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This data set contains 416 liver patient records and 167 non liver patient records collected from North East of Andhra Pradesh, India.
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The "Dataset" column is a class label used to divide groups into liver patient (liver disease) or not (no disease).
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This data set contains 441 male patient records and 142 female patient records.
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Any patient whose age exceeded 89 is listed as being of age "90".
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
from sklearn.linear_model import LogisticRegressiondf = pd.read_csv("/content/drive/MyDrive/Colab Notebooks/Semester 3/19AI403 _Intro to DS/Mini_Project/Liver.csv")
df
df.head()
df.info()
df.describe()
df.tail()
df.shape
df.columns
df.duplicated()
df.duplicated().sum()
df[df.duplicated()]
df=df.drop_duplicates()
df.duplicated().sum()
df.isnull().sum()
df['Albumin_and_Globulin_Ratio'] = df['Albumin_and_Globulin_Ratio'].fillna(df['Albumin_and_Globulin_Ratio'].mean())
df.isnull().sum()
df['Dataset']=df['Dataset'].map({1:1,2:0})
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df["Gender"] = le.fit_transform(df["Gender"])
dfdf.drop('Dataset',axis=1).plot(kind='box',layout=(2,5),subplots=True,figsize=(12,6))
plt.show()
q1=df['Total_Bilirubin'].quantile(0.25)
q3=df['Total_Bilirubin'].quantile(0.75)
iqr=q3-q1
df['Total_Bilirubin']=df['Total_Bilirubin'].apply(lambda x : q1-(1.5*iqr) if x<q1-(1.5*iqr) else x).apply(lambda x : q3+(1.5*iqr) if x>q3+(1.5*iqr) else x)
q1=df['Direct_Bilirubin'].quantile(0.25)
q3=df['Direct_Bilirubin'].quantile(0.75)
iqr=q3-q1
df['Direct_Bilirubin']=df['Direct_Bilirubin'].apply(lambda x : q3+(1.5*iqr) if x>q3+(1.5*iqr) else x).apply(lambda x : q1-(1.5*iqr) if x<q1-(1.5*iqr) else x)
q1=df['Alkaline_Phosphotase'].quantile(0.25)
q3=df['Alkaline_Phosphotase'].quantile(0.75)
iqr=q3-q1
df['Alkaline_Phosphotase']=df['Alkaline_Phosphotase'].apply(lambda x : q3+(1.5*iqr) if x>q3+(1.5*iqr) else x).apply(lambda x : q1-(1.5*iqr) if x<q1-(1.5*iqr) else x)
q1=df['Alamine_Aminotransferase'].quantile(0.25)
q3=df['Alamine_Aminotransferase'].quantile(0.75)
iqr=q3-q1
df['Alamine_Aminotransferase']=df['Alamine_Aminotransferase'].apply(lambda x : q3+(1.5*iqr) if x>q3+(1.5*iqr) else x).apply(lambda x : q1-(1.5*iqr) if x<q1-(1.5*iqr) else x)
q1=df['Aspartate_Aminotransferase'].quantile(0.25)
q3=df['Aspartate_Aminotransferase'].quantile(0.75)
iqr=q3-q1
df['Aspartate_Aminotransferase']=df['Aspartate_Aminotransferase'].apply(lambda x : q3+(1.5*iqr) if x>q3+(1.5*iqr) else x).apply(lambda x : q1-(1.5*iqr) if x<q1-(1.5*iqr) else x)
q1=df['Total_Protiens'].quantile(0.25)
q3=df['Total_Protiens'].quantile(0.75)
iqr=q3-q1
df['Total_Protiens']=df['Total_Protiens'].apply(lambda x : q3+(1.5*iqr) if x>q3+(1.5*iqr) else x).apply(lambda x : q1-(1.5*iqr) if x<q1-(1.5*iqr) else x)
q1=df['Albumin_and_Globulin_Ratio'].quantile(0.25)
q3=df['Albumin_and_Globulin_Ratio'].quantile(0.75)
iqr=q3-q1
df['Albumin_and_Globulin_Ratio']=df['Albumin_and_Globulin_Ratio'].apply(lambda x : q3+(1.5*iqr) if x>q3+(1.5*iqr) else x).apply(lambda x : q1-(1.5*iqr) if x<q1-(1.5*iqr) else x)
df.drop('Dataset',axis=1).plot(kind='box',layout=(2,5),subplots=True,figsize=(12,6))
plt.show()plt.figure(figsize=(10,6))
sns.heatmap(df.corr(),cmap='magma',annot=True)
plt.show()plt.figure(figsize=(10,5))
df.corr()['Dataset'].sort_values(ascending=False).plot(kind='bar',color='black')
plt.xticks(rotation=90)
plt.xlabel('Variables in the Data')
plt.ylabel('Correlation Values')
plt.show()- We have 10 independent variables to predict the class (y-variable).
- The class contains binary values either 0 or 1.
- Notation 0 stands for non-liver patients and 1 stands for people with liver disease.
