From 499b59319fa99fbc38f57ec2c5ed456ca12dfba8 Mon Sep 17 00:00:00 2001
From: Rakesh Joshi <rakeshjoshi21021369@gmail.com>
Date: Thu, 16 May 2024 01:21:37 +0530
Subject: [PATCH] EDA anlaysis of the dataset

---
 .../Liver_disease_EDA.ipynb                   | 1063 +++++++++++++++++
 Liver DIsease prediction/README.md            |   27 +
 2 files changed, 1090 insertions(+)
 create mode 100644 Liver DIsease prediction/Liver_disease_EDA.ipynb
 create mode 100644 Liver DIsease prediction/README.md

diff --git a/Liver DIsease prediction/Liver_disease_EDA.ipynb b/Liver DIsease prediction/Liver_disease_EDA.ipynb
new file mode 100644
index 00000000..fac86e31
--- /dev/null
+++ b/Liver DIsease prediction/Liver_disease_EDA.ipynb	
@@ -0,0 +1,1063 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Liver Disease EDA(Exploratory Data Analysis)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=pd.read_csv(r\"C:\\Users\\rakes\\health_proj\\Liver Disease Prediction\\Dataset\\indian_liver_patient.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Set Exploration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(583, 11)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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",
+       "    <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",
+       "</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",
+       "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": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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>578</th>\n",
+       "      <td>60</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>500</td>\n",
+       "      <td>20</td>\n",
+       "      <td>34</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>0.37</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>579</th>\n",
+       "      <td>40</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>98</td>\n",
+       "      <td>35</td>\n",
+       "      <td>31</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.10</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580</th>\n",
+       "      <td>52</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>245</td>\n",
+       "      <td>48</td>\n",
+       "      <td>49</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>581</th>\n",
+       "      <td>31</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>184</td>\n",
+       "      <td>29</td>\n",
+       "      <td>32</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>582</th>\n",
+       "      <td>38</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>216</td>\n",
+       "      <td>21</td>\n",
+       "      <td>24</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Age Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
+       "578   60   Male              0.5               0.1                   500   \n",
+       "579   40   Male              0.6               0.1                    98   \n",
+       "580   52   Male              0.8               0.2                   245   \n",
+       "581   31   Male              1.3               0.5                   184   \n",
+       "582   38   Male              1.0               0.3                   216   \n",
+       "\n",
+       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
+       "578                        20                          34             5.9   \n",
+       "579                        35                          31             6.0   \n",
+       "580                        48                          49             6.4   \n",
+       "581                        29                          32             6.8   \n",
+       "582                        21                          24             7.3   \n",
+       "\n",
+       "     Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
+       "578      1.6                        0.37        2  \n",
+       "579      3.2                        1.10        1  \n",
+       "580      3.2                        1.00        1  \n",
+       "581      3.4                        1.00        1  \n",
+       "582      4.4                        1.50        2  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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>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",
+       "      <td>583.000000</td>\n",
+       "      <td>579.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>44.746141</td>\n",
+       "      <td>0.756432</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>16.189833</td>\n",
+       "      <td>0.429603</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>0.000000</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>1.000000</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>45.000000</td>\n",
+       "      <td>1.000000</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>58.000000</td>\n",
