Indian Liver Disease USING Perceptron.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d302dd5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "093ca426",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read Dataset\n",
    "df = pd.read_csv(\"C:/Users/zizo/Desktop/indian_liver_patient.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3322d191",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "    </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",
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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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\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  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fe46e200",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset\n",
       "1    416\n",
       "2    167\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Dataset').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b4620b65",
   "metadata": {},
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4dbfa129",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 579 entries, 0 to 582\n",
      "Data columns (total 11 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   Age                         579 non-null    int64  \n",
      " 1   Gender                      579 non-null    object \n",
      " 2   Total_Bilirubin             579 non-null    float64\n",
      " 3   Direct_Bilirubin            579 non-null    float64\n",
      " 4   Alkaline_Phosphotase        579 non-null    int64  \n",
      " 5   Alamine_Aminotransferase    579 non-null    int64  \n",
      " 6   Aspartate_Aminotransferase  579 non-null    int64  \n",
      " 7   Total_Protiens              579 non-null    float64\n",
      " 8   Albumin                     579 non-null    float64\n",
      " 9   Albumin_and_Globulin_Ratio  579 non-null    float64\n",
      " 10  Dataset                     579 non-null    int64  \n",
      "dtypes: float64(5), int64(5), object(1)\n",
      "memory usage: 54.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# Remove records with missing values\n",
    "df.dropna(inplace=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0589b3e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gender']=df['Gender'].map({'Male':0,'Female':1}).astype(int) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "018065fd",
   "metadata": {},
   "outputs": [
    {
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       "      <td>100</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",
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       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>0</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       1              0.7               0.1                   187   \n",
       "1   62       0             10.9               5.5                   699   \n",
       "2   62       0              7.3               4.1                   490   \n",
       "3   58       0              1.0               0.4                   182   \n",
       "4   72       0              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  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6922911d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop('Dataset', axis=1)\n",
    "y = df['Dataset']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1b520a62",
   "metadata": {},
   "outputs": [
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       "      <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",
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       "      <td>0</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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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0   65       1              0.7               0.1                   187   \n",
       "1   62       0             10.9               5.5                   699   \n",
       "2   62       0              7.3               4.1                   490   \n",
       "3   58       0              1.0               0.4                   182   \n",
       "4   72       0              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  \n",
       "0      3.3                        0.90  \n",
       "1      3.2                        0.74  \n",
       "2      3.3                        0.89  \n",
       "3      3.4                        1.00  \n",
       "4      2.4                        0.40  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c357412a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "7    1\n",
       "8    2\n",
       "9    1\n",
       "Name: Dataset, dtype: int64"
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     "execution_count": 42,
     "metadata": {},
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   "source": [
    "y.head(10)"
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  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "334d649f",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_le = LabelEncoder()\n",
    "#  1 assume it is (not-patient) -> 0 and 2 assume it is (patient) -> 1\n",
    "y = class_le.fit_transform(df.Dataset.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7a4b6afa",
   "metadata": {},
   "outputs": [
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      " 1 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0\n",
      " 1 0 0 0 0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0\n",
      " 0 0 1 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0\n",
      " 1 1 0 0 1 0 1 0 0 0 0 0 0 1 0 1 1 0 0 1 0 0 0 1 0 1 1 1 1 1 0 0 0 1 0 0 0\n",
      " 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 1 1 1 1 1 1 0 0 0 1 0 1\n",
      " 1 0 0 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0\n",
      " 1 0 1 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0\n",
      " 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 1 1 1 0 0 0 1 1 1 1 1 0 0 0 0 1 0 0 1 0\n",
      " 0 0 0 1 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0\n",
      " 0 0 1 1 0 0 0 0 1 0 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "95cb994f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(393, 10) (393,)\n",
      "(186, 10) (186,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x,y,random_state=42,test_size=0.32)\n",
    "print(X_train.shape, y_train.shape)\n",
    "print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ee492f8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Data preprocessing with StandardScaler()\n",
      "\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "print('\\nData preprocessing with {scaler}\\n'.format(scaler=scaler))\n",
    "\n",
    "X_train_scaler = scaler.fit_transform(X_train)\n",
    "X_test_scaler = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2bb29c63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP Accuracy: 67.74%\n"
     ]
    }
   ],
   "source": [
    "mlp = Perceptron(\n",
    "    random_state=42\n",
    ")\n",
    "mlp.fit(X_train_scaler, y_train)\n",
    "\n",
    "mlp_predict = mlp.predict(X_test_scaler)\n",
    "print('MLP Accuracy: {:.2f}%'.format(accuracy_score(y_test, mlp_predict) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b729d63",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bdfa316",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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