diff --git a/Liver DIsease prediction/Liver_disease_EDA.ipynb b/Liver DIsease prediction/Liver_disease_EDA.ipynb new file mode 100644 index 0000000..05239c1 --- /dev/null +++ b/Liver DIsease prediction/Liver_disease_EDA.ipynb @@ -0,0 +1,1739 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Liver Disease Prediction | Dataset exploration " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Load the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "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": 27, + "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": [ + "## 2. Explore and get the labels of the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(583, 11)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AgeGenderTotal_BilirubinDirect_BilirubinAlkaline_PhosphotaseAlamine_AminotransferaseAspartate_AminotransferaseTotal_ProtiensAlbuminAlbumin_and_Globulin_RatioDataset
065Female0.70.118716186.83.30.901
162Male10.95.5699641007.53.20.741
262Male7.34.149060687.03.30.891
358Male1.00.418214206.83.41.001
472Male3.92.019527597.32.40.401
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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": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AgeGenderTotal_BilirubinDirect_BilirubinAlkaline_PhosphotaseAlamine_AminotransferaseAspartate_AminotransferaseTotal_ProtiensAlbuminAlbumin_and_Globulin_RatioDataset
57860Male0.50.150020345.91.60.372
57940Male0.60.19835316.03.21.101
58052Male0.80.224548496.43.21.001
58131Male1.30.518429326.83.41.001
58238Male1.00.321621247.34.41.502
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" + ], + "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": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": true + }, + "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": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Statistical details like mean, meadain and x percentiles of the labels" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AgeTotal_BilirubinDirect_BilirubinAlkaline_PhosphotaseAlamine_AminotransferaseAspartate_AminotransferaseTotal_ProtiensAlbuminAlbumin_and_Globulin_RatioDataset
count583.000000583.000000583.000000583.000000583.000000583.000000583.000000583.000000579.000000583.000000
mean44.7461413.2987991.486106290.57632980.713551109.9108066.4831903.1418520.9470641.286449
std16.1898336.2095222.808498242.937989182.620356288.9185291.0854510.7955190.3195920.452490
min4.0000000.4000000.10000063.00000010.00000010.0000002.7000000.9000000.3000001.000000
25%33.0000000.8000000.200000175.50000023.00000025.0000005.8000002.6000000.7000001.000000
50%45.0000001.0000000.300000208.00000035.00000042.0000006.6000003.1000000.9300001.000000
75%58.0000002.6000001.300000298.00000060.50000087.0000007.2000003.8000001.1000002.000000
max90.00000075.00000019.7000002110.0000002000.0000004929.0000009.6000005.5000002.8000002.000000
\n", + "
" + ], + "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 \n", + "count 583.000000 579.000000 583.000000 \n", + "mean 3.141852 0.947064 1.286449 \n", + "std 0.795519 0.319592 0.452490 \n", + "min 0.900000 0.300000 1.000000 \n", + "25% 2.600000 0.700000 1.000000 \n", + "50% 3.100000 0.930000 1.000000 \n", + "75% 3.800000 1.100000 2.000000 \n", + "max 5.500000 2.800000 2.000000 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(df.describe())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.Finiding NULL valur and performing imputation and changes as required" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": true + }, + "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": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "feature Albumin_and_Globulin_Ratio have 4 null values which should to filled for furthing finding for correlations and other insights.
