiYuZniGhBSUdWJ0h0olBpVHMws4Nz\nrs/LroZl5NvqTSTh93nSx7sicfzt3Q5MGX2ssS8sZuyzi8SS2dYLLWpx4Tfa0t+DUjlTYtWoTaxq\nZJ9q/cheAbCUiH4OIFm6YdUPEp0oVBqzOWjk87IdnejL920d7o2juzeO7z22ARNuewbffHANPo3E\nsHrLx0pfWNDnUfqNs6wXjT6EAj6AgUgsgcsfrpqqFYJ1bP2TrG75ZwDoAXAdgNlIBX18xd64BCMk\nOlGoNGZzUOXzMqqdqKqpGAx48zS+A+FYVud1/d53XHgyunvjWPf+/jxNcMroY3F0MFtTC/pMIn/L\naO2QqjqOszznqylW/5s3MfPNABIAfgMARPQTADfbHp6QhVQfECqN0Rz81SUTTX1eJ/QP4cpH1+dV\nub/4V68ZdkHPvc+Jrcad14cMCCGZYMNO040+Dzq7o3lV9Ac0BQzbIZXL2uFklX0hBTP/LPNrIaya\nFs83OHaB1UEJapzoZC0IfcFoDno9HhyKxJSdkVWdoK12R/+gM2x47sef9iASV3Sajhkf16uQ5HZH\nN+t87WQ39UzNTx/XvGUbEY4lir6nYI9ClT2u1DpDj8noFL2BiLajBB2d6xWJThQqTe4cDDV48d+b\nPjTuaq7QdFTRiUY5kUeH/Fg8qy0vV+zOp96xHRWp137Mjbps9HoMx9/o9Tha8V383JWnkGnx9wCe\nA3AHgB9nHO9i5n0lG1WFEDu3IKQIRxN46q2PwYwsX1Wjz6P0qXX3xpW5j0bHe2IJ3HHhyTixNYQd\n+w7j7qe3oqOr1zT6cd45I/Oq34ej6qhLVUURJ31n4ueuPJbbuBDROAB6F+iXmXlLyUZlQDFtXOwg\ndm7BAapiojghK2bywJz6nVVflYpEIml4n9ZQAJ1h68cHNAcw5tanDH1zo2950tZxDxWfCyqfHbaw\n9cYQUQhAIzN3Gv7eykJGRFcj1blzpXboqwDuY+Zf2hlMX3ByITOsYhBLSO8ioa9UxaeWU7JiZqFI\nJJIIxxJZeV52FjEd1X2MjkfiSUMZNet3Zud4X2RdrDm2MXxziOg9Zh5ucPzzAG5n5nOMrrM6864E\ncBoz36xFL54O4LsWr60qVFUMxM4tCNmY+W69Xg9aGv3wEKGl0V/UImZ2H6PjTvjmCh0vxXsl2KKF\niC7NfQEYB+BU1UVWtx8EIJbxcwxVsvu0i1kVA5VdvingM9xtyS5MEEqDymqi8kXZ8c1JHdOqJgCg\nHcYJ0b9VXWS6kBGRj5njAB4F8BoRrdB+9TUAjxQ50IpipnndedH4vG64QX9+N9zFsyagNeRHZzgm\ndnFBcBiVz6k15FfmXKq6o9s9LlScg8x8jd2LTH1kRLSBmSdq358KYApSmthLzPx6sSMtBqfs/od7\n40o7+29feS8vImrO5OFls7MLrqcqdjClDowC7PuE7JyvktEHL23P7gQtmpSbUfnIPsPM79i9WaFP\n3PTDtIWrrItXKTCrpDHr9KF5x4vJaRGEWsZulJ7d882sJroPChBNqhYpZhEDCi9kA4lI2Z2TmRcW\n89BKYmYfN6q4bZYzI7kjQj1it4ZhOJbAsjW7svK5lq3ZhcumjDA+X/KyBJsUCjXyAmgG0KJ4uRKj\nCKNkkm1V3FZX+i4ueksQ3ILdCN+g34MZEwZn9TWbMWEwgn5jWQn6jCtyiGwJKgptbz5i5tvKMpIK\nY7bLNNLgwrGEYdWAy6aMQHORociC4AbsakxmVfRbGvNlJRJPlly2JOK4trDsI6t17NrlQwEvFj+/\nAwuf3Z4+3+chfP/cUeUbtCBUAF1jyq2wodKYzPzMRpRatqQSR+1RaHtzbllGUQWYVco2wqiit+47\nE4RaJlNj2nr7NMyfPhbL1+5GJG7ca9eubNk93y5Srb72MF3IVHWtahG72f563llu9W6JWhRqHV1j\nmrroJZx08xOYuuglLH5+h3Lu25atElTeyB2/RBzXFhICpGEWzWhkT4/Ekli5cU+WHX/lxj1aJJb4\nyITaxa6PzG4ljVJX3pCoyNpDPnEzUEUzGtVmDPo8mHX60KxIrFmnD5XOzkLNU4zGZLcWYSlrF0pU\nZO1huY1LSR5O5AWwDsCHzPwVs3PLUa3ACKkyINigZP/8apMVVdV6N0QDHu6N46GXd+ZV8VHltQkl\nwdFJUen/2rUA3gbQr8LjUCJVBoQqoWpkRc+5dGv9UYk4rj0qpksT0WAAFwD4daXGYIVSR1AJQiGq\nTVbMov7cEA0oMl17VNIovAjAjwAYx+wCIKK5RLSOiNZ1dHSUb2QZlDqCShAsUFWyYtYXzA3RgCLT\ntUdF7GFE9BUA+5h5PRGdpTqPmZcAWAKk7P5lGl4W0rtIqCTVKCturz8qMl17VEoj+wKA6UT0PoDl\nAM4hoscqNJaCSPdXoYJUVFaSScbh3jiSrH1Nclk7L5cKkenaoqJRiwCg7TJ/WA2RWILQR0r6aVhu\nWTEr5QRAuqYLfcHRSSGJE4IgGGIWvKHSaETTESpBxQ3XzPwigBcrPAxBqHrKLStSyklwCzWhkRnZ\n8QVByMeOrBQTpi6yKFQC1y9kqhJSIkCCkI1dWbEbvCGyKFSKigd7WEXlwDYrIVVNIb9CXVAVDiEn\nZcVO8IbIomCDmipR1WfEji8I1ihGVvTgDaBwGTaRRaFSuN60KOVmBMEaJW9YKbIoVAjXL2RuSsIU\nhEpS8oaVIotChXC9jwywZ8cXhBJSFZOukrIisihYRHxkudix4wtCPVNqWRFZFCqB602LgiAIQn0j\nC5kgCILgamQhEwRBEFyNLGSCIAiCq5GFTBCEqkNqNgp2kLAiQRCqCrM+aBLKLxghGpkgCFWFWR80\nQTBCFjJBEKoKqdko2EUWMkEQqgonazaKr60+kIVMEISqwqmajdIfrX6QYA9BEJRUonaix0MY0BTA\ng5e29+m5mb42AGlfm/RHqz3kvykIgiGVjB50omaj+NrqBzEtCoJgiNujB6U/Wv0gC5kgCIa4XaOR\n/mj1g5gWBUEwRNdodB8TcESjcYOPySlfm1D9iEYmCIIhtaDR6L42D2lfZRGrSap/WyUIQkUQjUZw\nC7KQCYKgRDo+C25ATIuCIAiCq5GFTBAEQXA1spAJgiAIrkYWMkEQBMHVELM7CmgSUQeAXZUeh0Mc\nA+CTSg+ijNTL3/sJM59f6UGIrLieevibHZUV1yxktQQRrWPm9kqPo1zU298rOEc9zp16/Jv7ipgW\nBUEQBFcjC5kgCILgamQhqwxLKj2AMlNvf6/gHPU4d+rxb+4T4iMTBEEQXI1oZIIgCIKrkYWsjBDR\nQ0S0j4jeqvRYygERnUhELxDR20S0hYiurfSYBHcgsiKyYgcxLZYRIjoTwGEAS5l5XKXHU2qI6HgA\nxzPzBiJqAbAewAxm/nuFhyZUOSIrIit2EI2sjDDzSwA6C55YIzDzR8y8Qfu+C8DbAE6o7KgENyCy\nIrJiB1nIhLJARMMATACwprIjEYTqRmTFPrKQCSWHiJoBrABwHTMfqvR4BKFaEVkpDlnIhJJCRH6k\nBPM/mfnxSo9HEKoVkZXikYVMKBlERAB+A+BtZl5Y6fEIQrUistI3ZCErI0S0DMCrAMYQ0R4iurzS\nYyoxXwDwLQDnENEm7fXlSg9KqH5EVkRW7CDh94IgCIKrEY1MEARBcDWykAmCIAiuRhYyQRAEwdXI\nQiYIgiC4GlnIBEEQBFcjC5kgCILgamQhEwRBEFyNLGSCIAiCq5GFTBAEQXA1spAJgiAIrkYWMkEQ\nBMHVlHQhI6KHiGgfEb2VcayViJ4hou3a16NLOQZBcAMiK4JQPKXWyB4GcH7OsR8DeI6ZRwF4TvtZ\nEOqdhyGyIghFUfLq91rb7j8x8zjt560AzmLmj4joeAAvMvOYkg5CEFyAyIogFEclfGTHMfNHAKB9\nPdbKReeffz4DkJe8qvnlNCIr8qrVl6P4nL6hkxDRXABzAWDIkCEVHo0gVC8iK0I9UwmN7GPNTALt\n6z7Vicy8hJnbmbl94MCBZRugIFQJIiuCYIFKLGSrAFyqfX8pgP+uwBgEwQ2IrAiCBUodfr8MwKsA\nxhDRHiK6HMBPAXyRiLYD+KL2syDUNSIrglA8JfWRMfMsxa/OLeVzBcFtiKwIQvFIZQ9BEATB1chC\nJgiCILgaWcgEQRAEVyMLmSAIguBqZCETBEEQXI0sZIIgCIKrkYVMEARBcDWykAmCIAiuRhayMpJM\nMg73xpFk7WvS8SLQgiAIdUdVV7+vJZJJxv7uKOYt24jX3+/EqcNasXjWBAxoCsDjoUoPTxAEwbWI\nRlYmwrEE5i3biFd37kc8yXh1537MW7YR4Vii0kMTBEFwNbKQlYlQwIvX3+/MOvb6+50IBbwVGpEg\nCEJtIAtZmQhHEzh1WGvWsVOHtSIcFY1MEAShL8hCViZCfi8Wz5qAM0YMgM9DOGPEACyeNQEhv2hk\ngiAIfUGCPcqEx0MY0BTAg5e2IxTwIhxNIOT3SqCHIAhCH5GFrIx4PITmhtRbrn8VBEEQ+oaYFgVB\nEARXIwuZIAiC4GpkIRMEQRBcjSxkgiAIgquRhUwQBEFwNbKQCYIgCK5GFrIyItXvBUEQnEeSmcqE\nVL8XBEEoDaKRlYlyVL8XjU+oFWQuC3YQjaxMlLr6vWh8Qq0gc1mwi2hkZcKs+r0Tu0/pdybUCjKX\nBbvIQlYmVNXvgz4P9ndHccUj6zD6lidxxSPrsL87ansxk35nQq0gc1mwiyxkZSKz+v22BdPw4KXt\nGNAUQCSedGT3Kf3OhHJSSh9Wqa0XZohvzp3IQlZG9Or3HtK+esix3af0OxPKhe7D6qsVQUXQ58G9\nM9uy5vK9M9vQ6HXGeqGi1H+XUDpkIaswTmlSKo1PnOOC05TahxWJJ7F+Vyfuv2Qiti2YhvsvmYj1\nuzoRiZf2ueKbcy+ykFUYJzUpI41PEJym1D6soN+DsYP646rHNmD0LU/iqsc2YOyg/iV/rvjm3EvF\nwu+J6AcAvgOAAbwJYA4z91RqPE6STDLCsYSlTtDSOVooRLXJim5FeHXn/vQx3YrgRMPYcDSBlRv3\nYP70sRh5bDN27DuMlRv3YM7k4bafa0cWS/13CaWjIhoZEZ0AYB6AdmYeB8ALYGYlxuI0xdjZRZMS\nVFSjrJTaHxsKeDFjwmDMX7UFY259EvNXbcGMCYMRCth7rl1ZFD+zeyHm8jsyNeF8DcApAA4BWAlg\nMTM/rbqmvb2d161bV6YRFs/h3jiueGRd1q7ujBED8OCl7Y7t6uzsMoWy4vg/oVplxak5aHSf7mgc\nc5euz5OhJbMnIeT3IhxLoKnBh+7eOEJ+L7xe4/14MbIoslU2HH1TK6KRMfOHAO4GsBvARwA+NRJM\nIppLROuIaF1HR0e5h1kU5argIZFV9UG1yooTVgTVXG5q8BnKUFODD53hGOYuXY/RtzyJuUvXozMc\nU2tYRciiWEfcSaVMi0cD+CqA4QAGAWgioktyz2PmJczczsztAwcOLPcwi6K7N24Yhdit5aRIBQ/B\nDrUsK6q5bCZDqrlvJFuSW1k/VCpq8TwA7zFzBzPHADwO4PMVGoujhAJe3HnR+Cw7+50XjUfQ75UK\nHkIx1LSsqOaykQypzg/6jfPLgj6P+LzqhEotZLsBfI6IQkREAM4F8HaFxuIokVgyHXG19fZpmD99\nLFZu3IOIQ5qU7DLrjpqVFbO5bCRDZucbyVYknpTcyjrB0kJGRHcQUT8i8hHRaiL6mIj+v2Ifysxr\nAPwRwAakwok9AJYUe79qIuT3YtbpQ7MirmadPlRp95cKHoIZtS4rqrlsJENBv7GmZiZb4vOqDyxF\nLRLRJmZuI6IZAC4C8AMAzzHzKaUeoI5bohYB48incCzhWDRjIpG0HLkllJWq+JR0u6x4PGQ4xyPx\nJB56eSemjjs+nV+2+q2PMGfycGWUY0ujv4J/nWBCRaIW9U/aLwNYxsyfIJWcKRhgtAtU1Y8L+uwt\nQMkk24rcEoRqxkhWVHM86PMYampmPjWhPrCqkd0FYBqABIB2AEcB+DMzn17a4R3BTbtMIH+n6SHg\n1y/l7yYvmzLClkZWjjw1oWhEI3MAszmuWzdyrR0vb9uHM046Bv2CfhyKxPDqu59gyuhjRSaqF0dl\nxdJ/mZlv0BazTmaOE1EEwIVODqSWMO5w24adn3Rj6qKX0uf5PITvnzvK1r0lalGodczmuK65AUh/\nDfo8mDS0FVc9tiEtb8VYOwT3Yuc/PQzAhVqQx1cB/FNJRlQDGOfHbMJ1543OOq9QtKHkxghuw5Fc\nSZtzPBJP4trlm7Lk7drlmxCJJw3HIz3Hag+rUYsPA/gPpHJapmivyaUblrtR7SiHDAj1uU6c5MYI\n1YpTVWfsRuaaaXD54+lFV09MKuNUKUQUI6IuIjpk8FLu1q36yN4B8FlmTjo5aDu4ye6vtPHPbgcI\nluq42fUTlCOsWOrQFaQq3oxKyUpf/Le5c6vR60Ekbi0yt6snhle2d+T5yCaPGmgYzXjHhSfjrLtf\nNByjzPGyYfimEtEGZp5o93dWTYtbABxj8dy6R7mjDHgt57SY+gkqkBsjNR6FQhTrv82dWw+9vBOd\n4ajlyNygz5v2ken9yyYNbUXQZzyeE1tDhmOUOV4VBIr5ndWF7CgAbxPRn4nocf1la3h1hBPdmqvN\nFyY1HoVCFDtnc+fW1HHH5/m8zOZaJJ5Q+MiMx/NBZ9hwjDLHq4IYEeUVCyWiYwDEVBdZjU29o9hR\n1SNOmCeCPg/undWGa5dtyoh8rJwvTKIlhULolojsaN3CczZ3bo08ttnWXDOrlv+rSybiQDiGE1tD\n+KAzjKNDqQTpM0YMyB8jQeZ45VkK4DEiuoqZdwIAEQ1HqprNUtVFVsPvnyOiwQBGMfMLRNSIVIM/\nIQfj0PsJtjQyPSF0+Zrd6S653b1xNAUqZ6+X7rlCIYrtdp47t/YejBjPtd4Emhvz55peLT/3/O7e\nOKKJJG56/M2sNJjWkPEYD/cY30f1XMF5mPkeImoBsIaIfACSSBXf+A8Ai1TXWQ32uAzA9wEcxcwn\nEdFoAL9k5vMcGb0F3BLs4UTCcrH3KGWzQwB9XqDrgKp4I9wiKzq5m7+//vgc+DyErp54WpNqafQh\nGPAiFMif//F4Ep3hKK5dvikrj6xfow9zHrYeMNUTT6CzO4ob/rA5fZ+7Lh6P1qaA4XMzxy8BIrYp\n+AbpJkZmLthgz+o2Yx6A0wCs0W68jYiOs3htXeGECa6YezihCRa6TzG7bUEoRK4ml0wyOrujWZrU\nvTPbEFBELfp8HrSGAlgye1I6yjHo88LjJdO2L7lzvLXJj7tXb01bQXbsO4y7V2/Fwm+0KcfulNwJ\nKYhoJIDvAfgUwD0AokQ0lJl3mV1nNdijh5mjGQ8Ts6ICM4e3KhEz93hPzNxpbpgo7VSbGJP7SCVx\noRB2k43189P7cwYisQTW7+rE/ZdMxLYF03D/JROxflcnIiZz2eMhpLrcAEQEj4dst30JRxP4+FAv\npi56CSfd/ASmLnoJHx/qNQ1WkQARx1kB4AMATUiZE5MAHi10kdWF7K9E9CMAjUR0NoDfAfhTkQOt\naVSh90GfcfO/RCKZd7y7N45fXTLRMCFUmSjt9zjTJkaCOoQisRu+rp7LinB6m8UDGr3GhbrNgkPs\nFhsQeXGcbma+h5l/BN78GMEAABu3SURBVKBNU6BChS6yupD9CEAXgHcAXAvgOQC3FDtSt2Fnl+nx\nEFpDfiyZPQnbFkzDktmT0BryIxJPKnduRuWsvB6PYfi+cgfoULh+tYX9C+7BrnYSjiWwbM2urAaa\ny9bsQiSmCKc3uY9xY80Elq/dnXX/5Wt3p4NDMtHnuN20GZEXx3mBiOZoVr+EZmosiNWoxQSA+7VX\nXWHXBq5HHBrZ31W7QMMdXUOqQCqArAAP1Q5Q303aDX3ORW83k+s4lwKsQiHsaidBvwczJgzGjSuO\nBFeYNcpsUgQ6mcrE8zuw8Nnt6eM+D+Hqc0Yq57huPgdgKTir2JQDQck1SJkVHwAQBbAMqUBDU0z/\nU0S0jJlnEdFGGPQfU5ULqSUyd3sA0rs9s5YSRucvmT1JGSJsJ6zdLAzeiWCMSDyZ3sXqDu/la3en\n2s1I807BBLspGuFoAjeu2JwlKzeu2GwqK00BX57MdUfV4ffzzhmZ1zopHE04NsetphxIZKM1mLlf\nMdeZht8T0WBm3kNEJyke+m4xDy2GSoUUJ5kx+pYnEc8wJ/o8hK23n4/ObmPNa8ytT+Wdv23BNOw/\nbBApFfIbanBmGl8po6RUf++2BdPSGqKgpCreoIrJil3rhYls7T+cH07fGgrgQCRfVo4O+bH3YE+e\nZjfoqEbDsPzWpgA+8y/GMlqKOS6RjYaoai36AIwH0JJx+KcAbgLwnip60VQjY+Y92reXM/PNOQ/8\nCYCb86+qLYw0pnnnjMyKfAIKa15mGpMdTarYpFOrSOKzUCxmc9NIIwnH1HNNj1rMLQKskrn93T14\n4FuT0Nzow+GeON7t6MLRTf60r00//9rlm0xltBRz3MyqIzKVx1NIFdvoyjj2GQD/DOC/ABguZFb1\n6PMNjl1gZ3RuJbeN+vXnjcLM04YgFLAf+aQKX7cb1l7KMHi7LTQEIROjuWm7JVHAi2uWbULbbc9g\nxE1PoO22Z3DNsk2mvrMT+odw5aOpIsNXProeJ/QPORqd2BckstEWA5j5bGaerr+Q0sT+FzMvU11U\nyEd2JYDvAhhNRBsyftUCwD2lA/pAJJbElr0H07vDwz1xPPzX9zB13PEl9VUBlbGrl1rjE+qPzOhE\n3Se1bM0uXDZlhOFcU2lqZqWo7Ghe3b3xss5xsXLY4mGDY48UuqiQRvZ7ABcDeEL7qr++wMyzbA7Q\nlWS2Udd3ezMmDMar736SpalZ0bzsUMmWEpL4LDiJHp04f9UWjLn1ScxftQUzJgxG0O8xnGvKXEy/\nN0/m7rxovKnGozq/nHNcrBy2+BMRLSSifyWiflpd35WFLrJUaxEAiGgcjnSFfpmZtxQ/VvtUW7PA\n+dPH4r4XduDqs0di5LHNCEdTEVWOBFwkGd3RuGFTQLGrVzVVseJXW63Frp6Y4VxeMntSWgPLbaCp\n8qk99PLOvCjEOZOHK+//21feyzv/sikjlDJUKiuIRC3moQr2eAMprex4AP8A4DsAnmXmM81uZukT\nkYiuBnA1jqyMvyei+5j5lxYH7VpUu72RxzbjiTc/QkdXr7NRg5om1toUELu6UBOY+ao+6erNiyoc\n0BSA1+vJy+cK+b2YdfrQvOi/oM+ryAszPl+lCZUyutBuflod083M9wAAEW1m5igRFazsYfUdvRLA\nacx8WHvATwD8DUDNL2Rq+3Yc2xZMs7W7srIr0yOc5k8fK3Z1oSZQyZCZb6uJyFBWVD61w72xrKLB\nHV09CDX4bEdRSnRhxXmRiOYg1XvMcmUPq1GLhOzunDFUiRml1Kjs200Be/Z1qz4vXQO874UdSh+c\nILgJpY/IpCKHSlaMfFuNPg+aG/yYuzQVtTh36Xo0N/jR6DP2walkUaILq4KrAfwaQATAKDhR2SOD\nRwG8RkQrtJ+/BguRJLWAU1F8Vnd7+u511Rt7ASAd6eWkD04Qyklm/dFMX5iZpqaKcjTSjDJrMwKF\nNTu71XfEClI+mPmoYq6zpJEx888AzAUQRmql/C4z313MA92IExFOVnd7mbvXJ978CPNXbUFnd1QW\nMcG16PVHMzWmznAs7dvKrU4f9HuVUY5GKOuVBry2O0VIdGFlIaLZRDRE+34KEV1PRP9Q6LqC2wyt\nCvEGZj4FwOt9H2p9YjWXRPK4hFrDTANSVfBQ1WBsCuRrWKpai3ar70RiSUPZA1LRyyKPZeEGAP+p\nLV6/AfBbpNqG/ZPZRQU1Mq3y/d+J6AQnRlmv2MklkTwuoZYws0aMHdQ/q+/Y2EH9bfvOVJpdMZU9\ncmUPQMXyOeuUmLbmXADgMWa+A9l1Fw2xavg9BsDbRPQqgG79IDNfWMxIax1VdGLmbq9b3+HF1Ds8\nyT0RqhU7c9Osw4NK89p6+zTs2HcY972wA6ve2Hukmr3CzzygKWDZB2en+o5EMpadLiK6FsBlAGZT\nqu13wTfa6n/ip30ZmRFE1B+p6JRxSLWIuYyZX3X6OeWmUC5KyO81rIKfm6siFbMFnWqTFbtzU6+w\nYbXvWCjgw5hbn0yfN3JgE2ZMGGyq2XmI0KK1YGlp9KeeS6TsFWY1r0siGcvObKSaN9/FzG8QUROA\nqwpdVNC0SERfAXAyADDzc5mvPg74XgBPMfNnAJwC4O0+3q8qKNQlV/X77mh2B2q73XaFmqZishKP\nJ9HVE0OSGV09McTjSdtzM7Ne6bYF03D/JROxZe9BZafmHfsOp+9744rNmP35YVi5cQ/C0QS23j4N\nq687E9NPGZQ+v7s3bvjcTCuI1Y7PuTjVAdpOl/l6RmvT8hMAB4noAgCNzPzXQteZLmRE9AsAPwZw\nAoCfEZEjbVuIqB+AM5Fy5oGZo8x80Il7V5pCOzj1732S0yLkUUlZiceT6AxHc6INo2j0qqP+jMit\nV3rVYxswaWirsnbifS/syLpvS6MfM08bgt++8l46ivGHXxqD688bla6dqKKv/mYn6iRWsm6q2yCi\ns5AqSD8LqVJVjxPRFwtdV0gjOxvAWcx8A1LC5JRPbASADgC/JaKNRPRrTYV0PeFoAvPOGYnV152J\nd3/yZay+7sx0/zL997k7vHnnjERXTwxbb5+G+dPHYtmaXcrdqt2doOB6Si4rKm0hEj+Sn6VrSNcu\n34RI3J6WEoknje8TS2Dlxj2YP30stt6e0tRWbtyTzqHU79vdG8fytbux8NnthppabzxpW9uxqiE5\notWJdcUOPwNwLjN/E8AHSLUQ+7dCFxVayKLMHAcAZu62cL5VfAAmArifmScgFUDy49yTiGguEa0j\nonUdHR0OPbq0BH0ezDxtSFYOzMzThiDoS711uTs8vb/ZVY9tyMmZkZwWAUCJZcVMW3Cqn5dZFGJm\nvtjSv72PmacNMaxWv/j5HXnXtzT6cennh6FbK+xtVduxqyH1WasT64odGpj5Xe17YuYIgEChi0yr\n3xNRGMA7+o8Axmg/EwBm5onFjFTLEXiNmYdpP08B8GNmVjbrrJaK3oWitVTV8jOjnDLv0d1rXOU+\nlTPjk6hFd+H4P6fUsmI2X5nZ9ty0W7U+tzr9zo4uTB41EKGALx21ePXZIzF/1RbDcRAIVyw1lzc7\nf3MpIhHL/TyXoKp+vwnAGcwcIaK3kKq5OImZv2F2s0Lv4snFjdEcZv4fIvqAiMYw81YA5wL4eyme\n5SRWorWs7L4yI6bMdr2ZuSx1POHrmlLLitl8TSZYWVXeKOpPJR9HB1M+LqP7zJgwOC+aMej3orM7\nivmrtqQ6TQxsyhuHXu8UBNvaTrk1JN0KY7UKf51zK4DBALYjVZg+gFQovimmn44ZKp4pRPQKM08u\nfGYW1yCVwR0AsBPAHJvXlx0rOSV2u8FK91jBAiWTlULzrzWUnZ8V9Hnh8xl7GMwqaahqIeo+Ml0j\nW7lxj2Hn6KDPgwcvbUfQ70E4mupfFo4mAIZt+Sm3zEm1Husw858yvp8LAER0DYBfmF3nlM/LtvOZ\nmTcxczszj2fmGcx8wKGxlAwrOzm7UU7SPVYoRCllpdD88/k8aGn0p/K0Gv3KRSyZZIQa1L4w1fFZ\npw/N8ifPOn2oYYUNr9eDkN+Lzu4jNRuveGQdEskkFs9qsyU/lZA5qdZjDSL6PhG9QUTv6S8Ad2vf\nX6u6zqntR13EkSp3cr0JMDjd3dbO7kt2a0IlKXb+5frCPATsO9SrrGbf1wobgLFF5LuPbcBvvt1u\na/wic1XN1QCmATik/cwAXkQqgj6susgpjawuMNrJ3XXxeNy68k3MXboe+7ujSCSStndfslsTKond\n+WcU9dfdG8dftu0z6KHXpq5yr+gXpkJlEWn0e23Lj8hc1bKXmd9n5k7tdQDAJ9r3PaqLnNLIan4W\n6DvQ1qYjfZV27w/jZ09tTee9FOpuKwi1gLGveBPuuPBk3P301rTP64POMJoCPvQkkli+dneWL2z5\n2t2p/mJe63tpM9+WHh1pVeakjml1wsznWjmWi1ML2bcduk9VYhyN1YZFz27LSt7M7IEkNRKFWkWl\nGQ0ZEEJHVy8uWPwyTh3WirsuHo9YIomWoB+Ln9+Bhc9uT5/v8xC+f+4oe89VRP8FfR5bMid1TGuP\nQiWqDhBRp8HrABGlZzIzv1H6oVYO48z8TbjuvNFZ5+X2QJIsfqEWUdUf7O6N444LT05XqPnZU1vx\n3cc2FFWlxqjyRman6W0LpmHJ7EloDfkRiSdtyZxU2qg9Cmlkx5RlFFWO2Q70jBEDsnJjzLrVCkIt\noMyLCngxYeFfEM+okOHzULrzstU8KpXG1BryozMcy9ekmgO2ZE4qbdQehfLIsrYoRNQKoDHj0F7U\nAepoxbhhD6R554zMq2IgeWFCraCK+gvHjOd+JJZMa1KZsmInOlHPR7PT8VlyN90LES1k5uv1r4XO\nt+RpJaILiGgbgD0A1mhfn+/bUN2DMu8k4MvKsfF6PQVrLQpCLWAU9aea+41eDzrDsZwq+jFlbUOz\n2owqTUpyN2uOs7WvZ1k52er2YwGALwB4mpknaGX1L7I/NndiJ+8ks9I3cKSKwYOXttuK0BKEasJK\nlF8kbhydOGfycFtdllUakyofza7GJ3lktYfVT9Y4M3cA8BARMfMzSFXkrhus5p2I/V2oNaxWiw/6\nPVnV7PVODnZlwkxjMjoe9NnT+ADJI6s1rC5kn2o9kF4BsJSIfg4gWbphlZZSdmtVRWiputgKQrVj\nNcovHE3gxhWbs867ccVm212WPR7C0cHs6MSjg/6sqjmZvcHsRi0KtYfVhWwGgB4A1yFVLuRDAF8p\n0ZhKSqm7tYYCxl1vRSMT3IpVjcqp/mWJhHFnalXVHLGC1CS2PpCtLmQ3MXOCmWPM/BtmXgigYCRJ\nNVLqHJJwNLvr7fzpY7Fy4x7p7Cy4Fqsaldl5drosh2PGnamVeWE2Nb5KU0qLUA2xPOerKVYXsvMN\njikb+1Uzpd69hfxew8gtiYgS3IrVKD+z8+z4pMw0u76MrxootUWoVmDmn2V+LYRp1CIRXQnguwBG\nE9GGjF+1AKh8u+YicLJemxG6HT83gsrr9Uh9N8GVWI3ycyoaUBWd2N0bV3amdksUopWehoJ9Cmlk\nvwdwMYAntK/66wvMPKvEYysJZpFPTu2UvF5PXn6Z7MQEN2NVo3IiGjDkV1XL9yplyC1RiOLPKw3E\nbO2DlIjGAdC7QL/MzFtKNioD2tvbed06Z5RAI80oHEvgikfWZe0CzxgxwLGd0uHeeEnvL1QFVfHp\n6aSs2MUpq0MikUQ4lsiyakTiSdfLkHwOpHFUVqxW9rgaKe1siPb6PRF9z8mBlJNKRD7JTkyodZy0\nOhhZNWpBhtzkz6sERHQLER1lcPwUIjpPdZ3VYI8rAZzGzDcz880ATkfKd1YzlDryyW2RVYJgl3JE\nBLtdhjL9eVYiOOuQHwP4CxENzjn+MYCfqi6yupARgFjGzzFUiRnFKUq9U5KdmFDrlCMiuBZkyC3+\nvAqxHcC/AXiOiMbqB5n5fwD4VRcVilr0MXMcwKMAXiOiFdqvvgbgkT4PuYoodeSTmyKrBKEYSh0R\nLDJUFxAz/x8i+gjASiK6iZn/qC1qUdVFhTSytUA6ln8ugDCACIDvMvPdDg28aij1Tkl2YkItU46I\nYJGhmocBgJlfA/BFAHOJqAPAagA/Ul1kGrVIRBuZeYLDAy2KvkZilTOHS/LF6paq+CdXW9RiqSOC\nBVdiKCtEdAwzf5JzzM/MMaPzdQrNooFEpCxFpZWqqnpUHWdL4WQt57ME4f+1d7exclR1HMe/P1rD\nrRQTqWiIVKtVMFpp0cILQMKDJgQbrAYiTW2sAUwML0AjEkWBNPQF0VA1BLRWeRBiCbYo6QsMBLFW\n5OEWaAsUKNgQUWJb0UhBC/X+fTFn697t7t69DzuzZ/f3SZrdOztz5szc++9/zpnZc3pNrcUEHHjt\nh6cNrRyNSSwta5vEYOyuxWnATIqRPJr9y0K3n6aqal9mOeiHpw2tt43VIns5IlaUUpMuKvOK0Fef\nZqPV7p019lLk9rSh9a6xEllf9IW1e5pqqvvoy9yXWQ78tKF121hdi2eWUosuK/P7J/3yXRezqeSn\nDa2b2jYRIuKVdp/noswrQl99mpmVa2D6upo9TdUP+zIzG3SdDlFlZmbWk5zIzMwsa5UmMknTJD0u\naUOV9TDrdY4Vs9aqbpFdAmyvuA5mOXCsmLVQWSJL8818GlhTVR3McpBTrIyMBHv37Wck0us4Bgae\nzLY22KpskX2fYjTjkVYrSPqypGFJw7t37y6vZma9JYtYmcwM0VM5u7QNnkoSmaRFwK6I2NxuvYhY\nHRELI2LhkUceWVLtzHpHL8ZKq5bTZMYZ9RilNhlVfcnpZOAcSWcDQ8DbJN0WEV+oqD5mvaqnYqXd\n7A6TGWfUY5TaZFTSIouIb0bE0RExBzgfuN9JzOxgvRYr7VpOrUa5f62D+14eId8mo+qnFs0sI+1a\nTs3GGf3B+Qu4adPOMe97eYxSm4y2M0T3kipnvTXrUE8MqNnNWNm7b3/b2Z7rZ4h+bd9+btq0k+vu\n29F03UaeWX2gTOkv1i0yM+vYWC2n+lHuDzt0Oj+8//lR27e77+UR8m2iPKKtmXVsPLM7eG4+K4tb\nZGY2Lp22nHzfy8riyyIz6wrPzWdlcSIzs67x3HxWBnctmplZ1pzIzMwsa05kZmaWNScyMzPLmhOZ\nmZllzYnMzMyy5kRmZmZZcyIzM7OsOZGZmVnWnMjMzCxrTmRmZpY1JzIzM8uaE5mZmWXNiczMzLLm\nRGZmZllzIjMzs6w5kZmZWdacyMzMLGtOZGZmljUnMjMzy5oTmZmZZc2JzMzMsuZEZmZmWXMiMzOz\nrDmRmZlZ1hQRVdehI5J2Ay9WXY8p8g5gT9WVKNGgHO+eiDir6ko4VrI3CMc8pbGSTSLrJ5KGI2Jh\n1fUoy6Adr02dQfzbGcRjnix3LZqZWdacyMzMLGtOZNVYXXUFSjZox2tTZxD/dgbxmCfF98jMzCxr\nbpGZmVnWnMhKJOlnknZJerLqupRB0mxJv5W0XdJTki6puk6WB8eKY2U83LVYIkmnAnuBWyNiXtX1\n6TZJRwFHRcRjkg4HNgOLI+LpiqtmPc6x4lgZD7fIShQRG4FXqq5HWSLi5Yh4LL1/FdgOvLvaWlkO\nHCuOlfFwIrNSSJoDHA88XG1NzHqbY2X8nMis6yTNBNYBl0bEv6quj1mvcqxMjBOZdZWkt1AE5u0R\nsb7q+pj1KsfKxDmRWddIEvBTYHtEXFd1fcx6lWNlcpzISiTpF8AfgWMlvSTpgqrr1GUnA8uAMyQ9\nkf6dXXWlrPc5Vhwr4+HH783MLGtukZmZWdacyMzMLGtOZGZmljUnMjMzy5oTmZmZZc2JzMzMsjYQ\niUzSZyWFpA+ln+fUpoeQtFzS9V3a74PdKLeD/T4gaWGbz2dKulHSC5Iel7RZ0kXpswPnps324z5n\nkm6WdG56v0bSh8ezfdruakl/Sd+xeVrSkg62WVy/L0krJH1yvPseFI6Vgz53rGRgIBIZsATYBJxf\n5k4j4qQy9zcOa4B/AB+MiOOBs4Ajytp5RFw4iekpVkXEAuAzwI/TsD7tLAYOBGdEXBkR901w34PA\nsTKaYyUDfZ/I0iCcJwMX0Do4Z0u6R9Kzkq5K24262pL0dUlXp/cPSFolaWOaCO8ESesl7ZB0Td02\ne9PraWmbX0p6RtLtaUiaVnW+UtKjkp6UtLq2birjWkmPSHpO0ifS8hmS1kraKukOYEabsucCJwLf\njogRgIjYHRHXNll3SNJNkralq9HTJ3rOGso9cBUsaa+klZK2SHpI0rta1b1eROwAXgfensq5KJ2z\nLZLWSXqrpJOAc4DvpivTuQ1Xu2em49qmYiLHQzvZd79yrBxUtmMlk1jp+0RGcZVxT0Q8B7wi6WNN\n1jkRWAosAM5Tm66GOm9ExKnAj4BfAxcD84DlkmY1Wf944FKKK573U/yH0cr1EXFCmlBwBrCo7rPp\nEXFiKuuqtOwrwOsRcRywEvh4m7I/AmypBeYYLgaIiI9SXKnfImkofTaRc9bMYcBDETEf2Ahc1MlG\n6fe4IyJ2pUXr0zmbTzGX0wUR8SBwN3BZRCyIiBfqth8CbgY+n45vOsV5HGSOldEcK+QRK4OQyJYA\na9P7tennRvdGxN8j4t/AeuCUDsq9O71uA55KE+PtA/4EzG6y/iMR8VIKiieAOW3KPl3Sw5K2AWdQ\nBFRNbVTszXVlnArcBhARW4GtHdQfAElXpCuwvzb5+BTg56ncZ4AXgWPSZxM5Z828AWxI7+uPqZWv\nSnqWYq6mq+uWz5P0+3TOljL6nDVzLLAz/acNcAvFeRxkjpU2HCu9GyvTq65AN6WrvTMofnEBTAMC\nuKFh1cYBJwPYz+hEP9Swzr70OlL3vvZzs/Nav85/W6xTu/q5AVgYEX9O3Q31+66V01hGp4NmPg3M\nl3RIRIxExEpgZa1rp7E6bcqZyDlr5s34/4CfLc9LnVUR8T1JnwNulTQ3Iv5DccW4OCK2SFoOnDZG\nOe2ObeA4VppyrBR6Plb6vUV2LnBrRLw3IuZExGxgJ3B0w3qfknSEpBkU3St/AP4GvFPSrNQfvIhy\n1P6g96i4Z3FuB9tspLiyQtI84LhWK0bE88AwcI2kaWmbIZr/sdaXewzwHuDZ9Fml5yzN1zQMfDEt\nOhx4WcUN7aV1q76aPmv0DDBH0gfSz8uA33WpujlwrDRwrBzQ87HS74lsCXBXw7J1wLcalm2i6BZ4\nAlgXEcMR8SawgqJZvoHil9l1EfFP4CcU3TC/Ah7tYLMbgZmStgLfAB4ZY/0LgVnA85I2A/cBlzdZ\n7wZgWuqCuANYnrqEoDfO2Qrga5IOAb6T9ntvw37XApelG9VzawvTlemXgDvT8Y1Q3MMZVI6V5hwr\nGcSKp3ExM7Os9XuLzMzM+lxfP+zR6yTdBbyvYfHlEfGbKSr/YaDx+x7LImLbVJTfLZKuAM5rWHxn\nutluA8ix0pxjpeCuRTMzy5q7Fs3MLGtOZGZmljUnMjMzy5oTmZmZZc2JzMzMsvY/XquIpfI8qewA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x793022657e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(liver_df, col=\"Gender\", row=\"Dataset\", margin_titles=True)\n",
    "g.map(plt.scatter,\"Albumin_and_Globulin_Ratio\", \"Total_Protiens\",  edgecolor=\"w\")\n",
    "plt.subplots_adjust(top=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "79d7d365-9eab-453e-adc1-97b02461c8a1",
    "_uuid": "730a2d0a33cdb3db4ec8e783dbc9e03470af1f4f"
   },
   "source": [
    "# Observation:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "824ec774-42ef-4fad-849f-82893dd03849",
    "_uuid": "5b9a21eded807cc989db8eab46435ee77bc74e50"
   },
   "source": [
    "From the above jointplots and scatterplots, we find direct relationship between the following features:  \n",
    "Direct_Bilirubin & Total_Bilirubin  \n",
    "Aspartate_Aminotransferase & Alamine_Aminotransferase  \n",
    "Total_Protiens & Albumin  \n",
    "Albumin_and_Globulin_Ratio & Albumin  \n",
    "\n",
    "Hence, we can very well find that we can omit one of the features. I'm going to keep the follwing features:  \n",
    "Total_Bilirubin  \n",
    "Alamine_Aminotransferase  \n",
    "Total_Protiens  \n",
    "Albumin_and_Globulin_Ratio  \n",
    "Albumin  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_cell_guid": "af447fa3-486a-4c9f-a0bb-143d240a8b41",
    "_uuid": "6b2c57a7dc86c5a48f4749fb0ff55f76bf1ce2c1",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:40.699006Z",
     "iopub.status.busy": "2023-05-27T13:28:40.698640Z",
     "iopub.status.idle": "2023-05-27T13:28:40.719222Z",
     "shell.execute_reply": "2023-05-27T13:28:40.718215Z",
     "shell.execute_reply.started": "2023-05-27T13:28:40.698949Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0   65  Female              0.7               0.1                   187   \n",
       "1   62    Male             10.9               5.5                   699   \n",
       "2   62    Male              7.3               4.1                   490   \n",
       "\n",
       "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                        16                          18             6.8   \n",
       "1                        64                         100             7.5   \n",
       "2                        60                          68             7.0   \n",
       "\n",
       "   Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "0      3.3                        0.90        1  \n",
       "1      3.2                        0.74        1  \n",
       "2      3.3                        0.89        1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liver_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e1bd4a7c-ee90-480d-89d4-981ab14ad59f",
    "_uuid": "de6437c17e7114936ceaafcf2c292cd8660391f0"
   },
   "source": [
    "Convert categorical variable \"Gender\" to indicator variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_cell_guid": "21a8680c-ee95-4943-97a4-699a6936683a",
    "_uuid": "e3eda36cb69d136d9f05502abae8760acf45349d",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:40.721014Z",
     "iopub.status.busy": "2023-05-27T13:28:40.720688Z",
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     "shell.execute_reply.started": "2023-05-27T13:28:40.720952Z"
    }
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   Gender_Female  Gender_Male\n",
       "0              1            0\n",
       "1              0            1\n",
       "2              0            1\n",
       "3              0            1\n",
       "4              0            1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(liver_df['Gender'], prefix = 'Gender').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_cell_guid": "20bc6e16-444b-4319-8ad0-bc3fee31baac",
    "_uuid": "c0072980444041499d7be75dd70863794e2d6a6a",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:40.741241Z",
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     "shell.execute_reply.started": "2023-05-27T13:28:40.741182Z"
    }
   },
   "outputs": [],
   "source": [
    "liver_df = pd.concat([liver_df,pd.get_dummies(liver_df['Gender'], prefix = 'Gender')], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_cell_guid": "bd025bf6-cad5-4d92-a4bc-d8924b46abe5",
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    {
     "data": {
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       "      <th></th>\n",
       "      <th>Age</th>\n",
