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import pickle
import numpy as np
def load_model_and_features(model_path, feature_path):
"""
Load the trained model and feature names from pickle files.
Args:
model_path (str): Path to the trained model pickle file.
feature_path (str): Path to the feature names pickle file.
Returns:
tuple: (trained model, feature names)
"""
# Load the trained model
with open(model_path, "rb") as file:
model = pickle.load(file)
# Load the feature names
with open(feature_path, "rb") as file:
feature_names = pickle.load(file)
return model, feature_names
def predict_price(location, sqft, bath, bhk, model, feature_names):
"""
Predict the price using the trained model.
Args:
location (str): Location name.
sqft (float): Total square footage.
bath (int): Number of bathrooms.
bhk (int): Number of bedrooms.
model: Trained model object.
feature_names (list): List of feature names.
Returns:
float: Predicted price.
"""
# Create an input array with zeros for all features
x = np.zeros(len(feature_names))
# Assign values for sqft, bath, and bhk
if 'total_sqft' in feature_names:
x[feature_names.index('total_sqft')] = sqft
if 'bath' in feature_names:
x[feature_names.index('bath')] = bath
if 'bhk' in feature_names:
x[feature_names.index('bhk')] = bhk
# Set the location column to 1 if it exists in feature names
if location in feature_names:
loc_index = feature_names.index(location)
x[loc_index] = 1
# Make prediction
return model.predict([x])[0]
def main():
# Paths to the model and feature names
model_path = "models/lr_regg.pkl"
feature_path = "models/feature_names.pkl"
# Load the model and features
model, feature_names = load_model_and_features(model_path, feature_path)
# Test cases
test_cases = [
{"location": "Whitefield", "sqft": 1200, "bath": 2, "bhk": 2},
{"location": "Banaswadi", "sqft": 1500, "bath": 3, "bhk": 3},
{"location": "Basavangudi", "sqft": 1800, "bath": 3, "bhk": 4},
{"location": "Nonexistent Location", "sqft": 1000, "bath": 2, "bhk": 3},
{"location": "Electronic City Phase II", "sqft": 1056, "bath": 2, "bhk": 2},
{"location": "Chikka Tirupathi", "sqft": 800, "bath": 2, "bhk": 2}
]
print("\nPredictions:")
for case in test_cases:
location = case["location"]
sqft = case["sqft"]
bath = case["bath"]
bhk = case["bhk"]
try:
predicted_price = predict_price(location, sqft, bath, bhk, model, feature_names)
print(f"Location: {location}, Sqft: {sqft}, Bath: {bath}, BHK: {bhk} -> Predicted Price: {predicted_price/10:.0f} lakhs")
except Exception as e:
print(f"Prediction failed for Location: {location}, Error: {e}")
if __name__ == "__main__":
main() |