Data Science Project: House Prices Dataset – API

This is the 5th and final article in a series on Data Science and Machine Learning applied to a House Prices dataset from the Kaggle competition House Prices: Advanced Regression Techniques.

The first four articles were the Exploratory Data Analysis (EDA), Cleaning of the dataset, and the Machine Learning model:

• Exploratory Data Analysis – House Prices – Part 1
• Exploratory Data Analysis – House Prices – Part 2
• Data Science Project: Data Cleaning Script – House Prices DataSet
• Data Science Project: Machine Learning Model – House Prices Dataset
• Data Science Project: House Prices Dataset – API
• Data Science and Machine Learning Project: House Prices Dataset

The output of the fourth article is the Machine Learning Model (you have to unzip the file) that we are going to use in the API.

Class HousePriceModel

Save this script on a file named predict.py.

This file has the class HousePriceModel and is used to load the Machine Learning model and make the predictions.

# the pickle lib is used to load the machine learning model
import pickle
import pandas as pd

class HousePriceModel():

    def __init__(self):
        self.model = self.load_model()
        self.preds = None

    def load_model(self):
        # uses the file model.pkl
        pkl_filename = 'model.pkl'

        try:
            with open(pkl_filename, 'rb') as file:
                pickle_model = pickle.load(file)
        except:
            print(f'Error loading the model at {pkl_filename}')
            return None

        return pickle_model

    def predict(self, data):

        if not isinstance(data, pd.DataFrame):
            data = pd.DataFrame(data, index=[0])

        # makes the predictions using the loaded model
        self.preds = self.model.predict(data)
        return self.preds

The API with FastAPI

To run the API:

uvicorn api:app

Expected output:

INFO:     Started server process [56652]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)

The was API created with the framework FastAPI.

The "/predict" endpoint will give you a prediction based on a sample.

from fastapi import FastAPI
from datetime import datetime
from predict import HousePriceModel

app = FastAPI()

@app.get("/")
def root():
    return {"status": "online"}

@app.post("/predict")
def predict(inputs: dict):

    model = HousePriceModel()

    start = datetime.today()
    pred = model.predict(inputs)[0]
    dur = (datetime.today() - start).total_seconds()

    return pred

Testing the API

You can save the script on a file test_api.py and execute it directly with python3 test_api.py or python test_api.py, depending on your installation.

Remember to execute this test on a second terminal while the first one runs the server for the actual API.

Expected output:

The actual Sale Price: 109000
The predicted Sale Price: 109000.01144237864

The code to test the API:

# import requests library to make API calls
import requests
from predict import HousePriceModel

# a sample input with all the features we 
# used to train the model
sample_input = {'MSSubClass': 20, 'MSZoning': 'RL', 
'LotArea': 7922, 'Street': 'Pave', 
'LotShape': 'Reg', 'LandContour': 'Lvl', 
'Utilities': 'AllPub', 'LotConfig': 'Inside', 
'LandSlope': 'Gtl', 'Neighborhood': 'NAmes', 
'Condition1': 'Norm', 'Condition2': 'Norm', 
'BldgType': '1Fam', 'HouseStyle': '1Story', 
'OverallQual': 5, 'OverallCond': 7, 
'YearBuilt': 1953, 'YearRemodAdd': 2007, 
'RoofStyle': 'Gable', 'RoofMatl': 'CompShg', 
'Exterior1st': 'VinylSd', 'Exterior2nd': 'VinylSd', 
'MasVnrType': 'None', 'ExterQual': 3,
'ExterCond': 4, 'Foundation': 'CBlock', 
'BsmtQual': 3, 'BsmtCond': 3, 
'BsmtExposure': 'No', 'BsmtFinType1': 'GLQ', 
'BsmtFinSF1': 731, 'BsmtFinType2': 'Unf', 
'BsmtFinSF2': 0, 'BsmtUnfSF': 326, 
'TotalBsmtSF': 1057, 'Heating': 'GasA', 
'HeatingQC': 3, 'CentralAir': 'Y', 
'Electrical': 'SBrkr', '1stFlrSF': 1057, 
'2ndFlrSF': 0, 'LowQualFinSF': 0, 
'GrLivArea': 1057, 'BsmtFullBath': 1, 
'BsmtHalfBath': 0, 'FullBath': 1, 
'HalfBath': 0, 'BedroomAbvGr': 3, 
'KitchenAbvGr': 1, 'KitchenQual': 4, 
'TotRmsAbvGrd': 5, 'Functional': 'Typ', 
'Fireplaces': 0, 'FireplaceQu': 0, 
'GarageType': 'Detchd', 'GarageFinish': 'Unf',
'GarageCars': 1, 'GarageArea': 246, 
'GarageQual': 3, 'GarageCond': 3, 
'PavedDrive': 'Y', 'WoodDeckSF': 0, 
'OpenPorchSF': 52, 'EnclosedPorch': 0, 
'3SsnPorch': 0, 'ScreenPorch': 0, 
'PoolArea': 0, 'MiscVal': 0, 'MoSold': 1,
'YrSold': 2010, 'SaleType': 'WD', 
'SaleCondition': 'Abnorml'}

def run_prediction_from_sample():

    url="http://127.0.0.1:8000/predict"
    headers = {"Content-Type": "application/json", \
    "Accept":"text/plain"}

    response = requests.post(url, headers=headers, \
    json=sample_input)
    print("The actual Sale Price: 109000")
    print(f"The predicted Sale Price: {response.text}")

if __name__ == "__main__":
    run_prediction_from_sample()