feat: house price prediction model integration

backend/app/ai_models/california.py

@@ -0,0 +1,33 @@
+from sklearn.datasets import fetch_california_housing
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+import joblib
+import pandas as pd
+
+# Load dataset
+data = fetch_california_housing()
+df = pd.DataFrame(data.data, columns=data.feature_names)
+df['target'] = data.target  # in 100k USD
+
+# Engineer features
+df['square_feet'] = df['AveRooms'] * 350  
+df['bedrooms'] = df['AveBedrms']
+df['bathrooms'] = df['AveRooms'] * 0.2 
+
+# Clean bathrooms
+df['bathrooms'] = df['bathrooms'].clip(lower=1)
+
+X = df[['square_feet', 'bedrooms', 'bathrooms']]
+y = df['target']
+
+# Train/test split
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
+
+# Train model
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# Need to be tested of course..: )
+
+# Save model
+joblib.dump(model, 'price_predictor.pkl')