+       "      <td>1.000000</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.000000</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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Age      Gender  Total_Bilirubin  Direct_Bilirubin  \\\n",
+       "count  583.000000  583.000000       583.000000        583.000000   \n",
+       "mean    44.746141    0.756432         3.298799          1.486106   \n",
+       "std     16.189833    0.429603         6.209522          2.808498   \n",
+       "min      4.000000    0.000000         0.400000          0.100000   \n",
+       "25%     33.000000    1.000000         0.800000          0.200000   \n",
+       "50%     45.000000    1.000000         1.000000          0.300000   \n",
+       "75%     58.000000    1.000000         2.600000          1.300000   \n",
+       "max     90.000000    1.000000        75.000000         19.700000   \n",
+       "\n",
+       "       Alkaline_Phosphotase  Alamine_Aminotransferase  \\\n",
+       "count            583.000000                583.000000   \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",
+       "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",
+       "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  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(df.describe())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# To see if there are null values and target class distribution, correlation "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "416 167\n"
+     ]
+    }
+   ],
+   "source": [
+    "# target class distribution in data set, 1 represent its a liver patient and 2 represent it is not a liver patient\n",
+    "true_count=len(df.loc[df['Dataset']==1])\n",
+    "false_count=len(df.loc[df['Dataset']==2])\n",
+    "print(true_count,false_count)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "numerical_vars = ['Age', 'Gender','Total_Bilirubin', 'Direct_Bilirubin', 'Alkaline_Phosphotase', \n",
+    "                  'Alamine_Aminotransferase', 'Aspartate_Aminotransferase', 'Total_Protiens', \n",
+    "                  'Albumin', 'Albumin_and_Globulin_Ratio']\n",
+    "df[numerical_vars].hist(figsize=(12, 10))\n",
+    "plt.suptitle('Histograms of Numerical Variables', fontsize=16)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#bar chart for gender distribution in the dataset and also for the liver patient and non liver patient\n",
+    "categorical_vars = ['Gender', 'Dataset']\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "for i, var in enumerate(categorical_vars, 1):\n",
+    "    plt.subplot(1, 2, i)\n",
+    "    sns.countplot(x=var, data=df)\n",
+    "    plt.title(f'Bar Chart of {var}', fontsize=14)\n",
+    "    plt.xlabel(var)\n",
+    "    plt.ylabel('Count')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#to find the correlation we change the genders to numeric\n",
+    "df['Gender']=df['Gender'].replace({'Male':1,'Female':0})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x2000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "coormat=df.corr()#correlation between features in the dataset\n",
+    "top_corr_features=coormat.index\n",
+    "plt.figure(figsize=(20,20))\n",
+    "g=sns.heatmap(df[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\",fmt=\".2f\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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>Age</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.056560</td>\n",
+       "      <td>0.011763</td>\n",
+       "      <td>0.007529</td>\n",
+       "      <td>0.080425</td>\n",
+       "      <td>-0.086883</td>\n",
+       "      <td>-0.019910</td>\n",
+       "      <td>-0.187461</td>\n",
+       "      <td>-0.265924</td>\n",
+       "      <td>-0.216408</td>\n",
+       "      <td>-0.137351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <td>0.056560</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.089291</td>\n",
+       "      <td>0.100436</td>\n",
+       "      <td>-0.027496</td>\n",
+       "      <td>0.082332</td>\n",
+       "      <td>0.080336</td>\n",
+       "      <td>-0.089121</td>\n",
+       "      <td>-0.093799</td>\n",
+       "      <td>-0.003424</td>\n",
+       "      <td>-0.082416</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <td>0.011763</td>\n",
+       "      <td>0.089291</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.874618</td>\n",
+       "      <td>0.206669</td>\n",
+       "      <td>0.214065</td>\n",
+       "      <td>0.237831</td>\n",
+       "      <td>-0.008099</td>\n",
+       "      <td>-0.222250</td>\n",
+       "      <td>-0.206267</td>\n",
+       "      <td>-0.220208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <td>0.007529</td>\n",
+       "      <td>0.100436</td>\n",
+       "      <td>0.874618</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.234939</td>\n",
+       "      <td>0.233894</td>\n",