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       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
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       "      <th>Gender_Female</th>\n",
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       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
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       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0   65  Female              0.7               0.1                   187   \n",
       "1   62    Male             10.9               5.5                   699   \n",
       "2   62    Male              7.3               4.1                   490   \n",
       "3   58    Male              1.0               0.4                   182   \n",
       "4   72    Male              3.9               2.0                   195   \n",
       "\n",
       "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                        16                          18             6.8   \n",
       "1                        64                         100             7.5   \n",
       "2                        60                          68             7.0   \n",
       "3                        14                          20             6.8   \n",
       "4                        27                          59             7.3   \n",
       "\n",
       "   Albumin  Albumin_and_Globulin_Ratio  Dataset  Gender_Female  Gender_Male  \n",
       "0      3.3                        0.90        1              1            0  \n",
       "1      3.2                        0.74        1              0            1  \n",
       "2      3.3                        0.89        1              0            1  \n",
       "3      3.4                        1.00        1              0            1  \n",
       "4      2.4                        0.40        1              0            1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liver_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_cell_guid": "c6011e12-8e58-4e38-aa82-33bd336723ce",
    "_uuid": "6b834a47790a238c390fc7b552587c6fc6bcb24d",
    "execution": {
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     "shell.execute_reply.started": "2023-05-27T13:28:40.787468Z"
    }
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
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       "      <th>Gender_Male</th>\n",
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       "      <th>count</th>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
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       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>44.746141</td>\n",
       "      <td>3.298799</td>\n",
       "      <td>1.486106</td>\n",
       "      <td>290.576329</td>\n",
       "      <td>80.713551</td>\n",
       "      <td>109.910806</td>\n",
       "      <td>6.483190</td>\n",
       "      <td>3.141852</td>\n",
       "      <td>0.947064</td>\n",
       "      <td>1.286449</td>\n",
       "      <td>0.243568</td>\n",
       "      <td>0.756432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.189833</td>\n",
       "      <td>6.209522</td>\n",
       "      <td>2.808498</td>\n",
       "      <td>242.937989</td>\n",
       "      <td>182.620356</td>\n",
       "      <td>288.918529</td>\n",
       "      <td>1.085451</td>\n",
       "      <td>0.795519</td>\n",
       "      <td>0.319592</td>\n",
       "      <td>0.452490</td>\n",
       "      <td>0.429603</td>\n",
       "      <td>0.429603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>175.500000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>298.000000</td>\n",
       "      <td>60.500000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>2110.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>4929.000000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "              Age  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "count  583.000000       583.000000        583.000000            583.000000   \n",
       "mean    44.746141         3.298799          1.486106            290.576329   \n",
       "std     16.189833         6.209522          2.808498            242.937989   \n",
       "min      4.000000         0.400000          0.100000             63.000000   \n",
       "25%     33.000000         0.800000          0.200000            175.500000   \n",
       "50%     45.000000         1.000000          0.300000            208.000000   \n",
       "75%     58.000000         2.600000          1.300000            298.000000   \n",
       "max     90.000000        75.000000         19.700000           2110.000000   \n",
       "\n",
       "       Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "count                583.000000                  583.000000      583.000000   \n",
       "mean                  80.713551                  109.910806        6.483190   \n",
       "std                  182.620356                  288.918529        1.085451   \n",
       "min                   10.000000                   10.000000        2.700000   \n",
       "25%                   23.000000                   25.000000        5.800000   \n",
       "50%                   35.000000                   42.000000        6.600000   \n",
       "75%                   60.500000                   87.000000        7.200000   \n",
       "max                 2000.000000                 4929.000000        9.600000   \n",
       "\n",
       "          Albumin  Albumin_and_Globulin_Ratio     Dataset  Gender_Female  \\\n",
       "count  583.000000                  579.000000  583.000000     583.000000   \n",
       "mean     3.141852                    0.947064    1.286449       0.243568   \n",
       "std      0.795519                    0.319592    0.452490       0.429603   \n",
       "min      0.900000                    0.300000    1.000000       0.000000   \n",
       "25%      2.600000                    0.700000    1.000000       0.000000   \n",
       "50%      3.100000                    0.930000    1.000000       0.000000   \n",
       "75%      3.800000                    1.100000    2.000000       0.000000   \n",
       "max      5.500000                    2.800000    2.000000       1.000000   \n",
       "\n",
       "       Gender_Male  \n",
       "count   583.000000  \n",
       "mean      0.756432  \n",
       "std       0.429603  \n",
       "min       0.000000  \n",
       "25%       1.000000  \n",
       "50%       1.000000  \n",
       "75%       1.000000  \n",
       "max       1.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liver_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_cell_guid": "f8b7af4e-9b7c-4943-bc0c-d3de640b4544",
    "_uuid": "047946d891f4734a97a0fcc5c60192b4b4b7d74e",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:40.840182Z",
     "iopub.status.busy": "2023-05-27T13:28:40.839922Z",
     "iopub.status.idle": "2023-05-27T13:28:40.863152Z",
     "shell.execute_reply": "2023-05-27T13:28:40.862305Z",
     "shell.execute_reply.started": "2023-05-27T13:28:40.840143Z"
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   "outputs": [
    {
     "data": {
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       "      <th>Alkaline_Phosphotase</th>\n",
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       "      <th>209</th>\n",
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       "      <td>189</td>\n",
       "      <td>23</td>\n",
       "      <td>33</td>\n",
       "      <td>6.6</td>\n",
       "      <td>3.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>230</td>\n",
       "      <td>24</td>\n",
       "      <td>46</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>35</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>180</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>27</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>106</td>\n",
       "      <td>25</td>\n",
       "      <td>54</td>\n",
       "      <td>8.5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "209   45  Female              0.9               0.3                   189   \n",
       "241   51    Male              0.8               0.2                   230   \n",
       "253   35  Female              0.6               0.2                   180   \n",
       "312   27    Male              1.3               0.6                   106   \n",
       "\n",
       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "209                        23                          33             6.6   \n",
       "241                        24                          46             6.5   \n",
       "253                        12                          15             5.2   \n",
       "312                        25                          54             8.5   \n",
       "\n",
       "     Albumin  Albumin_and_Globulin_Ratio  Dataset  Gender_Female  Gender_Male  \n",
       "209      3.9                         NaN        1              1            0  \n",
       "241      3.1                         NaN        1              0            1  \n",
       "253      2.7                         NaN        2              1            0  \n",
       "312      4.8                         NaN        2              0            1  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liver_df[liver_df['Albumin_and_Globulin_Ratio'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_cell_guid": "a22f10fd-a319-401f-9a4a-1c02a8357975",
    "_uuid": "a00f9b9d4f6b3c086b4bf8b328955a6b49642057",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:40.864822Z",
     "iopub.status.busy": "2023-05-27T13:28:40.864488Z",
     "iopub.status.idle": "2023-05-27T13:28:40.870907Z",
     "shell.execute_reply": "2023-05-27T13:28:40.869870Z",
     "shell.execute_reply.started": "2023-05-27T13:28:40.864767Z"
    }
   },
   "outputs": [],
   "source": [
    "liver_df[\"Albumin_and_Globulin_Ratio\"] = liver_df.Albumin_and_Globulin_Ratio.fillna(liver_df['Albumin_and_Globulin_Ratio'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_cell_guid": "ab697d55-f4e6-4e43-87a8-e853f893349c",
    "_uuid": "73b9eacd147f702a0cb8bdd00bb0e6d52d8b8cb1",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:40.872587Z",
     "iopub.status.busy": "2023-05-27T13:28:40.872309Z",
     "iopub.status.idle": "2023-05-27T13:28:40.883114Z",
     "shell.execute_reply": "2023-05-27T13:28:40.882162Z",
     "shell.execute_reply.started": "2023-05-27T13:28:40.872537Z"
    }
   },
   "outputs": [],
   "source": [
    "#liver_df[liver_df['Albumin_and_Globulin_Ratio'] == 0.9470639032815201]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_cell_guid": "f2cc0e7b-0e62-4cd4-99a7-e213b983b9e4",
    "_uuid": "cdab8936d73dfe0533c10d723739df09b73c23ad",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:40.885124Z",
     "iopub.status.busy": "2023-05-27T13:28:40.884745Z",
     "iopub.status.idle": "2023-05-27T13:28:40.914832Z",
     "shell.execute_reply": "2023-05-27T13:28:40.913762Z",
