9. rebuild.py

import codecademylib3_seaborn
import matplotlib.pyplot as plt
import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

streeteasy = pd.read_csv("https://raw.githubusercontent.com/sonnynomnom/Codecademy-Machine-Learning-Fundamentals/master/StreetEasy/manhattan.csv")

df = pd.DataFrame(streeteasy)

x = df[['bedrooms', 'bathrooms', 'size_sqft', 'min_to_subway', 'floor', 'building_age_yrs', 'no_fee', 'has_roofdeck', 'has_washer_dryer', 'has_doorman', 'has_elevator', 'has_dishwasher', 'has_patio', 'has_gym']]

y = df[['rent']]

x_train, x_test, y_train, y_test = train_test_split(x, y, train_size = 0.8, test_size = 0.2, random_state=6)

lm = LinearRegression()

model = lm.fit(x_train, y_train)

y_predict= lm.predict(x_test)

print("Train score:")
print(lm.score(x_train, y_train))

print("Test score:")
print(lm.score(x_test, y_test))

plt.scatter(y_test, y_predict)
plt.plot(range(20000), range(20000))

plt.xlabel("Prices: $Y_i$")
plt.ylabel("Predicted prices: $\hat{Y}_i$")
plt.title("Actual Rent vs Predicted Rent")

plt.show()

# zoe_apartment = [[1, 1, 620, 16, 1, 98, 0, 0, 1, 0, 0, 0, 1, 0]]
# predict = model.predict(zoe_apartment)
# print("Predicted rent: $%.2f" % predict)