\n", + "->so here i am using mean method to impute the values in place of null values as only few values are null so default function will work" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "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": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 583 entries, 0 to 582\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Age 583 non-null int64 \n", + " 1 Gender 583 non-null object \n", + " 2 Total_Bilirubin 583 non-null float64\n", + " 3 Direct_Bilirubin 583 non-null float64\n", + " 4 Alkaline_Phosphotase 583 non-null int64 \n", + " 5 Alamine_Aminotransferase 583 non-null int64 \n", + " 6 Aspartate_Aminotransferase 583 non-null int64 \n", + " 7 Total_Protiens 583 non-null float64\n", + " 8 Albumin 583 non-null float64\n", + " 9 Albumin_and_Globulin_Ratio 583 non-null float64\n", + " 10 Dataset 583 non-null int64 \n", + "dtypes: float64(5), int64(5), object(1)\n", + "memory usage: 50.2+ KB\n" + ] + } + ], + "source": [ + "#to get the data type of each feature\n", + "df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In above we can see that Gender is not a numeric type so we convert it numeric for further analysis
\n", + "-> Gender: 1-male and 0-female" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "df['Gender']=df['Gender'].replace({'Male':1,'Female':0})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. EDA\n", + "-> Check the distribution of the columns like gender and target class(dataset)
\n", + "-> Perfoming multivariant analysis on all parameters and drawing conclusions from them
\n", + "-> explore the correlation matrix
\n", + "-> Overall Insights
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ->Check for Distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "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": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#bar chart for the target class (which is dataset here) and gender to visualize its distriubtion\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": "markdown", + "metadata": {}, + "source": [ + "### ->Mulitvariant analysis on Features" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "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": "markdown", + "metadata": {}, + "source": [ + "Analysis of the histogram:
\n", + "-> Age: The graph shows that most of the people age is between 30 to 70 in the dataset
\n", + "-> Gender: Gender is only 0 (for female) and 1 (for male) also indicates that male dominant data is present in the dataset
\n", + "-> Total Billirubin: around 500 people in the dataset have total billirubin between 0 to 10 in their blood
\n", + "-> Direct Billirubin: Direct billirubin which is processed by liver, in the dataset around 450 people have direct billirubin between 0 to 2 in their blood.\n", + "-> Alkaline phosphotase(ALP):Few people in dataset have high elevated ALP enzyme which indicates liver disorders
\n", + "-> Alanine Aminotransferase(ALT): This enzyme which convert alanine and amino acid into pyruvate but high level of this causes damages to liver and result int fatty liver disease. In dataset very few have high level ALT.
\n", + "-> Aspartate Aminotransferase(AST): High AST causes hepatitis, few have high level AST in the dataset.
\n", + "-> Total Protien: This indicates total amount of albumin and globulin in the blood, its low levels (less than 6g) indicates liver disease. Around 60 people in the dataset have it bewtween 5-6 gm and some have even less than this.
\n", + "-> Albumin: Main protien made by the liver, low levels(less than 3.5) indicate chronic liver disease. Around 100 people have low level of albumin the dataset.
\n", + "-> A/G ratio: low ratio signifise liver disease, in dataset around 50 have this ratio less than 0.5." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### -> Explore the Correaltion matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "corr=df.corr(method='kendall')\n", + "plt.figure(figsize=(10,10))\n", + "sns.heatmap(corr,annot=True,cmap='coolwarm',fmt=\".2f\",linewidth=.5)\n", + "plt.title(\"Correlation\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "the exact correaltion value is not visible in the heatmap so going to print the whole matrix and derive something from there.