     "shell.execute_reply.started": "2023-05-27T13:28:40.885055Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
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       "      <th>Total_Protiens</th>\n",
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      "text/plain": [
       "   Age  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0   65              0.7               0.1                   187   \n",
       "1   62             10.9               5.5                   699   \n",
       "2   62              7.3               4.1                   490   \n",
       "\n",
       "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                        16                          18             6.8   \n",
       "1                        64                         100             7.5   \n",
       "2                        60                          68             7.0   \n",
       "\n",
       "   Albumin  Albumin_and_Globulin_Ratio  Gender_Female  Gender_Male  \n",
       "0      3.3                        0.90              1            0  \n",
       "1      3.2                        0.74              0            1  \n",
       "2      3.3                        0.89              0            1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The input variables/features are all the inputs except Dataset. The prediction or label is 'Dataset' that determines whether the patient has liver disease or not. \n",
    "X = liver_df.drop(['Gender','Dataset'], axis=1)\n",
    "X.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_cell_guid": "134edf24-b704-483f-91f6-a000aa7525a5",
    "_uuid": "13e14244f1f54357aba978576a2d1a9515dece6e",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:40.916565Z",
     "iopub.status.busy": "2023-05-27T13:28:40.916292Z",
     "iopub.status.idle": "2023-05-27T13:28:40.930602Z",
     "shell.execute_reply": "2023-05-27T13:28:40.929775Z",
     "shell.execute_reply.started": "2023-05-27T13:28:40.916526Z"
    }
   },
   "outputs": [],
   "source": [
    "y = liver_df['Dataset'] # 1 for liver disease; 2 for no liver disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_cell_guid": "551ce97e-ab7e-4ffc-a9fd-f10ee1d9332a",
    "_uuid": "3ca13b008594f58b54943bd1c02f2984a10032e1",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:40.932408Z",
     "iopub.status.busy": "2023-05-27T13:28:40.932042Z",
     "iopub.status.idle": "2023-05-27T13:28:40.945324Z",
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     "shell.execute_reply.started": "2023-05-27T13:28:40.932337Z"
    }
   },
   "outputs": [],
   "source": [
    "# Correlation\n",
    "liver_corr = X.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_cell_guid": "8678807f-c33a-4efd-86de-23dc5b0acf80",
    "_uuid": "ab8a278a98ba9b59e8763198652c88233e6fa6d6",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:40.947561Z",
     "iopub.status.busy": "2023-05-27T13:28:40.946925Z",
     "iopub.status.idle": "2023-05-27T13:28:40.977914Z",
     "shell.execute_reply": "2023-05-27T13:28:40.976811Z",
     "shell.execute_reply.started": "2023-05-27T13:28:40.947494Z"
    }
   },
   "outputs": [
    {
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       "      <td>-0.042518</td>\n",
       "      <td>-0.029742</td>\n",
       "      <td>-0.002374</td>\n",
       "      <td>-0.082332</td>\n",
       "      <td>0.082332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <td>-0.019910</td>\n",
       "      <td>0.237831</td>\n",
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       "      <td>0.167196</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>-0.025645</td>\n",
       "      <td>-0.085290</td>\n",
       "      <td>-0.070024</td>\n",
       "      <td>-0.080336</td>\n",
       "      <td>0.080336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Protiens</th>\n",
       "      <td>-0.187461</td>\n",
       "      <td>-0.008099</td>\n",
       "      <td>-0.000139</td>\n",
       "      <td>-0.028514</td>\n",
       "      <td>-0.042518</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.089121</td>\n",
       "      <td>-0.089121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albumin</th>\n",
       "      <td>-0.265924</td>\n",
       "      <td>-0.222250</td>\n",
       "      <td>-0.228531</td>\n",
       "      <td>-0.165453</td>\n",
       "      <td>-0.029742</td>\n",
       "      <td>-0.085290</td>\n",
       "      <td>0.784053</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.686322</td>\n",
       "      <td>0.093799</td>\n",
       "      <td>-0.093799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <td>-0.216089</td>\n",
       "      <td>-0.206159</td>\n",
       "      <td>-0.200004</td>\n",
       "      <td>-0.233960</td>\n",
       "      <td>-0.002374</td>\n",
       "      <td>-0.070024</td>\n",
       "      <td>0.233904</td>\n",
       "      <td>0.686322</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003404</td>\n",
       "      <td>-0.003404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender_Female</th>\n",
       "      <td>-0.056560</td>\n",
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       "      <td>-0.100436</td>\n",
       "      <td>0.027496</td>\n",
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       "      <td>0.003404</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender_Male</th>\n",
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       "      <td>-0.093799</td>\n",
       "      <td>-0.003404</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                 Age  Total_Bilirubin  Direct_Bilirubin  \\\n",
       "Age                         1.000000         0.011763          0.007529   \n",
       "Total_Bilirubin             0.011763         1.000000          0.874618   \n",
       "Direct_Bilirubin            0.007529         0.874618          1.000000   \n",
       "Alkaline_Phosphotase        0.080425         0.206669          0.234939   \n",
       "Alamine_Aminotransferase   -0.086883         0.214065          0.233894   \n",
       "Aspartate_Aminotransferase -0.019910         0.237831          0.257544   \n",
       "Total_Protiens             -0.187461        -0.008099         -0.000139   \n",
       "Albumin                    -0.265924        -0.222250         -0.228531   \n",
       "Albumin_and_Globulin_Ratio -0.216089        -0.206159         -0.200004   \n",
       "Gender_Female              -0.056560        -0.089291         -0.100436   \n",
       "Gender_Male                 0.056560         0.089291          0.100436   \n",
       "\n",
       "                            Alkaline_Phosphotase  Alamine_Aminotransferase  \\\n",
       "Age                                     0.080425                 -0.086883   \n",
       "Total_Bilirubin                         0.206669                  0.214065   \n",
       "Direct_Bilirubin                        0.234939                  0.233894   \n",
       "Alkaline_Phosphotase                    1.000000                  0.125680   \n",
       "Alamine_Aminotransferase                0.125680                  1.000000   \n",
       "Aspartate_Aminotransferase              0.167196                  0.791966   \n",
       "Total_Protiens                         -0.028514                 -0.042518   \n",
       "Albumin                                -0.165453                 -0.029742   \n",
       "Albumin_and_Globulin_Ratio             -0.233960                 -0.002374   \n",
       "Gender_Female                           0.027496                 -0.082332   \n",
       "Gender_Male                            -0.027496                  0.082332   \n",
       "\n",
       "                            Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "Age                                          -0.019910       -0.187461   \n",
       "Total_Bilirubin                               0.237831       -0.008099   \n",
       "Direct_Bilirubin                              0.257544       -0.000139   \n",
       "Alkaline_Phosphotase                          0.167196       -0.028514   \n",
       "Alamine_Aminotransferase                      0.791966       -0.042518   \n",
       "Aspartate_Aminotransferase                    1.000000       -0.025645   \n",
       "Total_Protiens                               -0.025645        1.000000   \n",
       "Albumin                                      -0.085290        0.784053   \n",
       "Albumin_and_Globulin_Ratio                   -0.070024        0.233904   \n",
       "Gender_Female                                -0.080336        0.089121   \n",
       "Gender_Male                                   0.080336       -0.089121   \n",
       "\n",
       "                             Albumin  Albumin_and_Globulin_Ratio  \\\n",
       "Age                        -0.265924                   -0.216089   \n",
       "Total_Bilirubin            -0.222250                   -0.206159   \n",
       "Direct_Bilirubin           -0.228531                   -0.200004   \n",
       "Alkaline_Phosphotase       -0.165453                   -0.233960   \n",
       "Alamine_Aminotransferase   -0.029742                   -0.002374   \n",
       "Aspartate_Aminotransferase -0.085290                   -0.070024   \n",
       "Total_Protiens              0.784053                    0.233904   \n",
       "Albumin                     1.000000                    0.686322   \n",
       "Albumin_and_Globulin_Ratio  0.686322                    1.000000   \n",
       "Gender_Female               0.093799                    0.003404   \n",
       "Gender_Male                -0.093799                   -0.003404   \n",
       "\n",
       "                            Gender_Female  Gender_Male  \n",
       "Age                             -0.056560     0.056560  \n",
       "Total_Bilirubin                 -0.089291     0.089291  \n",
       "Direct_Bilirubin                -0.100436     0.100436  \n",
       "Alkaline_Phosphotase             0.027496    -0.027496  \n",
       "Alamine_Aminotransferase        -0.082332     0.082332  \n",
       "Aspartate_Aminotransferase      -0.080336     0.080336  \n",
       "Total_Protiens                   0.089121    -0.089121  \n",
       "Albumin                          0.093799    -0.093799  \n",
       "Albumin_and_Globulin_Ratio       0.003404    -0.003404  \n",
       "Gender_Female                    1.000000    -1.000000  \n",
       "Gender_Male                     -1.000000     1.000000  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liver_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_cell_guid": "7f17a7ba-96e2-4d91-899c-2378dc63f8fa",
    "_uuid": "44e9f67a5c8d33edaa35d750616a5ef8ae292412",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:40.979809Z",
     "iopub.status.busy": "2023-05-27T13:28:40.979426Z",
     "iopub.status.idle": "2023-05-27T13:28:42.189872Z",
     "shell.execute_reply": "2023-05-27T13:28:42.189040Z",
     "shell.execute_reply.started": "2023-05-27T13:28:40.979738Z"