- We have data of 416 people with liver problem and 167 people without liver problem
df['Dataset'].value_counts()asc = df["Age"].value_counts().sort_values(ascending=False).index
plt.figure(figsize = (18,6))
sns.countplot(data=df,x=df["Age"],hue = df["Dataset"],order=asc)
plt.title('Count across Age',fontsize=16)
plt.show()plt.figure(figsize=(9,6))
df['Gender'].value_counts().plot(kind='pie',autopct='%.2f%%',colors=['goldenrod','dimgray'],explode=(0,0.2),shadow=True)
label=['Male','Female']
plt.title('Percentage difference in the count between Male and Female')
plt.legend(label,title='Category')
plt.show()plt.figure(figsize=(9,6))
df['Dataset'].value_counts().plot(kind='pie',autopct='%.2f%%',colors=['deepskyblue','lawngreen'],explode=(0,0.2),shadow=True)
plt.title('Percentage difference in the count between Healthy & Diseased')
plt.show()sns.countplot(df['Dataset'],palette='Set2')
plt.title('Count Across Disease')
plt.show()sns.countplot(x="Dataset", hue="Gender", data=df)- People starts to get the Liver Disease from the age of 25 to 50
- There is chances because of the youngster consuming lot of junk food and Processed foods.
- May be also there is possibilty No taking proper food at proper time
plt.style.use("seaborn")
fig, ax = plt.subplots(figsize=(8,6))
plt.margins(x=0)
plt.title('Histogram for Age with Disease')
sns.histplot(x = df["Age"], hue = df["Dataset"], palette="winter_r", kde=True)g = sns.FacetGrid(df, col="Gender", row="Dataset", margin_titles=True)
g.map(plt.scatter,"Direct_Bilirubin", "Total_Bilirubin", edgecolor="w")
plt.subplots_adjust(top=0.9)
sns.jointplot("Total_Bilirubin", "Direct_Bilirubin", data=df, kind="reg")There seems to be direct relationship between Total_Bilirubin and Direct_Bilirubin.
g = sns.FacetGrid(df, col="Gender", row="Dataset", margin_titles=True)
g.map(plt.scatter,"Aspartate_Aminotransferase", "Alamine_Aminotransferase", edgecolor="w")
plt.subplots_adjust(top=0.9)
sns.jointplot("Aspartate_Aminotransferase", "Alamine_Aminotransferase", data=df, kind="reg")There is linear relationship between Aspartate_Aminotransferase and Alamine_Aminotransferase and the gender.
g = sns.FacetGrid(df, col="Gender", row="Dataset", margin_titles=True)
g.map(plt.scatter,"Alkaline_Phosphotase", "Alamine_Aminotransferase", edgecolor="w")
plt.subplots_adjust(top=0.9)
sns.jointplot("Alkaline_Phosphotase", "Alamine_Aminotransferase", data=df, kind="reg")No linear correlation between Alkaline_Phosphotase and Alamine_Aminotransferase
g = sns.FacetGrid(df, col="Gender", row="Dataset", margin_titles=True)
g.map(plt.scatter,"Total_Protiens", "Albumin", edgecolor="w")
plt.subplots_adjust(top=0.9)
sns.jointplot("Total_Protiens", "Albumin", data=df, kind="reg")There is linear relationship between Total_Protiens and Albumin and the gender.
g = sns.FacetGrid(df, col="Gender", row="Dataset", margin_titles=True)
g.map(plt.scatter,"Albumin", "Albumin_and_Globulin_Ratio", edgecolor="w")
plt.subplots_adjust(top=0.9)
sns.jointplot("Albumin_and_Globulin_Ratio", "Albumin", data=df, kind="reg")There is linear relationship between Albumin_and_Globulin_Ratio and Albumin.
From the above jointplots and scatterplots, we find direct relationship between the following features:
- Direct_Bilirubin & Total_Bilirubin
- Aspartate_Aminotransferase & Alamine_Aminotransferase
- Total_Protiens & Albumin
- Albumin_and_Globulin_Ratio & Albumin
X = df.drop(['Dataset'], axis=1)
y = df['Dataset']
scaler=MinMaxScaler()
scaled_values=scaler.fit_transform(X)
X.loc[:,:]=scaled_values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.40, random_state=101)
print("Training sample shape =",X_train.shape)
print("Testing sample sample =",X_test.shape)
reg = LogisticRegression()
reg.fit(X_train, y_train)
log_predicted= reg.predict(X_test)
print('Accuracy: \n', accuracy_score(y_test,log_predicted))
print('Confusion Matrix: \n', confusion_matrix(y_test,log_predicted))
sns.heatmap(confusion_matrix(y_test,log_predicted),annot=True,fmt="d")
print('Classification Report: \n', classification_report(y_test,log_predicted))
pred = reg.predict([[38,1,1.0,0.3,216,21,24,7.3,4.4,1.50]])
if(pred == 1):
print("Infected with Liver Cirrohisis")
else:
print("Not Infected with Liver Cirrohisis")- It is normal to have some bilirubin in the blood. A normal level is:
- Direct bilirubin: less than 0.3 mg/dL
- Total bilirubin: 0.1 to 1.2 mg/dL
- Alkaline_Phosphotase - The normal range is 44 to 147 international units7 per liter
- Alamine_Aminotransferase - The normal range is 4 to 36 U/L
- Aspartate_Aminotransferase - The normal range is 8 to 33 U/L.
- Total_Protiens - The normal range is 6.0 to 8.3 grams per deciliter (g/dL)
- Albumin - The normal range is 3.4 to 5.4 g/dL
- Albumin_and_Globulin_Ratio - The normal range for albumin/globulin ratio is over 1 , usually around 1 to 2.
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