I need help finishing the code. My output is not matching the expected output.

import numpy as np import pandas as pd import statsmodels.formula.api as sms house =house = pd.read_csv("kc_house_data.csv") # load the file kc_house_data.csv

model = sms.ols(formula='price~sqft_living',data=house).fit() # fit a linear model using the sms.ols function and the house dataframe

area = float(input())

test=pd.DataFrame.from_dict({'sqft_living':[area]})

prediction =prediction =round(model.predict(test[['sqft_living']])[0],6)

print (prediction)

1.9 LAB: Making predictions using SLR models The kc_house_data dataset contains information on house sale prices in King County, Washington from May 2014 and May 2015 . The columns include sale price, number of bedrooms, and square footage of living space. Write a program using the ois ) function that creating a model that takes in the square footage of living space in a house and gives the price of the house as output. For example, if the input is: 1000 the output should be: 0237042.824803 dtype: float 64