    }
   },
   "outputs": [
    {
     "data": {
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B0Tq782b9enYyfJy9NTnVtxMaQgZXuuleVcKj8sXr1BRby91LRuaL4eF/aM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\nKl5FfPENdX8fEc+Kjj6Al5cXYeXdlzsNr1iJ6Ki8l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AL/cvcdD7Ba+nEPBf7h7gN28zj7AUuBf7r7rZF1\n+wKrgGfc/cpS9j0f6FnaNvEmbVM6yUlF505JTk5i06ZNJe63Kb34/aonJ7FydVB8+H1D0LV8xMiX\nOaz/Qdx3x838tPRnnn/5dSpUqMBlF5wTpXchO1JSnqsnJ5G2qeSeEGnp6SQnFb0ZDvK8FoBG9eth\nZixYtIQObbYP3TV/UTAswcZSji/RU3KuEkkrpbfLnzp2JIeFj189OSnvvy1la9OmNKpVq0aFChUK\nrE9KTmbr1i1kZWVRqVKlovulpQXbFboeJCcn5x03vztvv5np04LWfy1btuL2u+7ea+aL2puVes1O\nL/mzubT9cj+bAS4790y2bs3iymG356078dijOb+Y+eIkdpTn8mPTpjSqFnPNTk6uXuo1Oz1yTU5M\nLtiSOrdnTO7rDRo25usvP2Xbtm15xYolixeSk5Nd5LousZMeyXNCkc/mIM/bsrKoWNxncyRH+Xs8\nQfDvI/e4APUbNuabLz8rNs/pm/b++Xn2FpvSir8HS05OZuvWrSWez2mR79ZF7sGqB3nO/e69adMm\nkpKL9p5ITk5m9eq9Zy6h8qp6cnBupqUXHB0kbVNWgderJ1Uqsk2w3TYaNaga4yhlV1SqXYOsDUU/\nU7M2bCRx/5TINjUB2PZHwWty1vqgB1ylWnv3/JciUjoVo2LAgrGc3gFGufuZkXXNgEEhxFLR3Usa\n/2sJMAC4NbI8GCh+IhQpdogud7AdTSJc7H6ed7zcavV+TZvwj6uCcbG7d+nE5s1bePXNtznvjCFU\nrarh+8pKcSOx5c9XyfuVnufkpESO6H8AL499h/2bpdByv2Z8+uU3TJ01F4AKenBdZoo7Z//UubyL\nx88d3SBax5fiuTs5OTlFXyjl3N3p8zqSzMK5vOzyK0hLS2PlyhW8Mfo17rjtZv714KNUrlz5zwUv\nu6zEa/YOz7fSr9kAr48bz6dfTuLqS8+nxX7NWLx0GS+8NoYa1ZO56Kwhuxm57AzlOf4E1+zsfGss\n3/8W3Rb+xDW78N55H8DB+iOPGciH743lhacfZfBZF7Bp40aefephEhIqqAFBjBTOs5WS58L52pn/\nRrBbsN8Rxwzio/fG8uLTjzD4rAtJ2/gHzz/1kPIcQyXdgxV7zv7Z89kK308X/fdR4vViJ/8NyZ9X\nId8p5EBxt947pVDD/dxrRIHR4Ypp3G+2a8MDShkpLjlmRVZ74RW5566SKxLXVIyKjcOBTHd/OneF\nuy8DnjCzCsB9wKFAFeDf7v5MpMfTHcA6oCMwDTjb3d3M/gI8Gnlteu4xzSwJeALoRJDLO9z93UjP\npeOBqkBSJJ7ibAbmm1lPd58KnAaMARpFjj8QuAWoDPwGnOXuBcYSM7O6wNNA7ngJ17h7kdkGzexS\n4FKA++66jbNPO7WU//v2PNWTk9hUTK+G9IwMkpKLtrrNlZyUxB8bi7bA25Sekddat3r1oEVX104F\n5xLq1rkjI197g5WrV9N8v2ZFjiHRF+S56DAA6RkZxfamydsvKYkNG4u2/gnyvH2/oRedy+0PPs41\ntwaTdNbbdx/OHXwiL45+i9o1a0bhHciOVE9KYlNG8edyaTn+U8fO6wFV8N9Q7rVjd48vpZs7ZzY3\nDftH3nLHTp3p1/8QNmdkkJ2dXaBlbvqmTVSpUrXIMD658lrfFup1kZvLwi1xGzUOWvm1aduODh06\ncfGF5/Dlfz/nqKP/svtvTEpU+jW75M/m6slJbPij9M/mDRs38vyrb3DNZRcy8OgjAOjSoR2VKlbk\n0REvcvLxx1C7lq7bZUF5jk8/zJnJHTcNzVtu37ErB/Y/nM2bNxe9ZqeXfs3O6wGVXvBeLHeuqKSk\n4JrduEkzLrvyOkY+9wSffvwulpDAkccMxMyoWWvHk7LLzvthzgzuKpTnvpE852RnF+gdtaM85/aA\nyij02Zyb58R8eb70yusY9dwTfJaX50GRPGuOmViYM2cOw264IW+5U6dO9D/4YDKKuQfblJ5OlSpV\nSsxz9cg9Vnqh0Udyl3Ov38nJyWz84w8KSy9hZBLZfd061uSJe7vmLc+Ys4Grbpq1S8dK2xS0l05O\nLvjvIHd5U6Q3VFp6FrVqFG3clZxUMW8b2bNkrd9IlbpFr7WValZn24aNkW0iPaBqFuwBldsjKusP\n9VYWiWcqRsVGB/IVjQq5CPjD3XuZWRXgGzP7JPJat8i+K4FvgIPMbCrwLEFBaTHwRr5j3Qx87u4X\nmlkt4Hsz+yzy2gFAZ3f/fQexjgZON7PVQHbkv507sPPXQN9IQexi4Hrg74X2fwx4xN2/NrOmwASg\nXaFtcPcRwAiAFT/O2euaOTRJaVxkbqi1v65jy5YtNC00l1R+TVMa88EnRSfX/iV1Bf369gagUYP6\nReYRAvImzjZTC76y0rRxoyJzQ6359Tc2b9la7JxQefulNGLWp18UWf9L6kr69emZt1yrZg0eG34L\na9f9RnrGZpo0bsib4z+iTu1aNKxfN3pvRErULKUhy1ILzt20Zl2Q48JzPe2sxg3qU7FiBX5ZsZJu\nHbdfBpetWElCgtGkUcPdOr6UrkXLVjz86JN5y9USE/lt3TpycnJYtWolKSnb5x5ITV1OSqG5CPJr\n2LAhFStWJHX5cjp16rJ9v+W/kJCQQOPGRecoyFWvfn2Sq1dn9SrNERZrTRs3KjLX29pf1wXX7FLO\n56aNGzH7hwVF1v+yYgX9+vQCYNXqtWzblk3L/fcrsE2r5vuRnZ3N6l/XqUhRRpTn+NS8ZRvue+TZ\nvOWq1RL5/bdfycnJZvWqFTTONy/QitRfiswTlF+Dho2pULEiK1N/oUOnbvn2W4YlJNCw8fbr/eFH\nH0+/Q49k1cpUatasTfUaNbnwzAEccfRujYAuJWjesi33PPJc3nK1QnnOn9eVqctKzXP9SJ5XpC6j\nfb48r4zkuVG+PB929AAOOvQoVq9MpUbN2tSoUZOLzjyew48eGOV3KAAtW7bk0ccey1tOTExkXd49\n2KoCczulLl9eZD6o/HLvwZanptKpc+e89csjc1A1jhyrSUoKH8+dW2T/5cuXc8CBB0bjbUkhC37a\nxEXXTstbzticXcrWpVuxajNZWTk0S0lk5tztRcVmKdXIznaWrwgaoSxL3Uzno4t+DjdLSWTS5HW7\n/N+X2ElfuIQ6/XoUWZ/Upjlr3gseV2b89As5mZkkt23O75Om5G2T3KY5np1N+o9LyyxeESl7espd\nBszs32Y2y8ymAEcD55rZTOA7YB8gdwKZ79091d1zgJnAfkBbYKm7L/KgD+sr+Q59NDAscqz/EvSE\nyr2D//RPFKIAPgaOAs6gYKELIAWYYGZzgOsICmWFHQk8GYnhPaCGmVUvZru9Wp8e3Zg6fRYZGZvz\n1n0x6RuqVK5Ml47tS9yvd89u/L5+A3PmbS9ILVy0mFWr19C7R/AlqlKlSvTo2pkZs+cU2Hf6rDlU\nrVKFxo0aRPndSEn6du/C9zNmk7F5e54//+ZbqlSuTNeORWqsefp078Lv6zcUeOi1YPESVq5ZS98e\nXYpsX2/ffdi/aQrZ2dl8+NmXHH/EIdF9I1KiPt278P3MQjn+enKQ4w5td+vYlStVolvH9nzxv+8K\nrP/868l0aN1KPaNiLDExkVat2+T9pKQ0oV37DiQmJvLNpK/yttuyZQvffzeZHj16lXisSpUq06lz\nF775+qsC6yd99SVt2rYrMo9Bfqmpy0nbuJH6DXTtjrU+PboyZUbBz+bPv/4T1+weXYtesxf9xMrV\na+nTPWjxW7/evgAs+qngl+GFPwUTLTespwYEZUV5jk/VEhNp0apt3k/jlKa0adeRaolJTP56ewOf\nrVu2MO37b+jWs2+Jx6pUqTIdO3Xj268LNgz636TPad22Q17PqFyVK1eh2X4tqFW7DpP++wmek8MB\n/UsaSEJ2R+E8N0ppSutInr/9+vO87XLz3HUHee7QqXuBfx8A30bynFhMnpsqz2UiMTGR1q1b5/2k\npKTQvn17EhMTmTRpUt52W7Zs4bvvvqNHz54lHqtS5cp07tyZr/PtB/DVV1/Rtm3bvHuwnr16sX79\neublK0j9+OOPrF69mp6lHF923ebN2SxcvCnvZ/mKzTveqQRZ25zpczZw2EEFP2cP71+PeQs3kp4R\nFLomT/2dfetUoXP77T1o2rRMpnHDakye9mced0lZWzvhK6o2rEftg7YXpGr26EhSi6as/Tj4bpWT\nmcVv//2OhqcUHEWi4eBjWT95Jts2ljwnqIjs/dQzKjbmAXmzHrv7FWa2LzAV+AW4yt0n5N8hMkzf\n1nyrstmen5J6EhlwirsvLHSsPkDRcaiK4e6ZZjaNoMdTByB/c7EngIfd/b18wwgWlgAc4O67fiey\nFxh47NG8Pf5Dbr/3AU4/5URWrV7DqNff5NQTB5KUuP0B89mXXkmXju25buj/AdChbRt6de/KfY88\nwWUXnkuCJTBi1Ct0at+WHl23t/Q654zBXH3Drdz/6L85/OCDWPLzMl4f+w7nnHYqlYuZ2FVi44S/\nHMHYDyZwy32PcObJA1m5ei0jR7/FkBOOK5DnMy6/li4d2jHsqksB6Ni2Nb27debux/7D/51/Fglm\nPP3SaDq3a0PPLp3y9pvwxSS2ZWfTqH491qxbx5vvfURCQgJnnXJCmb/X8urEY45k7AefcPP9j3LW\nSQNZuWYtL77xFqcNOrZAjk//69/o2qEtw668NG/d5Gkz2bx1K4uWLgPIKzq1a9mcBpEHlucPPomh\nt/6Tx59/mf59evDttJlMnj6LB2+7vgzfpeSqXLkypw4+ndGjXyU5OZmUJk14Z9xb5HgOAwadmLfd\n5xM/5bFHHuTZ51+iXv36AJx2xtncdMPfefaZp+h7wEFMnfI906Z+zx3D78nb7/nnnqFChQq0adOW\npKRkli//hbfHjqFhw0YcfMihZf12y50T/nIkb73/Mbfc9zBnnjyIlWvWMHL0WIaccHyB8/nMy66m\nS8d23HDV5UDuNbsL9zz6FP93wdmYGc+Meo1O7dvQs2twza5Tqxb9+vTimZdeIzMrk+bNmrF46c+M\nHD2WQw/qS62amli5rCjP5UflylU46dSzGPvGKJKSq9M4pSnj33kDd+fYAXlfrfhy4sc89dh9PPnc\naOrWCwr/p5x+PnfcOJQXRzxO7779mT71W2ZMnczNdz6Yt19GRjpvv/ES7Tp2oUJCBebOmcH740Zz\n2VXXU726cl1WKleuwgmnns3bb4wkObk6jVKa8UEkz38ZsH0o9y8nfsTTj93H48+9kS/P53HnjUMZ\nOeIxevXtz4ypk5kx9VtuvPOhvP0yMtIZ98Yo2nXsSkJCBX6YM533x43m0qtuIFl5LjOVK1dm8JAh\njH79dZKTk2mSksK4ceNwdwYN2j6V9sTPPuORRx7h+RdeoH7kHuyMM87ghhtu4Jmnn+aAAw9kypQp\nTJ0yheHDh+ft165dO3r06MFDDz3ERRdfjJnx4gsv0KFDB7p161YkHomdNi2TaVivKvXqBvNcd+1Y\ni5o1KrFq7RYWLg6KCn85rD7Drm7DaZd8x5pfg8deo95YxuP3dGXoxS2YNHkdfXvuwwE96vD3O7Y3\n0p23cCPfTfudW65ty5MvLMHd+et5zZk17w+mztpQ9m+2nEmoVpV6xwYNZ6s2rk/F6sk0OPkYANZ+\n9CU5m7dw6PxP+H3SFGZfejMAGybPZO2ESXR94X7m33A/npND23uu4/evp/Lb59/mHXvRPf+h72cv\n0f6hm1j97mfUO/YQ6h17CN8ff3HZv1ERKVMqRsXG58A9ZvZXd/9PZF3ut+UJwF/N7HN3zzKz1sCK\nYo8SWADsb2Yt3P0ngh5MuSYAV5nZVZGh9Lq5+4xdiPch4Et3/63QRKE188V2Xgn7fgJcCTwAYGZd\n3X3mLsSwR6uenMyD/7ydx595npuH30dyUiKnnnA8551RcGLr7OxscrILzuJ563XX8u/nRvLA40/h\nOU7fXj246tILC2zTrnUr7r51GM+OepXPv5xErVo1OXvIyZw5+KSYvzfZrnpyMo/edROPjBjJsLsf\nJDkpicEDj+WC0wvOcZadnV1kkt7b/34VT77wMvc/MYIcdw7s2Y2hl5xbYJscd157ezxrfl1HUmI1\n+vXpyaVnn0Zitaoxf28SqJ6cxKN33sijI0Zxwz1BjocMPJYLTjulwHbZ2dlkF8rxQ8+8yOpftw8H\ncdsDjwNw41WXctzhwU165/ZtuOv6q3nu1Td55+PPaFi/LrddewW98xWfpWydOuR0cjyHN8eMJi1t\nIy1btWb43fdTu/b2+UFycnLIycnJGx4VoEOHjgy76TZeeXkkH37wPvUbNOAf199I9+7bW9u2atWa\n9997hwkffUhmViZ169bjwIP6ceqQM6hatVqZvs/yqHpyMo/cdQuPjniRG+/+V3DNHnQc558+uMB2\n2TlFr9m3/WMoTz7/Evc/8TQ5Oc4Bvbox9JLzC2xz0zV/ZdQbb/HW+x+z7vf11K1Th4HHHMl5Q06O\n9VuTfJTn8uXEwWeT4864N18hLe0PWrRsy63DH6ZW7e3zT+R4Djk52QUmPm/XoTN/v/EuRr/8HJ98\n+A71GjTk6n/cRpfuvfO2SUhIYOmSRXw2YTyZmVtp2qw5fxt2F70POLhM36MEeXbP4Z18eb55+CMF\n8uzuRfLctkMX/nbjcEa//CyfRvJ81T9uL5Lnn5csYmIkz02aNefaYcPppTyXuSFDhuA5OYx54w3S\n0tJo1aoVd999d8F7MPci1+4OHTty08038/JLL/HBBx/QoEEDrr/+err3KDj01w3DhjFixAgefeQR\ncnJy6N2nD5dffnmZvDfZ7pQBjTnuiO0jAlx01n4AfDhxNfc8GrSbtgSoWMHI/7hp9g8bufW+eVxy\n9v6ceFwjVq3Zwp0PzmfKjPUFjn/7Az9w1cUtuXFoGxIS4H9TfuPREYtj/r4EqtTbhx5vPF5gXe7y\n5y0PZ/OyFVjFClCh4KBbM866lvYP3UjnZ++BhATWfvAF8669u8A267+ZxvTThtLmzmtoetkZbF6a\nyoxz/s66z4pMQS/llOfsdTO8yJ9k+W/uJHrMrCHwCNAH+JWgp9LTwJvAPwl6IFnktRMJ5ov6h7sP\niOz/JDDV3Uea2V+AR4F1BPM4dXT3AWZWLbL+wMixfo6sPx/o6e5XlhLffsD77t6x0Pq8fc3shMh7\nWAFMBnq5+6GFttkX+DfBPFEVga/cvdQ7wL1xzijZORWzM8MOQcqA6fMj7m2oUi/sEKQM1Mj6LewQ\nRCRK1lYoeS5TiQ852I43kr1e9QppYYcgZeC8q5eHHYKUgRs/vnTHG8le7/ishfqAjqIL71yrB067\n6YXb6+2R/yZVjJIyp2JU/FMxqnxQMSr+qRhVPqgYJRI/VIyKfypGlQ8qRpUPKkaVDypGlQ8qRkWX\nilG7b08tRiXseBMRERERERERERERERGRXaM5o+KcmXUCXi60equ79wkjHhERERERERERERERKV9U\njIpz7j4H6Bp2HCIiIiIiIiIiIiIiUj6pGCUiIiIiIiIiIiIiIqHL0RzlcUtzRomIiIiIiIiIiIiI\niEjMqBglIiIiIiIiIiIiIiIiMaNilIiIiIiIiIiIiIiIiMSMilEiIiIiIiIiIiIiIiISMypGiYiI\niIiIiIiIiIiISMxUDDsAERERERERERERERERz/GwQ5AYUc8oERERERERERERERERiRkVo0RERERE\nRERERERERCRmVIwSERERERERERERERGRmFExSkRERERERERERERERGJGxSgRERERERERERERERGJ\nGRWjREREREREREREREREJGYqhh2AiIiIiIiIiIiIiIiIu4cdgsSIekaJiIiIiIiIiIiIiIhIzKgY\nJSIiIiIiIiIiIiIiIjGjYpSIiIiIiIiIiIiIiIjEjIpRIiIiIiIiIiIiIiIiEjMqRomIiIiIiIiI\niIiIiEjMVAw7ABERERERERERERERkZwcDzsEiRH1jBIREREREREREREREZGYUTFKRERERERERERE\nREREYkbFKBEREREREREREREREYkZFaNEREREREREREREREQkZlSMEhERERERERERERERkZhRMUpE\nRERERERERERERERipmLYAYiIiIiIiIiIiIiIiHiOhx2CxIh6RomIiIiIiIiIiIiIiEjMqBglIiIi\nIiIiIiIiIiIiMaNilIiIiIiIiIiIiIiIiMSMilEiIiIiIiIiIiIiIiLlnJnVMbNPzWxR5HftErb7\nl5nNM7P5Zva4mdmOjq1ilIiIiIiIiIiIiIiIiAwDJrp7K2BiZLkAMzsQOAjoDHQEegGH7OjAFaMb\np4iIiIiIiIiIiIiIyM5z97BDKO9OAA6N/D0K+C9wQ6FtHKgKVAYMqASs2dGB1TNKRERERERERERE\nRERE6rv7KoDI73qFN3D3b4EvgFWRnwnuPn9HB1bPKBERERERERERERERkThgZpcCl+ZbNcLdR+R7\n/TOgQTG73vwnj98SaAekRFZ9amYHu/tXpe2nYpSIiIiIiIiIiIiIiEgciBSeRpTy+pElvWZma8ys\nobuvMrOGwNpiNjsJmOzumyL7fAT0BUotRmmYPhEREREREREREREREXkPOC/y93nAu8Vs8wtwiJlV\nNLNKwCHADofpUzFKRERERERERERERERE7gOOMrNFwFGRZcysp5k9F9lmLPATMAeYBcxy9/E7OrCG\n6RMRERERERERERERkdB5Tk7YIZRr7v4bcEQx66cCF0f+zgYu29ljq2eUiIiIiIiIiIiIiIiIxIyK\nUSIiIiIiIiIiIiIiIhIzKkaJiIiIiIiIiIiIiIhIzKgYJSIiIiIiIiIiIiIiIjGjYpSIiIiIiIiI\niIiIiIjEjIpRIiIiIiIiIiIiIiIiEjMVww5AREREREREREREREQkJ8fDDkFiRD2jRERERERERERE\nREREJGZUjBIREREREREREREREZGYUTFKREREREREREREREREYkbFKBEREREREREREREREYkZFaNE\nREREREREREREREQkZiqGHYCUP41bd7KwYyhrZnapu48IOw6JHeW4fChvea4XdgAhKW95hqZhBxCK\n8pfn8qc85rhB2AGEoDzmuTwqf3muG3YAoShvef56fPOwQyhz5S3HgYVhB1DmymeeJZrcPewQJEbU\nM0qkbFwadgASc8px+aA8lw/Kc/mgPMc/5bh8UJ7LB+W5fFCe459yXD4ozyJSLBWjRERERERERERE\nREREJGZUjBIREREREREREREREZGYUTFKpGxorNz4pxyXD8pz+aA8lw/Kc/xTjssH5bl8UJ7LB+U5\n/inH5YPyLCLFMk0IJiIiIiIiIiIiIiIiYRvy959VsNhNYx7az8KOoTgVww5ARERERERERERERETE\nc1SLilcapk9ERERERERERERERERiRsUoERERERERERERERERiRkN0ycisovMrDJwIrAf+a6n7n5P\nWDGJiEjJzKwa0NTdF4Ydi4jsGjO7F7gXyAA+ALoC17r7a6EGJiIiIphZkrunhx2HiOyZ1DNKJAbM\nrL6ZPW9mH0WW25vZRWEfuw7GAAAgAElEQVTHJVE3DjiNoBCVne9H4oyZHWRmn5rZj2a2xMyWmtmS\nsOOS6DCz1mY20czmRpY7m9ktYccl0WVmA4GZwMeR5a5m9l64UUm06XwuF451943AAGAt0AG4IdyQ\nJBbM7EAzO9PMzs39CTsmiS4zq2RmQ81sbOTnKjOrFHZcEl36bC4fItfsH4D5keUuZvZUyGGJyB5G\nxSiR2BgJTAAaRZZ/BK4JLRqJlWbufoq73+Pu9+f+hB2UxMTzwMNAP6AX0DPyW+LDs8CNQBaAu88G\nTg81IomFO4DewAYAd59J0LNV4ovO5/iX2xv9OOB1d18HaJbrOGNmLwMPsv3eK/f+S+LLf4AewFOR\nn+6RdRJf9NlcPjwCHAP8BuDus4CDQ41IRPY4GqZPJDb2dfcxZnYjgLtvMzP1mIk/k82svbv/EHYg\nEnN/uPtHYQchMZPo7t+bWf5128IKRmJmm7v/USjPEn90Pse/jyKt67OBK8xsX2BryDFJ9PUE2ru7\nCo3xrZe7d8m3/LmZzQotGokVfTaXE+6+vFCe9RxMRApQMUokNtLNbB8irTTNrC/wR7ghSQz0AWaY\n2WKChyAGuLt3DzcsiYEvzOwB4G3yPfBy9+nhhSRRtM7MWrD9mn0qsCrckCQG5prZmUAFM2sFDAX+\nF3JMEn06n+Ocu18X+Uz+PdLgazNwcthxSdTNBRqg8zfeZZtZC3f/CcDMmqOH1/FIn83lw3IzOxDw\nyPzaQ4kM2SeyszxHbVHilYpRIrHxN+A9oIWZfQPUBU4NNySJgRPDDkDKTJ/I7/zDwzhweAixSPRd\nAYwA2prZCmApcHa4IUkMXAXcTFBQfp1gON3hoUYksaDzuXzYDzjUzPJ/n30