From a2be1c0c33b5073610773b03b4084d7e9513e8ea Mon Sep 17 00:00:00 2001
From: Sameer Aftab <53132294+Sameer-aftab@users.noreply.github.com>
Date: Mon, 20 Oct 2025 07:29:32 +0500
Subject: [PATCH] Add anomaly detection for health data using Isolation Forest

This script simulates health data, trains an anomaly detection model using Isolation Forest, and visualizes the results.
---
 Machine Learning/Anomaly.py | 32 ++++++++++++++++++++++++++++++++
 1 file changed, 32 insertions(+)
 create mode 100644 Machine Learning/Anomaly.py

diff --git a/Machine Learning/Anomaly.py b/Machine Learning/Anomaly.py
new file mode 100644
index 0000000..4f174de
--- /dev/null
+++ b/Machine Learning/Anomaly.py	
@@ -0,0 +1,32 @@
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import IsolationForest
+import matplotlib.pyplot as plt
+
+# Step 1: Simulate health data
+np.random.seed(42)
+heart_rate = np.random.normal(75, 5, 200)  # Normal HR range
+oxygen = np.random.normal(97, 1.5, 200)    # Normal O2 range
+
+# Add a few anomalies
+heart_rate[195:] = [110, 120, 125, 130, 115]
+oxygen[195:] = [85, 88, 90, 87, 89]
+
+data = pd.DataFrame({"heart_rate": heart_rate, "oxygen": oxygen})
+
+# Step 2: Train anomaly detection model
+model = IsolationForest(contamination=0.05)
+model.fit(data)
+data["anomaly"] = model.predict(data)
+
+# Step 3: Visualize
+normal = data[data["anomaly"] == 1]
+anomaly = data[data["anomaly"] == -1]
+
+plt.scatter(normal["heart_rate"], normal["oxygen"], label="Normal", color="blue")
+plt.scatter(anomaly["heart_rate"], anomaly["oxygen"], label="Anomaly", color="red")
+plt.xlabel("Heart Rate (BPM)")
+plt.ylabel("Oxygen Level (%)")
+plt.legend()
+plt.title("Health Data Anomaly Detection")
+plt.show()