Program:
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
# loading the csv data to a Pandas DataFrame
heart_data = pd.read_csv('/content/data.csv')
# print first 5 rows of the dataset
heart_data.head()
# print last 5 rows of the dataset
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()
X = heart_data.drop(columns='target', axis=1)
Y = heart_data['target']
print(X)
print(Y)
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 = LogisticRegression()
# training the LogisticRegression model with Training data
model.fit(X_train, Y_train)
# 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)
input_data = (62,0,0,140,268,0,0,160,0,3.6,0,2,2)
# 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')
Output:
(303, 14)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 303 entries, 0 to 302
Data columns (total 14 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 age 303 non-null int64
1 sex 303 non-null int64
2 cp 303 non-null int64
3 trestbps 303 non-null int64
4 chol 303 non-null int64
5 fbs 303 non-null int64
6 restecg 303 non-null int64
7 thalach 303 non-null int64
8 exang 303 non-null int64
9 oldpeak 303 non-null float64
10 slope 303 non-null int64
11 ca 303 non-null int64
12 thal 303 non-null int64
13 target 303 non-null int64
dtypes: float64(1), int64(13)
memory usage: 33.3 KB
age 0
sex 0
cp 0
trestbps 0
chol 0
fbs 0
restecg 0
thalach 0
exang 0
oldpeak 0
slope 0
ca 0
thal 0
target 0
dtype: int64
age sex cp trestbps chol ... exang oldpeak slope ca thal
0 63 1 3 145 233 ... 0 2.3 0 0 1
1 37 1 2 130 250 ... 0 3.5 0 0 2
2 41 0 1 130 204 ... 0 1.4 2 0 2
3 56 1 1 120 236 ... 0 0.8 2 0 2
4 57 0 0 120 354 ... 1 0.6 2 0 2
.. ... ... .. ... ... ... ... ... ... .. ...
298 57 0 0 140 241 ... 1 0.2 1 0 3
299 45 1 3 110 264 ... 0 1.2 1 0 3
300 68 1 0 144 193 ... 0 3.4 1 2 3
301 57 1 0 130 131 ... 1 1.2 1 1 3
302 57 0 1 130 236 ... 0 0.0 1 1 2
[303 rows x 13 columns]
0 1
1 1
2 1
3 1
4 1
..
298 0
299 0
300 0
301 0
302 0
Name: target, Length: 303, dtype: int64
account_circle
(303, 13) (242, 13) (61, 13)
Accuracy on Training data : 0.8512396694214877
Accuracy on Test data : 0.819672131147541
[0]
The Person does not have a Heart Disease
