Property Price Prediction API Documentation

Table of Contents

Overview
Endpoints
- 1. Predict Property Price
How to Run
Error Handling

Overview

This API is designed to predict property prices based on geographical coordinates. It utilizes a decision tree model to estimate property prices per square meter at a given latitude and longitude.

Endpoints

1. Predict Property Price

URL: /predict_get
Method: GET
Query Params:
- latitude=[float] (required)
- longitude=[float] (required)
Success Response: JSON object with the predicted price per square meter.
Error Response:
- 400 Bad Request if query parameters are missing or invalid
- 503 Service Unavailable if the model is not loaded
- 500 Internal Server Error for other issues
Example: http://localhost:5000/predict_get?latitude=48.8566&longitude=2.3522

How to Run

Ensure FastAPI, Uvicorn, and necessary dependencies are installed.
Load the decision tree model named decision_tree_model.pkl.
Run the server using uvicorn main:app --host=localhost --port=5000, with main.py being the script containing the FastAPI app.
Access the API at http://localhost:5000.

Error Handling

Custom error handling is implemented to manage internal server errors.
The server returns appropriate error messages and HTTP status codes based on the nature of the error encountered.
The API checks for the model's availability before making predictions, ensuring reliable responses.