Update app.py

app.py

@@ -7,17 +7,16 @@ from sklearn.cluster import KMeans
 # Generate the datasets
 np.random.seed(42)
 num_samples = 30
-traffic_centers = [(20, 20), (80, 80)]
-nature_centers = [(0, 80), (80, 0)]
-population_centers = [(0, 0), (50, 50), (100, 100)]
 
-traffic_data = [np.random.normal(center, 10, (num_samples, 2)) for center in traffic_centers]
-nature_data = [np.random.normal(center, 10, (num_samples, 2)) for center in nature_centers]
-population_data = [np.random.normal(center, 10, (num_samples, 2)) for center in population_centers]
+traffic_data = np.random.uniform(0, 100, (num_samples * len(traffic_centers), 2))
+nature_data = np.random.uniform(0, 100, (num_samples * len(nature_centers), 2))
+population_data = np.random.uniform(0, 100, (num_samples * len(population_centers), 2))
 
-traffic_df = pd.DataFrame(np.vstack(traffic_data), columns=["x", "y"])
-nature_df = pd.DataFrame(np.vstack(nature_data), columns=["x", "y"])
-population_df = pd.DataFrame(np.vstack(population_data), columns=["x", "y"])
+traffic_df_uniform = pd.DataFrame(traffic_data, columns=["x", "y"])
+nature_df_uniform = pd.DataFrame(nature_data, columns=["x", "y"])
+population_df_uniform = pd.DataFrame(population_data, columns=["x", "y"])
+
+traffic_df_uniform.head(), nature_df_uniform.head(), population_df_uniform.head()
 
 def apply_kmeans(data, k):
     kmeans = KMeans(n_clusters=k, random_state=42).fit(data)