Update pages/1_User_Defined_DataLab.py

pages/1_User_Defined_DataLab.py

@@ -14,14 +14,26 @@ st.set_page_config(layout="wide")
 st.title("🧠 Neural Network Playground - Custom Dataset")
 
 # Generate synthetic 2-feature dataset
-n_samples = st.slider("Number of Samples", 100, 1000, 300)
-noise = st.slider("Noise Level", 0.0, 1.0, 0.2)
-random_state = st.number_input("Random State", value=42)
-n_clusters_per_class=st.slider("Number of Clusters",1,4,1)
-
-X, y = make_classification(n_samples=n_samples, n_features=2, n_redundant=0,
-                           n_informative=2, n_clusters_per_class=n_clusters_per_class,
-                           flip_y=noise, random_state=random_state, class_sep=4)
+from sklearn.datasets import make_classification, make_circles, make_moons
+
+# Dataset selection
+st.sidebar.title("📦 Dataset Settings")
+dataset_type = st.sidebar.selectbox("Choose Dataset Type", ["Linear (make_classification)", "Circles (make_circles)", "Moons (make_moons)"])
+
+n_samples = st.sidebar.slider("Number of Samples", 100, 1000, 300)
+noise = st.sidebar.slider("Noise Level", 0.0, 1.0, 0.2)
+random_state = st.sidebar.number_input("Random State", value=42)
+
+if dataset_type == "Linear (make_classification)":
+    n_clusters_per_class = st.sidebar.slider("Number of Clusters", 1, 4, 1)
+    X, y = make_classification(n_samples=n_samples, n_features=2, n_redundant=0,
+                               n_informative=2, n_clusters_per_class=n_clusters_per_class,
+                               flip_y=noise, random_state=random_state, class_sep=4)
+elif dataset_type == "Circles (make_circles)":
+    X, y = make_circles(n_samples=n_samples, noise=noise, factor=0.5, random_state=random_state)
+elif dataset_type == "Moons (make_moons)":
+    X, y = make_moons(n_samples=n_samples, noise=noise, random_state=random_state)
+
 
 df = pd.DataFrame(X, columns=["X1", "X2"])
 df["label"] = y