\n", + "Correlation with respect to different methods can be derived, correlation methods like pearson, spearman, kendall will be used here" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AgeGenderTotal_BilirubinDirect_BilirubinAlkaline_PhosphotaseAlamine_AminotransferaseAspartate_AminotransferaseTotal_ProtiensAlbuminAlbumin_and_Globulin_RatioDataset
Age1.0000000.0565600.0117630.0075290.080425-0.086883-0.019910-0.187461-0.265924-0.216089-0.137351
Gender0.0565601.0000000.0892910.100436-0.0274960.0823320.080336-0.089121-0.093799-0.003404-0.082416
Total_Bilirubin0.0117630.0892911.0000000.8746180.2066690.2140650.237831-0.008099-0.222250-0.206159-0.220208
Direct_Bilirubin0.0075290.1004360.8746181.0000000.2349390.2338940.257544-0.000139-0.228531-0.200004-0.246046
Alkaline_Phosphotase0.080425-0.0274960.2066690.2349391.0000000.1256800.167196-0.028514-0.165453-0.233960-0.184866
Alamine_Aminotransferase-0.0868830.0823320.2140650.2338940.1256801.0000000.791966-0.042518-0.029742-0.002374-0.163416
Aspartate_Aminotransferase-0.0199100.0803360.2378310.2575440.1671960.7919661.000000-0.025645-0.085290-0.070024-0.151934
Total_Protiens-0.187461-0.089121-0.008099-0.000139-0.028514-0.042518-0.0256451.0000000.7840530.2339040.035008
Albumin-0.265924-0.093799-0.222250-0.228531-0.165453-0.029742-0.0852900.7840531.0000000.6863220.161388
Albumin_and_Globulin_Ratio-0.216089-0.003404-0.206159-0.200004-0.233960-0.002374-0.0700240.2339040.6863221.0000000.162319
Dataset-0.137351-0.082416-0.220208-0.246046-0.184866-0.163416-0.1519340.0350080.1613880.1623191.000000
\n", + "
" + ], + "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 -0.216089 -0.003404 -0.206159 \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 -0.200004 -0.233960 \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 -0.002374 \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 -0.070024 0.233904 \n", + "Dataset -0.151934 0.035008 \n", + "\n", + " Albumin Albumin_and_Globulin_Ratio Dataset \n", + "Age -0.265924 -0.216089 -0.137351 \n", + "Gender -0.093799 -0.003404 -0.082416 \n", + "Total_Bilirubin -0.222250 -0.206159 -0.220208 \n", + "Direct_Bilirubin -0.228531 -0.200004 -0.246046 \n", + "Alkaline_Phosphotase -0.165453 -0.233960 -0.184866 \n", + "Alamine_Aminotransferase -0.029742 -0.002374 -0.163416 \n", + "Aspartate_Aminotransferase -0.085290 -0.070024 -0.151934 \n", + "Total_Protiens 0.784053 0.233904 0.035008 \n", + "Albumin 1.000000 0.686322 0.161388 \n", + "Albumin_and_Globulin_Ratio 0.686322 1.000000 0.162319 \n", + "Dataset 0.161388 0.162319 1.000000 " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.corr(method='pearson')" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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AgeGenderTotal_BilirubinDirect_BilirubinAlkaline_PhosphotaseAlamine_AminotransferaseAspartate_AminotransferaseTotal_ProtiensAlbuminAlbumin_and_Globulin_RatioDataset
Age1.0000000.0623640.1138270.1064730.059205-0.067737-0.018285-0.174271-0.260791-0.249505-0.129572
Gender0.0623641.0000000.2005030.2092100.0791310.2011070.209434-0.090905-0.095440-0.008342-0.082416
Total_Bilirubin0.1138270.2005031.0000000.9592160.3837940.4365860.508869-0.019252-0.222184-0.284200-0.303879
Direct_Bilirubin0.1064730.2092100.9592161.0000000.3678180.4123220.504138-0.019987-0.232664-0.297338-0.297270
Alkaline_Phosphotase0.0592050.0791310.3837940.3678181.0000000.4107520.3957320.014028-0.170809-0.321095-0.273247
Alamine_Aminotransferase-0.0677370.2011070.4365860.4123220.4107521.0000000.773611-0.018811-0.052673-0.082942-0.290709
Aspartate_Aminotransferase-0.0182850.2094340.5088690.5041380.3957320.7736111.000000-0.084779-0.204867-0.208809-0.308897
Total_Protiens-0.174271-0.090905-0.019252-0.0199870.014028-0.018811-0.0847791.0000000.7790770.2724900.032220
Albumin-0.260791-0.095440-0.222184-0.232664-0.170809-0.052673-0.2048670.7790771.0000000.7512230.167079
Albumin_and_Globulin_Ratio-0.249505-0.008342-0.284200-0.297338-0.321095-0.082942-0.2088090.2724900.7512231.0000000.187377
Dataset-0.129572-0.082416-0.303879-0.297270-0.273247-0.290709-0.3088970.0322200.1670790.1873771.000000
