Add prediction Model

heartdiseaseprediction.py

+# -*- coding: utf-8 -*-
+"""HeartDiseasePrediction.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1hhHh8Qb0fFC6wOQQ2LQtZaQwhztpL3Tr
+"""
+
+
+
+"""Importing Dependencies"""
+
+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
+
+
+
+"""Data Collection and Processing"""
+
+# Loading the csv data to a Pandas DataFrame
+heart_data = pd.read_csv('/content/data.csv')
+
+# Print first 5 rows in the Dataset
+heart_data.head()
+
+# Print last 5 rows in the Dataset
+heart_data.tail()
+
+# Number of rows and columns in the Dataset
+heart_data.shape
+
+# Getting some information about the Data
+heart_data.info()
+
+# Checking missing values
+heart_data.isnull().sum()
+
+# Statiscical measure about the data
+heart_data.describe()
+
+# Checking the destribution of target variable
+heart_data['target'].value_counts()
+
+
+
+"""1 --> Defective Heart
+
+0 --> Healthy Heart
+
+Splitting Features and Target
+"""
+
+x = heart_data.drop(columns = 'target' , axis = 1)
+Y = heart_data['target']
+
+print(x)
+
+print(Y)
+
+
+
+"""## Splitting 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
+"""
+
+model = LogisticRegression()
+
+# Training the LogisticRegression model with Training Data
+
+model.fit(x_train, Y_train)
+
+"""# Model Evaluation
+
+Accuracy Score
+"""
+
+# Accuracy Of 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 = (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')
+