tpFgkNvYFfjCz7ynY\nEGhQeCFJDFxH0OhrCUHDvmbABeGGJDGgz+by4XLgMaAxkAp8QpB7EZE8pl7vIrER+XLchuCmeqG7\nZ4UckkSJmSW5e7qZ1Sju9cik2iKylzGzJCDB3dPCjkViy8wqAEm6Xscvnc/xy8xGAu2BmWzvQeHu\n/n+hBSVRZ2aHFLfe3b8s61gktsysCtu/Ny9wdw27Gaf02Swif9apVy9RwWI3jX2s+R45Pr16RonE\ngJkVHiqktZn9Acxx97VhxCRRNRY4FphH0Dsm/wXegaZhBCXRZ2Znu/srZva34l5394fLOiaJPjO7\nGngRSAOeNbPuwDB3/yTcyCSazOw1ghab2cA0oKaZPezuD4QbmUSTzudyoS/BXEI5YQcisaOiU3wz\ns8Pd/fNivje3MDPc/e1QApOoKuk7VO6cQvouFR/M7AkiQzAWx92HlmE4IrKHUzFKJDYuAg4Avogs\nHwpMJihK3eXuL4cVmOw+dz828rtJ2LFIzCVFflcPNQqJtQvd/TEzOwaoRzA8zIsEQ0tI/Gjv7hvN\n7CzgQ+AGgqKUilHxRedz/JtHMISbGnjFITP72t37mVkaBR9u5s7NWuzIBLLXOQT4HBhYzGtOME+r\n7P30Hap8mBp2ACKy91AxSiQ2coB27r4GwMzqA/8hmHfmK0DFqDhhZoOAfgRfmia5+/shhyRR5O7P\nRH7fGXYsElO5vRuPA15091mW22RT4kklM6tEMN/fk+6eZWYa/iH+6HyOfzWB+WY2mYJzCRXuYSF7\nIXfvF/mth9hxzN1vj/x5l7svzf+ame0fQkgSA/oOVT64+6iwYxCRvYeKUSKxsV9uISpiLdDa3X83\nM80dFSfM7EmgHTA6supqMzvG3a8KMSyJATNrTjAZa1+CwuO3wLXuviTUwCRappnZJ8D+wI1mVp2g\nUYHEl2eAn4FZwFdm1gzQnFHxR+dz/Ls37ACkbJhZbaAJ+Z5buPv08CKSGHgL6F5o3VigRwixSIyY\nWVWC0WM6AFVz17v7haEFJVFnZnUJRh5oT8E8Hx5aULLXytFozHFLxSiR2JhkZu8Db0aWTyF48JUE\nbAgvLImyw4CO7u4AZvYCMDvckCRGXgP+DZwUWT4deJ2gt6Ps/S4CugJL3D3DzPYhGNpL4oi7Pw48\nnm/VMjM7LKx4JGZ0Psc5d59oZilAK3f/IvKQs0LYcUl0mdlw4HxgCdsLyg7ooWYcMLO2BIWJmoXm\njapBvofYEjdeBhYAxwB3AWcB80ONSGLhVeAN4HiCeVrPA34NNSIR2eOoGCUSG1cAJxMM3wbwPdDQ\n3dMJChgSH34EUoDlkeWGwNzwwpEYskJzvb1iZleGFo1ElbvnmNlSgnn99AAkjpnZ8RRqlUvwUETi\nhM7n+GdmFwJXEgzX1wJoCjwFHBlmXBJ1Q4AW7p4ZdiASE22AAUAtCs4blQZcEkpEEkst3X2wmZ3g\n7qPM7DVgQthBSdTt4+7Pm9nV7v4l8KWZfRl2UCKyZ1ExSiQG3N3N7CeCXhNDgKUEQxBIHDCzcQQt\nM/PPWeDAAcA3YcYm0WVmdSJ/fmFmwwiGZHTgNOCD0AKTqDKzi4GrCYrLMwmGY/wWtb6OK2b2NJBI\n0CjkOeBUgsYiEkd0PpcLQ4HewHcA7v5jZH5WiS9zCQoVa8MORKLP3d8F3jWzA9z927DjkZjLnapg\ng5l1BFYD+4UXjsRIbp5XRRqArSS4HxMRyaNilEgUmVlrguG7zgB+I+iibO6u3lDx5cmwA5AyM42g\n+GSR5cvyvebA8DKPSGLhaqAXMNndD4sMHaMJl+PPge7e2cxmu/udZvYQ8HbYQUnU6XyOf1vcPdMs\n+Gg2Mw3RF5/uBWaY2Vxga+5Kdx8UXkgSAzPM7Ao0l1C8GxGZA+5W4D0gGbgt3JAkBv5pZjWBvwNP\nEAy7eW24IYnInkbFKJHoWgBMAga6+2IAM9OHb5xx94lhxyBlw933DzsGKRNb3H2LmWFmVdx9gZm1\nCTsoibotkd8ZZtaIoNGIzvH4o/M5/n1jZtcDVSPzvl0BvB9yTBJ9o4D7gTlsnzNK4o/mEioH3P25\nyJ9fAs3DjEVix91zP4v/QNNTiEgJVIwSia5TCHpGfWFmHxMM6WWl7yJ7KzNLI+gdA8H1tAKw1d1r\nhBeVxIKZnVvcend/qaxjkZhINbNawDvAp2a2nmBYCYkv4yN5fgCYTnD9fjbckCQGdD7Hv+uBSwke\nYF9NMO/IM6FGJLGwzt0fDzsIiTnNJVQORD6XzyUYmi/vOaS7Dw0rJokeMyv1Wq08i0h+KkaJRJG7\njwPGmVkScCJBl+T6ZvYfYJy7fxJqgBJV7l49928zSwBOBrqEF5HEUK98f1cFjiB4mK1iVBxw95Mi\nf95hZl8QzAf3UYghSZRFrtET3X0D8JaZvQ9Udfc/Qg5NoqyE8/njEEOSKHP3bOA/kR+JX9PM7F6C\nIb3yD9M3PbyQJAY0l1D58CEwGfV0jFeXE8zzN4agAZAaZMtu8xzf8UayVzJ3JVcklsysDjAYOM3d\nNXl2nDOzye7eN+w4JLYiY2G/rHkL4oOZvezu5+xonezdzOxbdz8g7DgkdiJFx9nu3jHsWCT6zOx1\ndz/DzGawvWd6HnfvHkJYEiORYnJhru9T8cXMLgbeAjoBI4nMJeTuT4cZl0SXmU3XNTp+mdk+RJ55\nAdsI5k5/y93XhxqY7NVOunKRCha7adyTrfbIwrB6RonEmLv/TjB0iIYPiTNmlr8QkQD0RK2AyosM\noFXYQUjUdMi/YGYVgB4hxSKx84mZnQK87WqNFZfcPcfMZplZU3f/Jex4JOqui/w+NdQopEy4u+Yb\nKQfyzSX0FZpLKJ69bGaXEMzvl7+n4+/hhSTR4u6/AU8DT5tZY+AMYJ6Z3eDuL4cbnYjsaVSMEhHZ\ndYPz/b0N+Bk4IZxQJJbMbDzbW2EnAO0JhiGQvZiZ3QjcBFQzs41sLyZnAiNCC0xi5W9AEpBtZpsJ\n8u2a5y/uNCR4API9kJ67Uj1Z937unhr58yJ3vyn/a2Z2D8H1XOKEmd1W3Hp3v6usY5HYiDT+qe3u\n6yLLlYHzgL+5e7tQg5NoyySYs/Nmtn+nclSAjCtm1p2gEHUUwZDn08KNSET2RCpGiYjsgsiXpyma\nWLnceDDf39uAZfkeisleyt3vBe41s3vd/caw45HYyj/Pn8S1O8MOQGLuLxQtPB1fzDrZu6Xn+7sq\nMACYH1IsEmVmdjrByCHpZrYIuAN4GZgCnBViaBIbfwNa5hYeJb6Y2Z1sv0aPBm50923hRiUieyrN\nGSUisovM7L/ufmjYcUhsRQqPE9z9yLBjkdiJDLt5cGTxv+7+fpjxSPSZmRE84Nrf3YebWROgobt/\nH3JoEmVm1gxo5e6fmVkiUMHd08KOS3aPmV1GMEl6a2BhvpeqA1Pd/YxQApMyYWZVgPfc/ZiwY5Hd\nZ2ZzgRPdfXGkN8W3wOnuPi7k0CQGzOw9gvxmhB2LRJ+Z5QBLgM2RVbkPmnNHIegcSmCyV9OcUbtP\nc0aJiMSfr83sMYLWP/mHApodXkgSbe6ebWYZZlbT3f8IOx6JPjO7F+gNvBpZdbWZHaTeUnHnKSAH\nOBwYDmwC/g30CjMoia7InBSXAnWAFkBjgnkMjggzLomKMcBE4F5gWL71ae6+NpyQpAwloiG94kmm\nuy8GcPfpZrZUhai4lg3MNLMvKDhn1NDwQpIo2j/sACT+eI5qUfFKxSgRkV13SOR393zrnO29KyR+\nbAHmmNmnFCw86gtUfDge6OruOQBmNgqYAagYFV/6uHt3M5sB4O7rI/NTSHy5gqC4/B2Auy8ys3rh\nhiTR4O7rgfXAYDPrCPSLvDQJUDEqzpjZHLa3rq8A1AU0X1T8qGdmf8u3nJx/2d0fDiEmiZ13Ij8S\nh9x92Z/Zzsy+dfcDYh2PiOzZVIwSEdlF7t4/7BikzHwQ+ZH4VQv4PfJ3zTADkZjJigy76QBmVpeg\np5TEl63unhmMyghmVpHtD7QlDpjZFQRFx9wHm2PM7N/u/lSIYUn0Dcj39zZgjeYgiSvPEgyxWdKy\nxBF3H2Vm1YCm7r5whztIvKoadgAiEj4Vo0REdpKZneHur5tZsb1i3P3xso5JYsvdR4Udg8TUvcCM\nyNAhRtC7Ub2i4s/jwDiC1th3A6cCt4QbksTAl2Z2E1DNzI4C/g8YH3JMEl2XAb3dfROAmd0D/I9g\nKE6JE+6+LDKXUD+CgvLXBL2WJQ64+51/Zjszu9Hd7411PBJbZjYQeBCoDOxvZl2Bu9x9ULiRSRlT\n4yARUTFKRGQX1I78rhtqFBJzZjbG3YcUGiomjyZjjQ+R4vJ/CeYOMuAGd18dblQSLWa2v7svdfdX\nzWwawdxBRjBx+vyQw5PoGwZcBMwhKFp8CDwXakQSbQZk5VvOiqyTOGJmtwGDgbcjq0aa2Zvu/s8Q\nw5KyN5ig0ZDs3e4gGEL3vwDuPtPMNM+QiEg5pGKUiMhOyh0Gxt1vDTsWibmrI78HlLqVxIMEYB3B\nvVFrM2vt7l+FHJNEx1igh5lNdPcjgAVhByTRly+/97r7DQRDPkl8ehmYbGZvRZZPAtSDOf6cAXRz\n9y0AZnYfMB1QMap8UaE5Pmxz9z9yh9CNUC+Z8kfns4ioGCUisrPMrNQJdd39b6W9LnsPd18V+f2n\nJmWVvZOZ3Q+cBsxj+xxCDqgYFR8SzOx2giJjkeuzJkmPGw3N7BBgkJmNptADD3efHk5YEm3u/q/I\nsKr9CfJ8ubtPCTksib6fCeYX2RJZrgL8FFo0EhYVLOLDXDM7E6hgZq2AoQTDq0qciMzLOsHdjyxl\ns3PKKh7Z+7nr8h+vVIwSEdl588IOQMqGmaVR/JdgA9zda5RxSBIbJwJt3H1r2IFITJxOkOOKaHL0\neHYbwRB9KUDhAqMDh5d5RBJ1kYdd0929C6ACVBwysycIztmtwDwz+zSyfBTBvFFSvqgnRXy4CriZ\n4Lx+DZiAejnGFXfPNrMMM6vp7n+UsM3cso5LRPY8KkaJiOwkd38+7BikbLi7HlyXD0uASgRfkCXO\nuPtC4H4zm+3uH4Udj8SGu48FxprZre4+POx4JDYiD7t+MLPG7r4i7HgkJqZGfk8DxuVb/9+yD0X2\nAG+GHYDsOjOr6O7b3D2DoBh1c9gxSUxtAeZEGhGk565096HhhSQiexoVo0REdpKZPeTufzezcRTT\na8bdTw4hLIkBM6vh7hvNrE5xr7v772Udk0RPvtbXGcBMM5tIvoKUvjjFndZm9g2QBjwHdAOGufsn\n4YYlUfa5mSW5e7qZnQ10Bx7TcKtxZV9gvpl9S8GHXbr/igPurvm/yhEzqwtcAuxHvudT7n5h5Pc9\n4UQmUfI9wecwZvaEu18VcjwSWx9EfkRESqRilIjIznsj8vvJUKOQsvAaMICgda5TcKgQB5qHEZRE\nTf7W1++FGYiUiQvd/TEzOwaoC1wAvAioGBVf/gN0MbMuwPXA88BLwCGhRiXRdF/YAUjsmNkcSp4n\nyCNDNEr8eBeYBHwGZIcci0Rf/u9OB4UWhZQJdx9lZtWAppGRCUREilAxSkRkJ7n795HfE8OORWLL\n3QdEfu8fdiwSfflbX5tZZaAtwQOwhe6eGVpgEiu5D0SOA15091lmprko4s82d3czO4GgR9TzZnZe\n2EFJdJjZAKAlMEf3YXFrQDHrjGA+uJvKOBaJvUR3vyHsICRmSiosSxwys4HAg0BlYH8z6wrc5e6D\nwo1MRPYkCWEHICKytzGzFmb2rJn9y8wamdl4M9tgZtPMrEfY8Ul0mVnF3AfWZtbEzE6N3FhLnDCz\n44CfgMcJejwuNrNjw41KYmCamX1CUIyaYGbVgZyQY5LoSzOzG4GzgQ/MrALBnHCyl4sMrToMaAz8\ny8xUmIhD7r4s9weoDVxBMF/UcODDMGOTmHg/ch8m8amtmc2O9HjM/Xu2mc0xs9lhBydRdwfQG9gA\n4O4zATXqFJEC1DNKRGTnjQReB2oA3xEMA3QG0B/4N9A3tMgkqszsEuB+YJOZDQeuA6YD3czsBXe/\nP9QAJVoeBg5z98UQFJwJxjv/KNSoJNouAroCS9w9w8z2IRiqT+LLacCZwEXuvtrMmgIPhByTRMdh\nQFd332ZmScCXgOaTiTNm1ho4neDe+jeC4bHN3Q8LNTCJlauBm8xsK5BF0AvO3b1GuGFJlLQLOwAp\nU9vc/Y9CAw+od5zskpwctRmMVypGiYjsvOru/hQExQp3fz2y/iMzuzfEuCT6rgFaANWB+UAzd19n\nZonAFIJClez91uYWoiKWAGvDCkZiw91zzGwN0N7MdA8cp9x9NUGBOXf5F4I5o2Tvl+nu2wDcPd3M\nNMpHfFpAMIfQwHyNRK4NNySJFXevHnYMEjuRHo47ZGbfuvsBsY5HYm6umZ0JVDCzVsBQ4H8hxyQi\nexh9ERcR2Xn5m2j8UcprsvfLdPf1wHozW+zu6wAivSo0p1D8mGdmHwJjCFrvDQammNnJAO7+dpjB\nSXSY2f0EvWZ+YPsk6Q58FVpQEnWR8/Z+oB5BC3u1so8fbc1seuRvA9pElnNz3D280CSKTiHoGfWF\nmX0MjGb7nH8SJ8ysrbsvMLNiz1t3n17ceolbVcMOQKLiKuBmYCvBSDITCIZYFRHJo2KUiMjOa5vv\n4UebQg9GWocXlsRANTPrRjDHYuXI37kPN/WlKX5UBdYAh0SWfwXqAAMJihUqRsWHE/n/9u482pKy\nPNv4ddMCzaxoqyviB4ISBRUZYhTQRCKaRBQUUUFwjME4EMEhBI0yGP2cY/giDhBtcR6AQDSKA7YQ\njQQQ0yg4IcREDCoKBKSR7uf7o+rYu6G7oZtd5z1n7+u31l61663da91rFdTZu563nhd+t6qWtQ6i\nQb2Z7omKS1sH0dg9pHUADa+qTgdO71sxHgAcCdwryUnA6VV1dtOAGpeXAy8A3raaYwXsM7tx1Jit\n3CZAVd1IV4x6desskuYui1GStO68GTI9rmJlu6dVWj/1+5oAVeW6QdPhcmBDutmamlz/YyFqMlXV\nD+/I55KcV1V7D51Hw6qqG4APAx9OsjXdU8tHAxajJkBVvaDfuhaYNM8lOYu1FBSr6kmzGEfSHGcx\nSpLWkTdDpscd/YGcZN+q+sLQeTSMJG8GXg/8GvgcsAvwsqr6UNNgGrcbgYuTfImRglRVHdEukgZw\nQZKPA2ew6nn2CcfpsVnrABqvqroGeE//0gSYaYW8Jl6zp46tOOe3t/bbpwD3BmZ+Qx0MXNEikKS5\ny2KUJA3HmyHT402Axaj563FV9aokTwb+i2729Tms/CGlyXBm/9Jk25Ku8Pi4kTHbbU4X2z1Jc98T\n13LMa/YESbIA+HxVPXYtHztstvJo/KpqCUCSE6rq0SOHzkri2qxaL7XCr3OTymKUJA3Hv57Tw9l8\n89uG/fZPgY9W1TWJp3TSVNXi1hk0PNtuStLc57V6elTV8iQ3Jtmqqq5dw2cume1cGsSiJNtX1eUA\nSe4HLGqcSdIcYzFKkqQ7z8Lj/HZWksvo2vS9KMki4KbGmTRmSR4AvBHYCVg4M15V2zcLpbFLshB4\nPrAzq57n5zULpdnmbAJpnkjy2tWNV9Xxs51Fg7oJWJrkC8ANM4O2Sp44RwJfSXJ5v78dcHi7OJLm\nIotRkjQcb4ZI80BVHZ3kTcB1/ezNG4D9W+fS2L0feB3wDuAxwHPxOj2JTgUuAx4PHA88E7i0aSLN\ntue0DiDpDrth5P1CYD+8Zk+iz/QvTbCq+lw/+euB/dBlVbVsbf9G0vSxGCVJw3lO6wAajyQb3/qL\n9K3Grpj9VBqXJBvS9ap/dN+ebwnw7qahNIRNqupLSVJVVwLHJjmXrkClyXH/qjooyf5VtTjJR4DP\ntw6lOy/JL1n9k8gBqqq2pnvzrVkNJmm9VdXbRveTvBXXd5w4/d/jTYD/U1XfbZ1Hg9qd7omouwC7\nJKGqPtg2kqS5xGKUJK0jb4ZMpa8Du61prKqeMuuJNE4n0a0b9a5+/7B+7M+aJdIQbkqyAfD9JC8B\n/hu4Z+NMGr/f9NtfJXkw8FO6myKa/+7ROoCkwW0K2D53wiR5IvBWYCPgfkkeBhxfVU9qm0zjlORU\nYAfgYmB5P1yAxShJv2UxSpLWnTdDpkSSewP3ATZJsisrW3ptSfdjWZPh96pql5H9LyexmDx5Xkb3\n/+0RwAl0rfqe3TSRhvDeJHcDXkM3u35z4G/aRtI4VNXy0f0kWzOyLhjwk9lNJOnOSrKUlZP8FgCL\n6FqsarIcCzwc+ApAVV2c5H4tA2kQewA7VZXrKetOq1rROoIGYjFKktaRN0OmyuPp2i1uA7yNlcWo\n64BjGmXS+C1PskNV/RAgyfasnM2nCZBkAfC0qnol8L9060VpwvRPvl1XVb8Evoqz6ydSkifQrf22\nDfALukkj32PlGhWS5o/9Rt7fAvxPVd3SKowGc0tVXdu3w55hwWLyXALcG7iqdRBJc5fFKElaT94M\nmXxVtRhYnOTAqvp06zwazCuBc5JcTldw3BaLFROlqpYn2b1fL8qbHxOqqlb0LRg/0TqLBvW3wF7A\n2VW1a5J9gQMbZ5K0HqrqyiS7AXvTFSfOA77ZNpUGcEmSQ4AFSR5A95T61xpn0vjdA/hOkvOB3663\nbDtGSaMsRknS+vNmyPTYPcmXqupXAH0LqJdX1Wsa59IYVNWX+h/Gv0tXjLqsqpbdzj/T/PNN4J+S\nfBK4YWawqk5rF0kD+EKSVwAfZ9XzfE27SBqzW6rqZ0k26AvMX0jyt61DSVp3SV4LHATM/C3+QJJP\nVtXrG8bS+L0UeDVdgeKjwOfpWiZrshzbOoCkuS9ODpWk9ZPkgqrao19b5mFVVUnOr6qHt86m8Ury\nzara9VZjF1XVbq0yabyS7Alsx8hEnapysd0JkuT9qxmuqnrerIfRYJL8aDXDVVW27JsQSb4EPAl4\nM90ajlcDe1XVI5oGk7TOklwK7FpVN/X7mwAXVdWD2iaTtD6SbAs8oKq+mGRTYEFVXd86l+afJ/zZ\nJRYs7qTPnPzg3P6nZp9PRknS+rs2yWZ07SQ+mORqwFUWJ9OCJBvPPC3T/1DeuHEmjUmSU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nE4CfANs0zKM2bAUhSdIs67977cyqrZKPb5dIkiQNxWKUJEmadq9PshXwcrq2XlsCR7aNJEmSNNmS\nvBvYFHgMcDLwVOD8pqEkSdJgLEZJkqRp97Wquha4lu5mCEnu1zaSGpiTC7xq7DzPkjR37FlVD03y\nH1V1XJK3Aae1DiVJaqtqResIGsgGrQNIkiQ1dlaSLWd2kjwIOKthHg0kybZJHtu/3yTJFiOHD2sU\nSwNIstkaDr1zVoNIktbm1/32xiS/Q9c62QlBkiRNKItRkiRp2r2BriC1eZLdgU8BhzbOpDFL8gK6\nc/uefmgb4IyZ41V1SYtcGq8keyb5DnBpv79LknfNHK+qD7TKJkm6jX9OclfgLcBFwBXAR5smkiRJ\ng7FNnyRJmmpV9ZkkGwJnA1sAB1TV9xvH0vi9GHg48A2Aqvp+knu2jaQBvAN4PHAmQFV9K8mj20aS\nJK1OVZ3Qv/10kn8GFvatkwFIsm9VfaFNOkmSNG4WoyRJ0lRKciJQI0NbApcDL01CVR3RJpkGsqyq\nbk66JYOS3IVVz78mRFX9eOY895a3yiJJumOqahmw7FbDbwIsRkmSNCEsRkmSpGl1wa32L2ySQrNl\nSZJjgE2S7Au8CNcGm0Q/TrInUEk2Ao6gb9knSZp3cvsfkSRJ84XFKEmSNJWqanHrDJpVRwPPB5YC\nhwOfBU5umkhDeCHwTuA+wH/Rtd98UdNEkqT15RPMkjSFVqzw8j+pLEZJkqSplGQpa77JUVW1y2zm\n0bCqagXwvv6lyfW7VfXM0YEkewH/2iiPJEmSJAmLUZIkaXrtt5qxANsAx8xyFg2sL0gcC2xL9x04\ndEXH7Vvm0tidCOx2B8YkSXPfFa0DSJKk8bEYJUmSplJVXTnzPsnDgEOApwE/Aj7dKpcGcwpwJN3a\nYMsbZ9GYJXkksCewKMlRI4e2BBa0SSVJuj39On/bMXJ/qqo+2G+f0iiWJEkagMUoSZI0lZLsCDwD\nOBj4BfBxIFX1mKbBNJRrq+pfWofQYDYCNqf7fbPFyPh1wFObJJIkrVWSU4EdgItZOVGkgA82CyVJ\nkgZjMUqSJE2ry4BzgSdW1Q8AkhzZNpIGdE6StwCnActmBqvqonaRNC5VtQRYkuQDo089SpLmtD2A\nnarKleolSZoCFqMkSdK0OpDuyahzknwO+BjdOkKaTL/fb/cYGStgnwZZNJwb+6LjzsDCmcGq8jxL\n0txzCXBv4KrWQSRJ0vAsRkmSpKlUVacDpyfZDDiAbj2heyU5CTi9qs5uGlBjZfvFqfFhupab+wEv\nBJ4N/KxpIknSmtwD+E6S81n1qeUntYskSWqtVqxoHUEDsRglSZKmWlXdQHcD+8NJtgYOAo4GLEZN\ngCSHVtWHkhy1uuNV9fbZzqRB3b2qTknylyOt+5a0DiVJWq1jWweQJEmzx2KUJElSr6quAd7TvzQZ\nNuu3WzRNodnym357VZInAD8BtmmYR5K0Bv2kAUmSNCUsRkmSJGliVdV7+u1xrbNoVrw+yVbAy4ET\ngS2Bl7WNJEkaleS8qto7yfV06zf+9hBQVbVlo2iSJGlAFqMkSZI08ZLcD3gpsB0j34Fdl2Li/LKq\nrgWuBR4DkGSvtpEkSaOqau9+61PLkiRNEYtRkiRJmgZnAKcAZwGuiDu5TgR2uwNjkqQ5IMndgPuy\n6kSRi9olkiRJQ7EYJUmSpGlwU1X9fesQGkaSRwJ7AouSHDVyaEtgQZtUkqS1SXIC8BzgclZOFClg\nn1aZJEnt1Yq6/Q9pXrIYJUmSpGnwziSvA84Gls0MOvt6YmwEbE73+2a07dN1wFObJJIk3Z6nATtU\n1c2tg0iSpOFZjJIkSdI0eAhwGN1sa2dfT5iqWpLkPOAhVXVc6zySpDvkEuCuwNWtg0iSpOFZjJIk\nSdI0eDKwvbOvJ1dVLU+ydesckqQ77I3AN5NcwqpPLT+pXSRJkjQUi1GSJEmaBt/C2dfT4JtJzgQ+\nCdwwM1hVp7WLJElag8XAm4ClrHxqWZIkTSiLUZIkSZoG9wIuS/LvOPt6km0N/IJV2y8WYDFKkuae\nn1fV37cOIUmSZofFKEmSJE2D17UOoOFV1XNbZ5Ak3WEXJnkjcCarThS5qF0kSVJrVT4sO6ksRkmS\nJGniVdWS1hk0vCQLgecDOwMLZ8ar6nnNQkmS1mTXfvuIkbFi1adbJUnShLAYJUmSpImX5BHAicCD\ngI2ABcANVbVl02Aat1OBy4DHA8cDzwQubZpIkrRaVfWY1hkkSdLssRglSZKkafD/gGcAnwT2AJ4F\nPKBpIg3h/lV1UJL9q2pxko8An28dSpJ0W0leu7rxqjp+trNIkqThbdA6gCRJkjQbquoHwIKqWl5V\n7wf+sHEkjd9v+u2vkjwY2ArYrl0cSdJa3DDyWg78CV6zJUmaWD4ZJUmSpGlwY5KNgIuTvBm4Ctis\ncSaN33uT3A34G+BMYPP+vSRpjqmqt43uJ3kr3bVbkiRNIItRkiRJmgaH0a0T9RLgSOC+wIFNE2ns\nqurk/u0SYPuWWSRJ62xTvHZLkjSxLEZJkiRp4lXVlf3bXwPHtcyi4SS5O3AssBdQwLnACVX1i5a5\nJEm3lWQp3bUaugkjiwDXi5KkKVcr6vY/pHnJYpQkSZIm1q1udN1aVdUus5lHg/sY8FVWPvX2TODj\nwGObJZIkrcl+I+9vAf6nqm5pFUaSJA3LYpQkSZIm2X6rGQuwDXDMLGfR8LauqhNG9l+f5IBmaSRJ\na1RVVybZDdibbuLIecA326aSJElD2aB1AEmSJGkoVXXlzAu4G/Bi4CvACcBnW2bTIM5J8owkG/Sv\npwGfaR1KknRbSV4LLAbuDtwD+ECS17RNJUmShpIqezBKkiRpMiXZEXgGcDDwC7qWba+oqm2bBtMg\nklwPbAYsp3sCbgPghv5wVdWWrbJJklaV5FJg16q6qd/fBLioqh7UNpkkqaVH7X+uBYs76dx/elRa\nZ1gd2/RJkiRpkl0GnAs8sap+AJDkyLaRNJSq2qJ1BknSHXYFsBC4qd/fGPhhszSSJGlQFqMkSZI0\nyQ6kezLqnCSfAz5G98SMJlCSvYCLq+qGJIcCuwF/V1X/2TiaJKmX5ES6NaKWAd9O8oV+f1+6daMk\nSVOsVqxoHUEDsRglSZKkiVVVpwOnJ9kMOAA4ErhXkpOA06vq7KYBNW4nAbsk2QV4FXAKcCrwB01T\nSZJGXdBvLwROHxn/yuxHkSRJs8VilCRJkiZeVd0AfBj4cJKtgYOAowGLUZPllqqqJPsD76yqU5I8\nu3UoSdJKVbW4dQZJkjT7LEZJkiRpqlTVNcB7+pcmy/VJ/ho4DHhUkgX4m0eS5pQkS+na8q1OVdUu\ns5lHkiTNDn+YSZIkSZoUTwcOAZ5bVT9N8mhgs8aZJEmr2m81YwG2AY6Z5SySJGmWWIySJEmSNBH6\nAtSXgUOSfAj4EfB3jWNJkkZU1ZUz75M8jG4SwdPortmfbpVLkiQNK1VrejJakiRJkua+JDsCzwAO\nBn4BfBx4RVVt2zSYJOk2vGZLkjSdLEZJkiRJmteSrADOBZ5fVT/oxy6vqu3bJpMk3ZrXbEmSptMG\nrQNIkiRJ0p10IPBT4Jwk70vyR3Trj0iS5h6v2ZIkTSGfjJIkSZI0EZJsBhxA1/ppH2AxcHpVnd00\nmCTpNrxmS5I0XSxGSZIkSZo4SbYGDgKeXlX7tM4jSVozr9mSJE0+i1GSJEmSJEmSJEkajGtGSZIk\nSZIkSZIkaTAWoyRJkiRJkiRJkjQYi1GSJEmSJEmSJEkajMUoSZIkSZIkSZIkDcZilCRJkiRJkiRJ\nkgbz/wHu2OvtJHbUjAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7930223e8278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 30))\n",
    "sns.heatmap(liver_corr, cbar = True,  square = True, annot=True, fmt= '.2f',annot_kws={'size': 15},\n",
    "           cmap= 'coolwarm')\n",
    "plt.title('Correlation between features');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_cell_guid": "e5ddc363-f1eb-4225-979b-64d86c2aa578",
    "_uuid": "7879565f25f9f5c349c8e39c506f57243d597636",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:42.191626Z",
     "iopub.status.busy": "2023-05-27T13:28:42.191128Z",
     "iopub.status.idle": "2023-05-27T13:28:42.196614Z",
     "shell.execute_reply": "2023-05-27T13:28:42.195659Z",
     "shell.execute_reply.started": "2023-05-27T13:28:42.191575Z"
    }
   },
   "outputs": [],
   "source": [
    "#The above correlation also indicates the following correlation\n",
    "# Total_Protiens & Albumin\n",
    "# Alamine_Aminotransferase & Aspartate_Aminotransferase\n",
    "# Direct_Bilirubin & Total_Bilirubin\n",
    "# There is some correlation between Albumin_and_Globulin_Ratio and Albumin. But its not as high as Total_Protiens & Albumin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d452f2ac-60ed-4330-98a6-70db275af530",
    "_uuid": "458d0033ebbc367888af6082573eef6b7e27e2c9"
   },
   "source": [
    "# Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_cell_guid": "10d22778-b70f-4190-a277-82b4d020f281",
    "_uuid": "6bc5635a45d3950a0ea94cf088c8571ec8029cbb",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:42.198266Z",
     "iopub.status.busy": "2023-05-27T13:28:42.197998Z",
     "iopub.status.idle": "2023-05-27T13:28:42.446014Z",
     "shell.execute_reply": "2023-05-27T13:28:42.445050Z",
     "shell.execute_reply.started": "2023-05-27T13:28:42.198220Z"
    }
   },
   "outputs": [],
   "source": [
    "# Importing modules\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, BaggingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_cell_guid": "ade84f4a-7907-4ff1-a20d-7493ac06c16b",
    "_uuid": "bc317a65e95970b06de98f20e046e1845cb72f75",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:42.447682Z",
     "iopub.status.busy": "2023-05-27T13:28:42.447413Z",
     "iopub.status.idle": "2023-05-27T13:28:42.460351Z",
     "shell.execute_reply": "2023-05-27T13:28:42.459542Z",
     "shell.execute_reply.started": "2023-05-27T13:28:42.447643Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(408, 11)\n",
      "(408,)\n",
      "(175, 11)\n",
      "(175,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=101)\n",
    "print (X_train.shape)\n",
    "print (y_train.shape)\n",
    "print (X_test.shape)\n",
    "print (y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_cell_guid": "6a33cbbf-4c26-4509-aca6-7d8160a68fa7",
    "_uuid": "79462fc8649ec0e0916e874fc6d8766ef6f3b31a",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:42.461808Z",
     "iopub.status.busy": "2023-05-27T13:28:42.461453Z",
     "iopub.status.idle": "2023-05-27T13:28:42.690381Z",
     "shell.execute_reply": "2023-05-27T13:28:42.689360Z",
     "shell.execute_reply.started": "2023-05-27T13:28:42.461759Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Training Score: \n",
      " 73.53\n",
      "Logistic Regression Test Score: \n",
      " 66.29\n",
      "Coefficient: \n",
      " [[-0.01330864 -0.02731202 -0.47915889 -0.00100509 -0.0105125  -0.00311187\n",
      "  -0.20786575  0.21451407  0.66623491  0.4855822   0.24378845]]\n",
      "Intercept: \n",
      " [ 0.72937065]\n",
      "Accuracy: \n",
      " 0.662857142857\n",
      "Confusion Matrix: \n",
      " [[104  20]\n",
      " [ 39  12]]\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          1       0.73      0.84      0.78       124\n",
      "          2       0.38      0.24      0.29        51\n",
      "\n",
      "avg / total       0.62      0.66      0.64       175\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x793013769438>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A66//LXc5Da8wHTBwM/CulNLjb98REXd3S0UF0Wfnfky84GSiVy969Qr+99cPsPiuxzj6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x793013766eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2) Logistic Regression\n",
    "# Create logistic regression object\n",
    "logreg = LogisticRegression()\n",
    "# Train the model using the training sets and check score\n",
    "logreg.fit(X_train, y_train)\n",
    "#Predict Output\n",
    "log_predicted= logreg.predict(X_test)\n",
    "\n",
    "logreg_score = round(logreg.score(X_train, y_train) * 100, 2)\n",
    "logreg_score_test = round(logreg.score(X_test, y_test) * 100, 2)\n",
    "#Equation coefficient and Intercept\n",
    "print('Logistic Regression Training Score: \\n', logreg_score)\n",
    "print('Logistic Regression Test Score: \\n', logreg_score_test)\n",
    "print('Coefficient: \\n', logreg.coef_)\n",
    "print('Intercept: \\n', logreg.intercept_)\n",
    "print('Accuracy: \\n', accuracy_score(y_test,log_predicted))\n",
    "print('Confusion Matrix: \\n', confusion_matrix(y_test,log_predicted))\n",
    "print('Classification Report: \\n', classification_report(y_test,log_predicted))\n",
    "\n",
    "sns.heatmap(confusion_matrix(y_test,log_predicted),annot=True,fmt=\"d\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_cell_guid": "89140e45-c762-4514-9474-821c9d85447f",
    "_uuid": "12a4e1da350653eb0e1c0307cd1d6d17015df422",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:42.692118Z",
     "iopub.status.busy": "2023-05-27T13:28:42.691836Z",
     "iopub.status.idle": "2023-05-27T13:28:42.711260Z",
     "shell.execute_reply": "2023-05-27T13:28:42.710430Z",
     "shell.execute_reply.started": "2023-05-27T13:28:42.692060Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Albumin_and_Globulin_Ratio</td>\n",
       "      <td>0.666235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gender_Female</td>\n",
       "      <td>0.485582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Gender_Male</td>\n",
       "      <td>0.243788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Albumin</td>\n",
       "      <td>0.214514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alkaline_Phosphotase</td>\n",
       "      <td>-0.001005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Aspartate_Aminotransferase</td>\n",
       "      <td>-0.003112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alamine_Aminotransferase</td>\n",
       "      <td>-0.010512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.013309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total_Bilirubin</td>\n",
       "      <td>-0.027312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Total_Protiens</td>\n",
       "      <td>-0.207866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Direct_Bilirubin</td>\n",
       "      <td>-0.479159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Feature  Correlation\n",
       "8   Albumin_and_Globulin_Ratio     0.666235\n",
       "9                Gender_Female     0.485582\n",
       "10                 Gender_Male     0.243788\n",
       "7                      Albumin     0.214514\n",
       "3         Alkaline_Phosphotase    -0.001005\n",
       "5   Aspartate_Aminotransferase    -0.003112\n",
       "4     Alamine_Aminotransferase    -0.010512\n",
       "0                          Age    -0.013309\n",
       "1              Total_Bilirubin    -0.027312\n",
       "6               Total_Protiens    -0.207866\n",
       "2             Direct_Bilirubin    -0.479159"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(X.columns)\n",
    "coeff_df.columns = ['Feature']\n",
    "coeff_df[\"Correlation\"] = pd.Series(logreg.coef_[0])\n",
    "pd.Series(logreg.coef_[0])\n",
    "\n",
    "coeff_df.sort_values(by='Correlation', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_cell_guid": "133bdeb1-d6d9-4eed-8c95-88a4fa237d84",
    "_uuid": "da565cb49c73098981463b6dd5e941cddd2cab9b",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:42.712877Z",
     "iopub.status.busy": "2023-05-27T13:28:42.712562Z",
     "iopub.status.idle": "2023-05-27T13:28:42.911731Z",
     "shell.execute_reply": "2023-05-27T13:28:42.910867Z",
     "shell.execute_reply.started": "2023-05-27T13:28:42.712818Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Score: \n",
      " 56.13\n",
      "Gaussian Test Score: \n",
      " 53.14\n",
      "Accuracy: \n",
      " 0.531428571429\n",
      "[[44 80]\n",
      " [ 2 49]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.96      0.35      0.52       124\n",
      "          2       0.38      0.96      0.54        51\n",
      "\n",
