diff --git a/models/knn.py b/models/knn.py
new file mode 100644
index 0000000..c8db9b1
--- /dev/null
+++ b/models/knn.py
@@ -0,0 +1,29 @@
+from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
+import numpy as np
+
+class KNNModel:
+    def __init__(self, task_type="classification", k=5):
+        self.task_type = task_type
+        self.k = k
+        self.model = None
+
+    def initialize_model(self):
+        """Initialize KNN model based on task type."""
+        if self.task_type == "classification":
+            self.model = KNeighborsClassifier(n_neighbors=self.k)
+        else:
+            self.model = KNeighborsRegressor(n_neighbors=self.k)
+
+    def train(self, X, y):
+        """Train KNN model."""
+        if self.model is None:
+            self.initialize_model()
+        self.model.fit(X, y)
+
+    def predict(self, X):
+        """Make predictions."""
+        return self.model.predict(X)
+
+    def score(self, X, y):
+        """Return accuracy (for classification) or R² (for regression)."""
+        return self.model.score(X, y)
diff --git a/pages/KNN.py b/pages/KNN.py
new file mode 100644
index 0000000..3a98c96
--- /dev/null
+++ b/pages/KNN.py
@@ -0,0 +1,46 @@
+import streamlit as st
+from models.knn import KNNModel
+from utils.data_helpers import generate_classification_data, generate_sample_regression_data
+from utils.plot_helpers import plot_classification_results, plot_regression_results
+import pandas as pd
+
+st.set_page_config(page_title="KNN Model", layout="wide")
+
+st.title("🧠 K-Nearest Neighbors (KNN) Simulator")
+
+# --- Sidebar Controls ---
+st.sidebar.header("⚙️ Configuration")
+task_type = st.sidebar.selectbox("Select Task Type", ["classification", "regression"])
+k_value = st.sidebar.slider("Select k (number of neighbors)", min_value=1, max_value=20, value=5, step=1)
+n_samples = st.sidebar.slider("Number of samples", 50, 500, 200)
+n_features = st.sidebar.slider("Number of features", 2, 10, 2)
+
+# --- Generate Dataset ---
+st.subheader("📊 Dataset Preview")
+
+if task_type == "classification":
+    X, y = generate_classification_data(n_samples=n_samples, n_features=n_features)
+else:
+    data = generate_sample_regression_data(n_samples=n_samples, n_features=n_features)
+    X, y = data.iloc[:, :-1], data.iloc[:, -1]
+
+df = pd.DataFrame(X)
+df["target"] = y
+st.dataframe(df.head())
+
+# --- Train Model ---
+model = KNNModel(task_type=task_type, k=k_value)
+model.train(X, y)
+score = model.score(X, y)
+
+# --- Display Results ---
+st.success(f"✅ Model trained successfully with k = {k_value}")
+st.metric("Model Score", f"{score:.3f}")
+
+# --- Visualizations ---
+st.subheader("📈 Visualization")
+
+if task_type == "classification":
+    st.pyplot(plot_classification_results(X, y, model))
+else:
+    st.pyplot(plot_regression_results(X, y, model))