Multiple Linear Regression¶

Practice of Multiple Linear Regression using house rentals data in Manhattan, NY¶

Import Libraries, File and Inspect Data¶

In [1]:
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

import warnings
warnings.filterwarnings('ignore')
In [2]:
df = pd.read_csv('ManhattanHousing.csv')
In [3]:
df.head()
Out[3]:
rental_id rent 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 neighborhood borough
0 1545 2550 0.0 1 480 9 2.0 17 1 1 0 0 1 1 0 1 Upper East Side Manhattan
1 2472 11500 2.0 2 2000 4 1.0 96 0 0 0 0 0 0 0 0 Greenwich Village Manhattan
2 2919 4500 1.0 1 916 2 51.0 29 0 1 0 1 1 1 0 0 Midtown Manhattan
3 2790 4795 1.0 1 975 3 8.0 31 0 0 0 1 1 1 0 1 Greenwich Village Manhattan
4 3946 17500 2.0 2 4800 3 4.0 136 0 0 0 1 1 1 0 1 Soho Manhattan

Set up training and test data¶

In [4]:
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)

Create and fit a Linear Regression model¶

In [5]:
mlr = LinearRegression()

mlr.fit(x_train, y_train) 

y_predict = mlr.predict(x_test)

Compare the actual rent prices to the ones predicted by the model¶

In [6]:
plt.scatter(y_test, y_predict, alpha=0.4)
plt.plot(y_test,y_test, alpha = 0.3)

plt.xlabel("Actual Rent Prices")
plt.ylabel("Predicted Rent Prices")

plt.title("Comparing the actual rent with the predicted rent using Mutliple Linear Regression")

plt.show()

Inspecting the coefficients of the features¶

In [7]:
print(x.columns)
print(mlr.coef_)

Index(['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'],
      dtype='object')
[[-302.73009383 1199.3859951     4.79976742  -24.28993151   24.19824177
    -7.58272473 -140.90664773   48.85017415  191.4257324  -151.11453388
    89.408889    -57.89714551  -19.31948556  -38.92369828]]

Seeing how some individual features correlate to the rental price¶

In [8]:
plt.scatter(df[['size_sqft']], df[['rent']], alpha=0.4)
plt.xlabel("Size - sq. feet")
plt.ylabel("Rental Prices")
plt.show
Out[8]:
<function matplotlib.pyplot.show(close=None, block=None)>
In [9]:
plt.scatter(df[['floor']], df[['rent']], alpha=0.4)
plt.xlabel("Floor Level of Flat")
plt.ylabel("Rental Prices")
plt.show
Out[9]:
<function matplotlib.pyplot.show(close=None, block=None)>

Checking the Correlation Coefficient (it's above 0.7, so a good fit)¶

In [10]:
print("Train score:")
print(mlr.score(x_train, y_train))

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

Train score:
0.7725460559817883
Test score:
0.8050371975357635

Calculate a good rental price to charge for my flat¶

In [12]:
JimsFlat = [[2, 1, 620, 16, 1, 98, 1, 0, 1, 0, 0, 1, 1, 0]]

predict = mlr.predict(JimsFlat)

print("You could charge a rental price of: $%.2f" % predict)

You could charge a rental price of: $2090.85
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