Heart Disease Risk Analysis and Visualization Project in Python

Python/heartproject.py

@@ -0,0 +1,50 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+np.random.seed(111)
+
+data = {
+    'Age': np.random.randint(29, 77, 300),
+    'Cholesterol': np.random.randint(150, 300, 300),
+    'RestingBP': np.random.randint(90, 180, 300),
+    'MaxHeartRate': np.random.randint(70, 190, 300),
+    'ChestPainType': np.random.choice(['Typical', 'Atypical', 'Non-Anginal', 'Asymptomatic'], 300),
+    'Disease': np.random.choice([0, 1], 300, p=[0.45, 0.55])
+}
+
+df = pd.DataFrame(data)
+
+df['RiskScore'] = (df['Cholesterol'] * 0.5) + (df['RestingBP'] * 0.3) + (df['MaxHeartRate'] * 0.2)
+
+print("Average Cholesterol:", df['Cholesterol'].mean())
+print("Average Resting BP:", df['RestingBP'].mean())
+print("Average Heart Rate:", df['MaxHeartRate'].mean())
+
+df['AgeGroup'] = pd.cut(df['Age'], bins=[29, 40, 55, 80], labels=['Young', 'Middle', 'Old'])
+
+print("\nPatients per Age Group:")
+print(df['AgeGroup'].value_counts())
+
+plt.figure(figsize=(6,4))
+plt.hist(df['Age'], bins=10)
+plt.title("Age Distribution")
+plt.xlabel("Age")
+plt.ylabel("Count")
+plt.show()
+
+plt.figure(figsize=(6,4))
+df['ChestPainType'].value_counts().plot(kind='bar')
+plt.title("Chest Pain Type Frequency")
+plt.xlabel("Type")
+plt.ylabel("Count")
+plt.show()
+
+plt.figure(figsize=(6,4))
+df['Disease'].value_counts().plot(kind='pie', autopct='%1.1f%%')
+plt.title("Disease Ratio")
+plt.ylabel("")
+plt.show()
+
+print("\nTop 5 High Risk Patients:")
+print(df[['Age', 'Cholesterol', 'RestingBP', 'MaxHeartRate', 'RiskScore']].sort_values('RiskScore', ascending=False).head())