boston_valuation.py

from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

import pandas as pd
import numpy as np

# Gather Data
boston_dataset = load_boston()
data = pd.DataFrame(data=boston_dataset.data, 
                    columns=boston_dataset.feature_names)
features = data.drop(['INDUS', 'AGE'],axis=1)

log_prices = np.log(boston_dataset.target)
target = pd.DataFrame(log_prices, columns=['PRICE'])

CRIME_IDX = 0
ZN_IDX = 1
CHAS_IDX = 2
RN_IDX = 4
PTRATIO_IDX = 8

ZILLOW_MEDIAN_PRICE = 583.3
SCALE_FACTOR = ZILLOW_MEDIAN_PRICE/np.median(boston_dataset.target)

property_stats = features.mean().values.reshape(1,11)

regr = LinearRegression().fit(features, target)
fitted_vals = regr.predict(features)

# Calculate the MSE and RMSE using sklearn
MSE = mean_squared_error(target, fitted_vals)
RMSE = np.sqrt(MSE)

def get_log_estimate(nr_rooms,
                    students_per_classroom,
                    next_to_river=False,
                    high_confidence=True):
    
    # Configure property
    property_stats[0][RN_IDX] = nr_rooms
    property_stats[0][PTRATIO_IDX] = students_per_classroom
    
    if next_to_river:
        property_stats[0][CHAS_IDX] = 1
    else:
        property_stats[0][CHAS_IDX] = 0
    
    # make prediction
    log_estimate = regr.predict(property_stats)[0][0]
    
    # Calc Range
    if high_confidence:
        upper_bound = log_estimate + 2*RMSE
        lower_bound = log_estimate - 2*RMSE
        interval = 95
    else:
        upper_bound = log_estimate + 1*RMSE
        lower_bound = log_estimate - 1*RMSE
        interval = 68
    
    return log_estimate, upper_bound, lower_bound, interval

def get_dollar_estimate(rm, ptratio, chas=False, large_range=True):
    """Estimate the price of a property in Boston
    
    Keyword arguments:
    rm -- number of rooms in the property
    ptratio -- number of students per teacher in the classroom for the school in the area
    chas -- True if the property is nexet to the river; false otherwise
    large_range -- True for a 95% prediction interval, False for a 68% interval
    """
    if rm < 1 or ptratio <1:
        print('That is unrealistic. Try again')
        return
    
    log_est, upper, lower, conf = get_log_estimate(rm, students_per_classroom=ptratio,
                                              next_to_river=chas,
                                              high_confidence=large_range)
    # convert to today's $$
    dollar_est = np.e**log_est*1000*SCALE_FACTOR
    dollar_hi = np.e**upper*1000*SCALE_FACTOR
    dollar_low = np.e**lower*1000*SCALE_FACTOR


    # round the dollar value to the neareset $000
    rounded_est = np.around(dollar_est, -3)
    rounded_high = np.around(dollar_hi, -3)
    rounded_low = np.around(dollar_low, -3)

    print(f'The estimated propery value is {rounded_est}.')
    print(f'At {conf}% range the valuation range is:')
    print(f'USD {rounded_low} at the lower end and {rounded_high} at the higher end')