boston_evaluation.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
RM_IDX = 4
PTRATIO_IDX = 8

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

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

# MSE & RMSE
MSE = mean_squared_error(target, regr.predict(features))
RMSE = mean_squared_error(target, regr.predict(features), squared=False)


def get_log_estimate(nr_rooms,
                     students_per_classroom,
                     next_to_river=False,
                     high_confidence=False):
    # Configure property
    property_stats[0][RM_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 + RMSE
        lower_bound = log_estimate - 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 next 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

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

    log_est, upper, lower, conf = get_log_estimate(rm, ptratio, chas, large_range)

    # Convert to todays dollars
    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 values to nearest thousand
    rounded_est = np.around(dollar_est, -3)
    rounded_hi = np.around(dollar_hi, -3)
    rounded_low = np.around(dollar_low, -3)

    print(f"The estimated property value is {rounded_est}")
    print(f"At {conf}% confidence the valuation range is")
    print(f"USD {rounded_low} at the lower end to USD {rounded_hi} at the high end.")