first commit.can input rating and see vet clinics.

IT19980782(001).py

+import pandas as pd
+from sklearn.cluster import KMeans
+from sklearn.preprocessing import StandardScaler
+
+# Step 1: Collect data (example data)
+#data = {
+ #   'Clinic': ['Clinic A', 'Clinic B', 'Clinic C', 'Clinic D', 'Clinic E'],
+  #  'Location': [1, 3, 2, 1, 3],  # Example feature: Location (1=City, 2=Suburb, 3=Rural)
+   # 'Rating': [4.5, 3.8, 4.2, 4.9, 3.6]  # Example feature: Rating (1-5 scale)
+#}
+mydf = pd.read_csv('vetClinics.csv',encoding='cp1252')
+df=mydf.dropna()
+#df = pd.DataFrame(data)
+
+# Step 2: Preprocess the data
+features = [ 'rating']
+df_features = df[features]  # Selecting relevant features
+
+# Standardize the features
+scaler = StandardScaler()
+df_features_scaled = scaler.fit_transform(df_features)
+
+# Step 3: Apply K-means clustering
+k = 2  # Number of clusters
+kmeans = KMeans(n_clusters=k, random_state=42)
+kmeans.fit(df_features_scaled)
+
+# Step 4: Evaluate and interpret the clusters
+df['Cluster'] = kmeans.labels_
+
+# Step 5: Make recommendations
+#user_location = int(input("Enter your location (1=City, 2=Suburb, 3=Rural): "))
+user_rating = float(input("Enter your preferred rating (1-5 scale): "))
+
+# Scale the user input features
+user_input = scaler.transform([[ user_rating]])
+
+# Assign the user input to a cluster
+user_cluster = kmeans.predict(user_input)[0]
+
+# Find the unique best clinics in the user's cluster
+best_clinics = set(df[df['Cluster'] == user_cluster]['veterinary clinic name'])
+
+print("The suggested vet clinic(s) based on your input are:")
+for clinic in best_clinics:
+    print(clinic)
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