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d73e2fb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | # Import necessary libraries
import numpy as np
import joblib # For loading the serialized model
import pandas as pd # For data manipulation
from flask import Flask, request, jsonify # For creating the Flask API
# Initialize the Flask application
rental_price_predictor_api = Flask('Airbnb Rental Price Predictor')
# Load the trained machine learning model
model = joblib.load('rental_price_prediction_model_v1_0.joblib')
# Define a route for the home page (GET request)
@rental_price_predictor_api.get('/')
def home():
return 'Welcome to the Airbnb Rental Price Prediction API!'
# Define an endpoint for single property prediction (POST request)
@rental_price_predictor_api.post('/v1/rental')
def predict_rental_price():
property_data = request.get_json()
sample = {
'room_type': property_data['room_type'],
'accommodates': property_data['accommodates'],
'bathrooms': property_data['bathrooms'],
'cancellation_policy': property_data['cancellation_policy'],
'cleaning_fee': property_data['cleaning_fee'],
'instant_bookable': property_data['instant_bookable'],
'review_scores_rating': property_data['review_scores_rating'],
'bedrooms': property_data['bedrooms'],
'beds': property_data['beds']
}
input_data = pd.DataFrame([sample])
predicted_log_price = model.predict(input_data)[0]
predicted_price = np.exp(predicted_log_price)
predicted_price = round(float(predicted_price), 2)
return jsonify({'Predicted Price (in dollars)': predicted_price})
# Define an endpoint for batch prediction (POST request)
@rental_price_predictor_api.post('/v1/rentalbatch')
def predict_rental_price_batch():
file = request.files['file']
input_data = pd.read_csv(file)
predicted_log_prices = model.predict(input_data).tolist()
predicted_prices = [round(float(np.exp(log_price)), 2) for log_price in predicted_log_prices]
property_ids = input_data['id'].tolist()
output_dict = dict(zip(property_ids, predicted_prices))
return output_dict
# Run the Flask application
if __name__ == '__main__':
rental_price_predictor_api.run(debug=True)
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