Update app.py

app.py

@@ -1,73 +1,73 @@
-import streamlit as st
-import numpy as np
-import matplotlib.pyplot as plt
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.metrics import accuracy_score,f1_score
-from sklearn.model_selection import train_test_split
-from sklearn.datasets import make_classification, make_circles, make_blobs, make_moons
-from mlxtend.plotting import plot_decision_regions, plot_learning_curves
-
-# Streamlit UI
-st.set_page_config(page_title="KNN Classifier App", layout="wide")
-st.title("🔍 KNN Classifier Interactive App")
-st.write("Select a dataset, modify parameters, and tune KNN hyperparameters.")
-
-# Sidebar options
-dataset_choice = st.sidebar.selectbox("📂 Select a Dataset", ["Classification", "Circles", "Blobs", "Moons"])
-
-# Dataset parameters
-if dataset_choice == "Classification":
-    n_samples = st.sidebar.slider("Samples", 1000, 5000, 2000)
-    class_sep = st.sidebar.slider("Class Separation", 0.5, 5.0, 1.0)
-    X, y = make_classification(n_samples=n_samples, n_features=2, n_redundant=0, 
-                               n_clusters_per_class=1, class_sep=class_sep, random_state=23)
-
-elif dataset_choice == "Circles":
-    n_samples = st.sidebar.slider("Samples", 500, 5000, 2000)
-    noise = st.sidebar.slider("Noise", 0.0, 0.5, 0.2)
-    factor = st.sidebar.slider("Factor", 0.1, 0.9, 0.2)
-    X, y = make_circles(n_samples=n_samples, factor=factor, noise=noise, random_state=23)
-
-elif dataset_choice == "Blobs":
-    n_samples = st.sidebar.slider("Samples", 500, 5000, 2000)
-    clusters = st.sidebar.slider("Clusters", 2, 5, 3)
-    X, y = make_blobs(n_samples=n_samples, centers=clusters, n_features=2, random_state=23)
-
-else:  # Moons
-    n_samples = st.sidebar.slider("Samples", 500, 5000, 2000)
-    noise = st.sidebar.slider("Noise", 0.0, 0.5, 0.2)
-    X, y = make_moons(n_samples=n_samples, noise=noise, random_state=23)
-
-# Split dataset
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=23)
-
-# KNN Parameters
-st.sidebar.subheader("⚙️ KNN Parameters")
-n_neighbors = st.sidebar.slider("Neighbors (k)", 1, 15, 3)
-weights = st.sidebar.selectbox("Weights", ["uniform", "distance"])
-p = st.sidebar.selectbox("p (Minkowski)", [1, 2])
-
-# Train KNN
-knn = KNeighborsClassifier(n_neighbors=n_neighbors, weights=weights, p=p, metric='minkowski')
-knn.fit(X_train, y_train)
-y_pred = knn.predict(X_test)
-accuracy = accuracy_score(y_test, y_pred)
-f1=f1_score(y_test,y_pred)
-
-# Display results
-st.subheader("📊 Model Accuracy")
-st.write(f"**Accuracy Score: {accuracy:.4f}**")
-
-st.subheader("📊 Model F1-Score")
-st.write(f"f1 Score: {f1:.4f}")
-
-fig, ax = plt.subplots(figsize=(6, 4))
-plot_learning_curves(X_train, y_train, X_test, y_test, knn, scoring='accuracy')
-st.subheader("📈 Learning Curve")
-st.pyplot(fig)
-
-fig, ax = plt.subplots(figsize=(6, 4))
-knn.fit(X, y)
-plot_decision_regions(X, y, knn)
-st.subheader("🔷 Decision Boundary")
-st.pyplot(fig)
+import streamlit as st
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import accuracy_score,f1_score
+from sklearn.model_selection import train_test_split
+from sklearn.datasets import make_classification, make_circles, make_blobs, make_moons
+from mlxtend.plotting import plot_decision_regions, plot_learning_curves
+
+# Streamlit UI
+st.set_page_config(page_title="KNN Classifier App", layout="wide")
+st.title("🔍 KNN Classifier Interactive App")
+st.write("Select a dataset, modify parameters, and tune KNN hyperparameters.")
+
+# Sidebar options
+dataset_choice = st.sidebar.selectbox("📂 Select a Dataset", ["Classification", "Circles", "Blobs", "Moons"])
+
+# Dataset parameters
+if dataset_choice == "Classification":
+    n_samples = st.sidebar.slider("Samples", 1000, 5000, 2000)
+    class_sep = st.sidebar.slider("Class Separation", 0.5, 5.0, 1.0)
+    X, y = make_classification(n_samples=n_samples, n_features=2, n_redundant=0, 
+                               n_clusters_per_class=1, class_sep=class_sep, random_state=23)
+
+elif dataset_choice == "Circles":
+    n_samples = st.sidebar.slider("Samples", 500, 5000, 2000)
+    noise = st.sidebar.slider("Noise", 0.0, 0.5, 0.2)
+    factor = st.sidebar.slider("Factor", 0.1, 0.9, 0.2)
+    X, y = make_circles(n_samples=n_samples, factor=factor, noise=noise, random_state=23)
+
+elif dataset_choice == "Blobs":
+    n_samples = st.sidebar.slider("Samples", 500, 5000, 2000)
+    clusters = st.sidebar.slider("Clusters", 2, 5, 3)
+    X, y = make_blobs(n_samples=n_samples, centers=clusters, n_features=2, random_state=23)
+
+else:  # Moons
+    n_samples = st.sidebar.slider("Samples", 500, 5000, 2000)
+    noise = st.sidebar.slider("Noise", 0.0, 0.5, 0.2)
+    X, y = make_moons(n_samples=n_samples, noise=noise, random_state=23)
+
+# Split dataset
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=23)
+
+# KNN Parameters
+st.sidebar.subheader("⚙️ KNN Parameters")
+n_neighbors = st.sidebar.slider("Neighbors (k)", 1, 15, 3)
+weights = st.sidebar.selectbox("Weights", ["uniform", "distance"])
+p = st.sidebar.selectbox("p (Minkowski)", [1, 2])
+
+# Train KNN
+knn = KNeighborsClassifier(n_neighbors=n_neighbors, weights=weights, p=p, metric='minkowski')
+knn.fit(X_train, y_train)
+y_pred = knn.predict(X_test)
+accuracy = accuracy_score(y_test, y_pred)
+f1=f1_score(y_test,y_pred)
+
+# Display results
+st.subheader("📊 Model Accuracy")
+st.write(f"**Accuracy Score: {accuracy:.4f}**")
+
+st.subheader("📊 Model F1-Score")
+st.write(f"**F1 Score: {f1:.4f}**")
+
+fig, ax = plt.subplots(figsize=(6, 4))
+plot_learning_curves(X_train, y_train, X_test, y_test, knn, scoring='accuracy')
+st.subheader("📈 Learning Curve")
+st.pyplot(fig)
+
+fig, ax = plt.subplots(figsize=(6, 4))
+knn.fit(X, y)
+plot_decision_regions(X, y, knn)
+st.subheader("🔷 Decision Boundary")
+st.pyplot(fig)