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" + ], + "text/plain": [ + " Age Gender Total_Bilirubin \\\n", + "Age 1.000000 0.062364 0.113827 \n", + "Gender 0.062364 1.000000 0.200503 \n", + "Total_Bilirubin 0.113827 0.200503 1.000000 \n", + "Direct_Bilirubin 0.106473 0.209210 0.959216 \n", + "Alkaline_Phosphotase 0.059205 0.079131 0.383794 \n", + "Alamine_Aminotransferase -0.067737 0.201107 0.436586 \n", + "Aspartate_Aminotransferase -0.018285 0.209434 0.508869 \n", + "Total_Protiens -0.174271 -0.090905 -0.019252 \n", + "Albumin -0.260791 -0.095440 -0.222184 \n", + "Albumin_and_Globulin_Ratio -0.249505 -0.008342 -0.284200 \n", + "Dataset -0.129572 -0.082416 -0.303879 \n", + "\n", + " Direct_Bilirubin Alkaline_Phosphotase \\\n", + "Age 0.106473 0.059205 \n", + "Gender 0.209210 0.079131 \n", + "Total_Bilirubin 0.959216 0.383794 \n", + "Direct_Bilirubin 1.000000 0.367818 \n", + "Alkaline_Phosphotase 0.367818 1.000000 \n", + "Alamine_Aminotransferase 0.412322 0.410752 \n", + "Aspartate_Aminotransferase 0.504138 0.395732 \n", + "Total_Protiens -0.019987 0.014028 \n", + "Albumin -0.232664 -0.170809 \n", + "Albumin_and_Globulin_Ratio -0.297338 -0.321095 \n", + "Dataset -0.297270 -0.273247 \n", + "\n", + " Alamine_Aminotransferase \\\n", + "Age -0.067737 \n", + "Gender 0.201107 \n", + "Total_Bilirubin 0.436586 \n", + "Direct_Bilirubin 0.412322 \n", + "Alkaline_Phosphotase 0.410752 \n", + "Alamine_Aminotransferase 1.000000 \n", + "Aspartate_Aminotransferase 0.773611 \n", + "Total_Protiens -0.018811 \n", + "Albumin -0.052673 \n", + "Albumin_and_Globulin_Ratio -0.082942 \n", + "Dataset -0.290709 \n", + "\n", + " Aspartate_Aminotransferase Total_Protiens \\\n", + "Age -0.018285 -0.174271 \n", + "Gender 0.209434 -0.090905 \n", + "Total_Bilirubin 0.508869 -0.019252 \n", + "Direct_Bilirubin 0.504138 -0.019987 \n", + "Alkaline_Phosphotase 0.395732 0.014028 \n", + "Alamine_Aminotransferase 0.773611 -0.018811 \n", + "Aspartate_Aminotransferase 1.000000 -0.084779 \n", + "Total_Protiens -0.084779 1.000000 \n", + "Albumin -0.204867 0.779077 \n", + "Albumin_and_Globulin_Ratio -0.208809 0.272490 \n", + "Dataset -0.308897 0.032220 \n", + "\n", + " Albumin Albumin_and_Globulin_Ratio Dataset \n", + "Age -0.260791 -0.249505 -0.129572 \n", + "Gender -0.095440 -0.008342 -0.082416 \n", + "Total_Bilirubin -0.222184 -0.284200 -0.303879 \n", + "Direct_Bilirubin -0.232664 -0.297338 -0.297270 \n", + "Alkaline_Phosphotase -0.170809 -0.321095 -0.273247 \n", + "Alamine_Aminotransferase -0.052673 -0.082942 -0.290709 \n", + "Aspartate_Aminotransferase -0.204867 -0.208809 -0.308897 \n", + "Total_Protiens 0.779077 0.272490 0.032220 \n", + "Albumin 1.000000 0.751223 0.167079 \n", + "Albumin_and_Globulin_Ratio 0.751223 1.000000 0.187377 \n", + "Dataset 0.167079 0.187377 1.000000 " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.corr(method='spearman')" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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AgeGenderTotal_BilirubinDirect_BilirubinAlkaline_PhosphotaseAlamine_AminotransferaseAspartate_AminotransferaseTotal_ProtiensAlbuminAlbumin_and_Globulin_RatioDataset
Age1.0000000.0515200.0780990.0747330.038868-0.046261-0.013206-0.120690-0.180176-0.177241-0.107040
Gender0.0515201.0000000.1686710.1803620.0648460.1655210.172013-0.075289-0.079378-0.007077-0.082416
Total_Bilirubin0.0780990.1686711.0000000.8981360.2702670.3064830.361626-0.014417-0.153281-0.203865-0.255635
Direct_Bilirubin0.0747330.1803620.8981361.0000000.2658090.2924160.364823-0.015559-0.164638-0.219255-0.256279
Alkaline_Phosphotase0.0388680.0648460.2702670.2658091.0000000.2779390.2641680.010076-0.115934-0.227519-0.223921
Alamine_Aminotransferase-0.0462610.1655210.3064830.2924160.2779391.0000000.596488-0.012909-0.033134-0.055798-0.239269
Aspartate_Aminotransferase-0.0132060.1720130.3616260.3648230.2641680.5964881.000000-0.057158-0.137458-0.145771-0.253705
Total_Protiens-0.120690-0.075289-0.014417-0.0155590.010076-0.012909-0.0571581.0000000.6130980.1922200.026685
Albumin-0.180176-0.079378-0.153281-0.164638-0.115934-0.033134-0.1374580.6130981.0000000.5929800.138960
Albumin_and_Globulin_Ratio-0.177241-0.007077-0.203865-0.219255-0.227519-0.055798-0.1457710.1922200.5929801.0000000.158967
Dataset-0.107040-0.082416-0.255635-0.256279-0.223921-0.239269-0.2537050.0266850.1389600.1589671.000000
\n", + "
" + ], + "text/plain": [ + " Age Gender Total_Bilirubin \\\n", + "Age 1.000000 0.051520 0.078099 \n", + "Gender 0.051520 1.000000 0.168671 \n", + "Total_Bilirubin 0.078099 0.168671 1.000000 \n", + "Direct_Bilirubin 0.074733 0.180362 0.898136 \n", + "Alkaline_Phosphotase 0.038868 0.064846 0.270267 \n", + "Alamine_Aminotransferase -0.046261 0.165521 0.306483 \n", + "Aspartate_Aminotransferase -0.013206 0.172013 0.361626 \n", + "Total_Protiens -0.120690 -0.075289 -0.014417 \n", + "Albumin -0.180176 -0.079378 -0.153281 \n", + "Albumin_and_Globulin_Ratio -0.177241 -0.007077 -0.203865 \n", + "Dataset -0.107040 -0.082416 -0.255635 \n", + "\n", + " Direct_Bilirubin Alkaline_Phosphotase \\\n", + "Age 0.074733 0.038868 \n", + "Gender 0.180362 0.064846 \n", + "Total_Bilirubin 0.898136 0.270267 \n", + "Direct_Bilirubin 1.000000 0.265809 \n", + "Alkaline_Phosphotase 0.265809 1.000000 \n", + "Alamine_Aminotransferase 0.292416 0.277939 \n", + "Aspartate_Aminotransferase 0.364823 0.264168 \n", + "Total_Protiens -0.015559 0.010076 \n", + "Albumin -0.164638 -0.115934 \n", + "Albumin_and_Globulin_Ratio -0.219255 -0.227519 \n", + "Dataset -0.256279 -0.223921 \n", + "\n", + " Alamine_Aminotransferase \\\n", + "Age -0.046261 \n", + "Gender 0.165521 \n", + "Total_Bilirubin 0.306483 \n", + "Direct_Bilirubin 0.292416 \n", + "Alkaline_Phosphotase 0.277939 \n", + "Alamine_Aminotransferase 1.000000 \n", + "Aspartate_Aminotransferase 0.596488 \n", + "Total_Protiens -0.012909 \n", + "Albumin -0.033134 \n", + "Albumin_and_Globulin_Ratio -0.055798 \n", + "Dataset -0.239269 \n", + "\n", + " Aspartate_Aminotransferase Total_Protiens \\\n", + "Age -0.013206 -0.120690 \n", + "Gender 0.172013 -0.075289 \n", + "Total_Bilirubin 0.361626 -0.014417 \n", + "Direct_Bilirubin 0.364823 -0.015559 \n", + "Alkaline_Phosphotase 0.264168 0.010076 \n", + "Alamine_Aminotransferase 0.596488 -0.012909 \n", + "Aspartate_Aminotransferase 1.000000 -0.057158 \n", + "Total_Protiens -0.057158 1.000000 \n", + "Albumin -0.137458 0.613098 \n", + "Albumin_and_Globulin_Ratio -0.145771 0.192220 \n", + "Dataset -0.253705 0.026685 \n", + "\n", + " Albumin Albumin_and_Globulin_Ratio Dataset \n", + "Age -0.180176 -0.177241 -0.107040 \n", + "Gender -0.079378 -0.007077 -0.082416 \n", + "Total_Bilirubin -0.153281 -0.203865 -0.255635 \n", + "Direct_Bilirubin -0.164638 -0.219255 -0.256279 \n", + "Alkaline_Phosphotase -0.115934 -0.227519 -0.223921 \n", + "Alamine_Aminotransferase -0.033134 -0.055798 -0.239269 \n", + "Aspartate_Aminotransferase -0.137458 -0.145771 -0.253705 \n", + "Total_Protiens 0.613098 0.192220 0.026685 \n", + "Albumin 1.000000 0.592980 0.138960 \n", + "Albumin_and_Globulin_Ratio 0.592980 1.000000 0.158967 \n", + "Dataset 0.138960 0.158967 1.000000 " + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.corr(method='kendall')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-> Pearson correlation: Feature in which target class (dataset) depends the most
\n", + "* Albumin\n", + "* A/G ratio\n", + "* Total protiens
\n", + "
\n", + "\n", + "-> Spearman correlation: Feature in which target class depends the most
\n", + "* A/G ratio\n", + "* Albumin\n", + "* Total protiens
\n", + "
\n", + "\n", + "-> Kendall correlation : Feature in which target class depends the most
\n", + "* A/G ratio\n", + "* Albumin\n", + "* Total protiens" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ->Overall Insights\n", + "From the above correlation and visualisation we can conclude that target class(dataset) mostly depends on these features (descending order):
\n", + "* A/G ratio\n", + "* Albumin\n", + "* Total protiens\n", + "
\n", + "\n", + "So from the original features
\n", + "**10 independent variable - 1 dependent class**
\n", + "to
\n", + "**3 independent variable - 1 dependent class**" + ] + }, + { + "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 0000000..94b1c54 --- /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.