      "avg / total       0.79      0.53      0.53       175\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7930137668d0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jYkVELOm17ysR8XxELKptR9Ubx6CVVCpNfmbYlcARG9l/UUppWm27rd4gTh1IKpVmTh2k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\nYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7930137664e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gaussian Naive Bayes\n",
    "\n",
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X_train, y_train)\n",
    "#Predict Output\n",
    "gauss_predicted = gaussian.predict(X_test)\n",
    "\n",
    "gauss_score = round(gaussian.score(X_train, y_train) * 100, 2)\n",
    "gauss_score_test = round(gaussian.score(X_test, y_test) * 100, 2)\n",
    "print('Gaussian Score: \\n', gauss_score)\n",
    "print('Gaussian Test Score: \\n', gauss_score_test)\n",
    "print('Accuracy: \\n', accuracy_score(y_test, gauss_predicted))\n",
    "print(confusion_matrix(y_test,gauss_predicted))\n",
    "print(classification_report(y_test,gauss_predicted))\n",
    "\n",
    "sns.heatmap(confusion_matrix(y_test,gauss_predicted),annot=True,fmt=\"d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_cell_guid": "3ebf85d5-2867-49a8-821d-4566f7d066a0",
    "_uuid": "1613eb76e677ff43f9253c41775f0064a6162b1d",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:42.913412Z",
     "iopub.status.busy": "2023-05-27T13:28:42.913101Z",
     "iopub.status.idle": "2023-05-27T13:28:43.296681Z",
     "shell.execute_reply": "2023-05-27T13:28:43.294731Z",
     "shell.execute_reply.started": "2023-05-27T13:28:42.913352Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Score: \n",
      " 100.0\n",
      "Random Forest Test Score: \n",
      " 66.86\n",
      "Accuracy: \n",
      " 0.668571428571\n",
      "[[99 25]\n",
      " [33 18]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.75      0.80      0.77       124\n",
      "          2       0.42      0.35      0.38        51\n",
      "\n",
      "avg / total       0.65      0.67      0.66       175\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "\n",
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, y_train)\n",
    "#Predict Output\n",
    "rf_predicted = random_forest.predict(X_test)\n",
    "\n",
    "random_forest_score = round(random_forest.score(X_train, y_train) * 100, 2)\n",
    "random_forest_score_test = round(random_forest.score(X_test, y_test) * 100, 2)\n",
    "print('Random Forest Score: \\n', random_forest_score)\n",
    "print('Random Forest Test Score: \\n', random_forest_score_test)\n",
    "print('Accuracy: \\n', accuracy_score(y_test,rf_predicted))\n",
    "print(confusion_matrix(y_test,rf_predicted))\n",
    "print(classification_report(y_test,rf_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_cell_guid": "43ad5917-e40f-4e2a-bae5-182d2baadb6c",
    "_uuid": "2d8812b1ccb4a56cfde468f223a6539974b54394",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:43.298346Z",
     "iopub.status.busy": "2023-05-27T13:28:43.298043Z",
     "iopub.status.idle": "2023-05-27T13:28:43.315493Z",
     "shell.execute_reply": "2023-05-27T13:28:43.314517Z",
     "shell.execute_reply.started": "2023-05-27T13:28:43.298287Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Score</th>\n",
       "      <th>Test Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>100.00</td>\n",
       "      <td>66.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>73.53</td>\n",
       "      <td>66.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gaussian Naive Bayes</td>\n",
       "      <td>56.13</td>\n",
       "      <td>53.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Model   Score  Test Score\n",
       "2         Random Forest  100.00       66.86\n",
       "0   Logistic Regression   73.53       66.29\n",
       "1  Gaussian Naive Bayes   56.13       53.14"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###Model evaluation\n",
    "#We can now rank our evaluation of all the models to choose the best one for our problem. \n",
    "models = pd.DataFrame({\n",
    "    'Model': [ 'Logistic Regression', 'Gaussian Naive Bayes','Random Forest'],\n",
    "    'Score': [ logreg_score, gauss_score, random_forest_score],\n",
    "    'Test Score': [ logreg_score_test, gauss_score_test, random_forest_score_test]})\n",
    "models.sort_values(by='Test Score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_uuid": "65f3a976213dfb21de9375c8bd75e75b59463aec",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:43.317052Z",
     "iopub.status.busy": "2023-05-27T13:28:43.316769Z",
     "iopub.status.idle": "2023-05-27T13:28:43.379083Z",
     "shell.execute_reply": "2023-05-27T13:28:43.378109Z",
     "shell.execute_reply.started": "2023-05-27T13:28:43.317005Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression Score: \n",
      " 13.12\n",
      "Linear Regression Test Score: \n",
      " 8.07\n",
      "Coefficient: \n",
      " [ -2.53338525e-03  -4.02775624e-04  -2.29658815e-02  -2.17508414e-04\n",
      "  -3.93047877e-04   6.12504741e-05  -5.55814496e-02   8.31023933e-02\n",
      "   6.22099710e-02   4.61371689e-02  -4.61371689e-02]\n",
      "Intercept: \n",
      " 1.59425288848\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RFE(estimator=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False),\n",
       "  n_features_to_select=3, step=1, verbose=0)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear = linear_model.LinearRegression()\n",
    "# Train the model using the training sets and check score\n",
    "linear.fit(X_train, y_train)\n",
    "#Predict Output\n",
    "lin_predicted = linear.predict(X_test)\n",
    "\n",
    "linear_score = round(linear.score(X_train, y_train) * 100, 2)\n",
    "linear_score_test = round(linear.score(X_test, y_test) * 100, 2)\n",
    "#Equation coefficient and Intercept\n",
    "print('Linear Regression Score: \\n', linear_score)\n",
    "print('Linear Regression Test Score: \\n', linear_score_test)\n",
    "print('Coefficient: \\n', linear.coef_)\n",
    "print('Intercept: \\n', linear.intercept_)\n",
    "\n",
    "from sklearn.feature_selection import RFE\n",
    "rfe =RFE(linear, n_features_to_select=3)\n",
    "rfe.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_uuid": "baeb67c97907ccb86bfba6cfa3478b5b57e8aa36",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:43.380600Z",
     "iopub.status.busy": "2023-05-27T13:28:43.380325Z",
     "iopub.status.idle": "2023-05-27T13:28:43.388371Z",
     "shell.execute_reply": "2023-05-27T13:28:43.387532Z",
     "shell.execute_reply.started": "2023-05-27T13:28:43.380545Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total_Protiens\n",
      "Albumin\n",
      "Gender_Male\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(rfe.ranking_)):\n",
    "    if rfe.ranking_[i] == 1:\n",
    "        print(X.columns.values[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_cell_guid": "02e32ee7-5d28-40c4-b3fd-6a945d9a4830",
    "_uuid": "bc5d9679e8ac9ef04048e24eb735e189cfd1aa50",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:43.390130Z",
     "iopub.status.busy": "2023-05-27T13:28:43.389827Z",
     "iopub.status.idle": "2023-05-27T13:28:43.409211Z",
     "shell.execute_reply": "2023-05-27T13:28:43.408326Z",
     "shell.execute_reply.started": "2023-05-27T13:28:43.390065Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Gender_Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Protiens  Albumin  Gender_Male\n",
       "0             6.8      3.3            0\n",
       "1             7.5      3.2            1\n",
       "2             7.0      3.3            1\n",
       "3             6.8      3.4            1"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#I'm considering seven important features based on recursive feature elimination\n",
    "#finX = liver_df[['Age','Direct_Bilirubin','Total_Protiens','Albumin', 'Gender_Female', 'Gender_Male']]\n",
    "finX = liver_df[['Total_Protiens','Albumin', 'Gender_Male']]\n",
    "finX.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_cell_guid": "7f8cb246-bf58-4f0a-914b-f4d72e39638c",
    "_uuid": "868e712ae862044058d4390fb26a6d91db86edc2",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:43.411036Z",
     "iopub.status.busy": "2023-05-27T13:28:43.410657Z",
     "iopub.status.idle": "2023-05-27T13:28:43.424871Z",
     "shell.execute_reply": "2023-05-27T13:28:43.424005Z",
     "shell.execute_reply.started": "2023-05-27T13:28:43.410972Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(finX, y, test_size=0.30, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_uuid": "8f6d358db23a877239ad211bb57b2f952d5c4149",
    "execution": {
     "iopub.execute_input": "2023-05-27T13:28:43.426429Z",
     "iopub.status.busy": "2023-05-27T13:28:43.426154Z",
     "iopub.status.idle": "2023-05-27T13:28:43.645201Z",
     "shell.execute_reply": "2023-05-27T13:28:43.644304Z",
     "shell.execute_reply.started": "2023-05-27T13:28:43.426383Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Training Score: \n",
      " 71.08\n",
      "Logistic Regression Test Score: \n",
      " 71.43\n",
      "Coefficient: \n",
      " [[-0.59570175  1.09488855 -0.54783114]]\n",
      "Intercept: \n",
      " [-0.1301474]\n",
      "Accuracy: \n",
      " 0.714285714286\n",
      "Confusion Matrix: \n",
      " [[120   4]\n",
      " [ 46   5]]\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          1       0.72      0.97      0.83       124\n",
      "          2       0.56      0.10      0.17        51\n",
      "\n",
      "avg / total       0.67      0.71      0.63       175\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7930211d39b0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FwC9vvJvtd51YOFKVtnr1GgCefPJpliy5kb333rNwRK2vlSrgfhfhIuLeDX0E+P/PG3DH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\nS1IhJmBJKsQELEmF/D9NDxuOxu76kgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7930211d3358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Logistic Regression\n",
    "logreg = LogisticRegression()\n",
    "# Train the model using the training sets and check score\n",
    "logreg.fit(X_train, y_train)\n",
    "#Predict Output\n",
    "log_predicted= logreg.predict(X_test)\n",
    "\n",
    "logreg_score = round(logreg.score(X_train, y_train) * 100, 2)\n",
    "logreg_score_test = round(logreg.score(X_test, y_test) * 100, 2)\n",
    "#Equation coefficient and Intercept\n",
    "print('Logistic Regression Training Score: \\n', logreg_score)\n",
    "print('Logistic Regression Test Score: \\n', logreg_score_test)\n",
    "print('Coefficient: \\n', logreg.coef_)\n",
    "print('Intercept: \\n', logreg.intercept_)\n",
    "print('Accuracy: \\n', accuracy_score(y_test,log_predicted))\n",
    "print('Confusion Matrix: \\n', confusion_matrix(y_test,log_predicted))\n",
    "print('Classification Report: \\n', classification_report(y_test,log_predicted))\n",
    "\n",
    "sns.heatmap(confusion_matrix(y_test,log_predicted),annot=True,fmt=\"d\")\n"
   ]
  }
 ],
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