+       "      <td>0.257544</td>\n",
+       "      <td>-0.000139</td>\n",
+       "      <td>-0.228531</td>\n",
+       "      <td>-0.200125</td>\n",
+       "      <td>-0.246046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <td>0.080425</td>\n",
+       "      <td>-0.027496</td>\n",
+       "      <td>0.206669</td>\n",
+       "      <td>0.234939</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.125680</td>\n",
+       "      <td>0.167196</td>\n",
+       "      <td>-0.028514</td>\n",
+       "      <td>-0.165453</td>\n",
+       "      <td>-0.234166</td>\n",
+       "      <td>-0.184866</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <td>-0.086883</td>\n",
+       "      <td>0.082332</td>\n",
+       "      <td>0.214065</td>\n",
+       "      <td>0.233894</td>\n",
+       "      <td>0.125680</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.791966</td>\n",
+       "      <td>-0.042518</td>\n",
+       "      <td>-0.029742</td>\n",
+       "      <td>-0.002375</td>\n",
+       "      <td>-0.163416</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <td>-0.019910</td>\n",
+       "      <td>0.080336</td>\n",
+       "      <td>0.237831</td>\n",
+       "      <td>0.257544</td>\n",
+       "      <td>0.167196</td>\n",
+       "      <td>0.791966</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.025645</td>\n",
+       "      <td>-0.085290</td>\n",
+       "      <td>-0.070040</td>\n",
+       "      <td>-0.151934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <td>-0.187461</td>\n",
+       "      <td>-0.089121</td>\n",
+       "      <td>-0.008099</td>\n",
+       "      <td>-0.000139</td>\n",
+       "      <td>-0.028514</td>\n",
+       "      <td>-0.042518</td>\n",
+       "      <td>-0.025645</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.784053</td>\n",
+       "      <td>0.234887</td>\n",
+       "      <td>0.035008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albumin</th>\n",
+       "      <td>-0.265924</td>\n",
+       "      <td>-0.093799</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>0.689632</td>\n",
+       "      <td>0.161388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <td>-0.216408</td>\n",
+       "      <td>-0.003424</td>\n",
+       "      <td>-0.206267</td>\n",
+       "      <td>-0.200125</td>\n",
+       "      <td>-0.234166</td>\n",
+       "      <td>-0.002375</td>\n",
+       "      <td>-0.070040</td>\n",
+       "      <td>0.234887</td>\n",
+       "      <td>0.689632</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.163131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dataset</th>\n",
+       "      <td>-0.137351</td>\n",
+       "      <td>-0.082416</td>\n",
+       "      <td>-0.220208</td>\n",
+       "      <td>-0.246046</td>\n",
+       "      <td>-0.184866</td>\n",
+       "      <td>-0.163416</td>\n",
+       "      <td>-0.151934</td>\n",
+       "      <td>0.035008</td>\n",
+       "      <td>0.161388</td>\n",
+       "      <td>0.163131</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 Age    Gender  Total_Bilirubin  \\\n",
+       "Age                         1.000000  0.056560         0.011763   \n",
+       "Gender                      0.056560  1.000000         0.089291   \n",
+       "Total_Bilirubin             0.011763  0.089291         1.000000   \n",
+       "Direct_Bilirubin            0.007529  0.100436         0.874618   \n",
+       "Alkaline_Phosphotase        0.080425 -0.027496         0.206669   \n",
+       "Alamine_Aminotransferase   -0.086883  0.082332         0.214065   \n",
+       "Aspartate_Aminotransferase -0.019910  0.080336         0.237831   \n",
+       "Total_Protiens             -0.187461 -0.089121        -0.008099   \n",
+       "Albumin                    -0.265924 -0.093799        -0.222250   \n",
+       "Albumin_and_Globulin_Ratio -0.216408 -0.003424        -0.206267   \n",
+       "Dataset                    -0.137351 -0.082416        -0.220208   \n",
+       "\n",
+       "                            Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
+       "Age                                 0.007529              0.080425   \n",
+       "Gender                              0.100436             -0.027496   \n",
+       "Total_Bilirubin                     0.874618              0.206669   \n",
+       "Direct_Bilirubin                    1.000000              0.234939   \n",
+       "Alkaline_Phosphotase                0.234939              1.000000   \n",
+       "Alamine_Aminotransferase            0.233894              0.125680   \n",
+       "Aspartate_Aminotransferase          0.257544              0.167196   \n",
+       "Total_Protiens                     -0.000139             -0.028514   \n",
+       "Albumin                            -0.228531             -0.165453   \n",
+       "Albumin_and_Globulin_Ratio         -0.200125             -0.234166   \n",
