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import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import pickle
heart_data = pd.read_csv(r'C:\Student\data/Copy of heart_disease_data.csv')
# print first 5 rows of the dataset
heart_data.head()
heart_data.tail()
# number of rows and columns in the dataset
heart_data.shape
# getting some info about the data
heart_data.info()
# checking for missing values
heart_data.isnull().sum()
# statistical measures about the data
heart_data.describe()
# checking the distribution of Target Variable
heart_data['target'].value_counts()
# 1 --> Defective Heart
#0 --> Healthy Heart
#Splitting the Features and Target
X = heart_data.drop(columns='target', axis=1)
Y = heart_data['target']
print(X)
print(Y)
#Splitting the Data into Training data & Test Data
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)
print(X.shape, X_train.shape, X_test.shape)
#Model Training
#Logistic Regression
model = LogisticRegression()
# training the LogisticRegression model with Training data
model.fit(X_train, Y_train)
#Model Evaluation
# Save the trained model to a pickle file
with open('heart_disease_model.pkl', 'wb') as model_file:
pickle.dump(model, model_file)
print("Model trained and saved as 'heart_disease_model.pkl'")
# Load the saved model from the pickle file
with open('heart_disease_model.pkl', 'rb') as model_file:
loaded_model = pickle.load(model_file)
#Accuracy Score
# accuracy on training data
X_train_prediction = model.predict(X_train)
training_data_accuracy = accuracy_score(X_train_prediction, Y_train)
print('Accuracy on Training data : ', training_data_accuracy)
# accuracy on test data
X_test_prediction = model.predict(X_test)
test_data_accuracy = accuracy_score(X_test_prediction, Y_test)
print('Accuracy on Test data : ', test_data_accuracy)
#Building a Predictive System
input_data = (99,1,4,300,600,1,2,500,1,7.2,0,2,5)
# change the input data to a numpy array
input_data_as_numpy_array= np.asarray(input_data)
# reshape the numpy array as we are predicting for only on instance
input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)
prediction = model.predict(input_data_reshaped)
print(prediction)
if (prediction[0]== 0):
print('The Person does not have a Heart Disease')
else:
print('The Person has Heart Disease') |