+       "Dataset                            -0.246046             -0.184866   \n",
+       "\n",
+       "                            Alamine_Aminotransferase  \\\n",
+       "Age                                        -0.086883   \n",
+       "Gender                                      0.082332   \n",
+       "Total_Bilirubin                             0.214065   \n",
+       "Direct_Bilirubin                            0.233894   \n",
+       "Alkaline_Phosphotase                        0.125680   \n",
+       "Alamine_Aminotransferase                    1.000000   \n",
+       "Aspartate_Aminotransferase                  0.791966   \n",
+       "Total_Protiens                             -0.042518   \n",
+       "Albumin                                    -0.029742   \n",
+       "Albumin_and_Globulin_Ratio                 -0.002375   \n",
+       "Dataset                                    -0.163416   \n",
+       "\n",
+       "                            Aspartate_Aminotransferase  Total_Protiens  \\\n",
+       "Age                                          -0.019910       -0.187461   \n",
+       "Gender                                        0.080336       -0.089121   \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                   -0.070040        0.234887   \n",
+       "Dataset                                      -0.151934        0.035008   \n",
+       "\n",
+       "                             Albumin  Albumin_and_Globulin_Ratio   Dataset  \n",
+       "Age                        -0.265924                   -0.216408 -0.137351  \n",
+       "Gender                     -0.093799                   -0.003424 -0.082416  \n",
+       "Total_Bilirubin            -0.222250                   -0.206267 -0.220208  \n",
+       "Direct_Bilirubin           -0.228531                   -0.200125 -0.246046  \n",
+       "Alkaline_Phosphotase       -0.165453                   -0.234166 -0.184866  \n",
+       "Alamine_Aminotransferase   -0.029742                   -0.002375 -0.163416  \n",
+       "Aspartate_Aminotransferase -0.085290                   -0.070040 -0.151934  \n",
+       "Total_Protiens              0.784053                    0.234887  0.035008  \n",
+       "Albumin                     1.000000                    0.689632  0.161388  \n",
+       "Albumin_and_Globulin_Ratio  0.689632                    1.000000  0.163131  \n",
+       "Dataset                     0.161388                    0.163131  1.000000  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Filling the null values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#we can also fill the nan values in the column Albumin_and_Globulin_Ratio\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "imputer=SimpleImputer(strategy='mean')\n",
+    "imputer.fit(df[['Albumin_and_Globulin_Ratio']])\n",
+    "df['Albumin_and_Globulin_Ratio']=imputer.transform(df[['Albumin_and_Globulin_Ratio']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "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    0\n",
+       "Dataset                       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Liver DIsease prediction/README.md b/Liver DIsease prediction/README.md
new file mode 100644
index 00000000..94b1c541
--- /dev/null
+++ b/Liver DIsease prediction/README.md	
@@ -0,0 +1,27 @@
+# Liver Disease Prediction  Dataset 📊
+
+Patients with Liver disease have been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. This dataset was used to evaluate prediction algorithms in an effort to reduce burden on doctors.
+
+- [Liver Disease Prediction Dataset](https://www.kaggle.com/datasets/uciml/indian-liver-patient-records)
+
+## Dataset Features 📋
+
+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). This data set contains 441 male patient records and 142 female patient records.
+Columns:
+
+- **Age**: Older age may indicate a higher risk of liver disease.
+- **Gender**: Certain liver diseases have gender-specific prevalence.
+- **Total Bilirubin**: Elevated levels suggest liver dysfunction.
+- **Direct Bilirubin**: Elevated levels may indicate bile duct obstruction.
+- **Alkaline Phosphotase**: Elevated levels can signal liver or bone disease.
+- **Alamine Aminotransferase (ALT)**: Elevated levels indicate liver damage.
+- **Aspartate Aminotransferase (AST)**: Elevated levels suggest liver inflammation.
+- **Total Proteins**: Changes may occur in liver disease.
+- **Albumin**: Decreased levels suggest liver dysfunction.
+- **Albumin and Globulin Ratio**: Provides additional insight into liver function.
+- **Dataset**: Indicates whether the patient has liver disease.
+- **Diagnosis**: Indicates whether the patient has diabetes.
+
+## Inspiration 💡
+
+This dataset can inspire research and analysis aimed at predicting liver patient based on various clinical indicators. By examining the relationship between various parameters.