Practice: Simple Linear Regression¶

Using the LinearRegression class to find a relationship between the Horse Power of a car and its retail price.¶

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
In [2]:
df = pd.read_csv('CarDataset.csv')
In [3]:
df.head()
Out[3]:
Make Model Year Engine Fuel Type Engine HP Engine Cylinders Transmission Type Driven_Wheels Number of Doors Market Category Vehicle Size Vehicle Style highway MPG city mpg Popularity MSRP
0 BMW 1 Series M 2011 premium unleaded (required) 335.0 6.0 MANUAL rear wheel drive 2.0 Factory Tuner,Luxury,High-Performance Compact Coupe 26 19 3916 46135
1 BMW 1 Series 2011 premium unleaded (required) 300.0 6.0 MANUAL rear wheel drive 2.0 Luxury,Performance Compact Convertible 28 19 3916 40650
2 BMW 1 Series 2011 premium unleaded (required) 300.0 6.0 MANUAL rear wheel drive 2.0 Luxury,High-Performance Compact Coupe 28 20 3916 36350
3 BMW 1 Series 2011 premium unleaded (required) 230.0 6.0 MANUAL rear wheel drive 2.0 Luxury,Performance Compact Coupe 28 18 3916 29450
4 BMW 1 Series 2011 premium unleaded (required) 230.0 6.0 MANUAL rear wheel drive 2.0 Luxury Compact Convertible 28 18 3916 34500
In [4]:
df.dtypes
Out[4]:
Make                  object
Model                 object
Year                   int64
Engine Fuel Type      object
Engine HP            float64
Engine Cylinders     float64
Transmission Type     object
Driven_Wheels         object
Number of Doors      float64
Market Category       object
Vehicle Size          object
Vehicle Style         object
highway MPG            int64
city mpg               int64
Popularity             int64
MSRP                   int64
dtype: object

Remove NaN rows¶

In [5]:
df.dropna(inplace=True)

Create an Array of Horse Power Values¶

In [6]:
HP = df['Engine HP']
In [7]:
HP_array = HP.to_numpy()
In [8]:
HP_array = HP_array.reshape(-1, 1)

Create an Array of Price of Car Values¶

In [9]:
Price = df['MSRP']
In [10]:
Price.dropna(inplace=True)
In [11]:
Price_array = Price.to_numpy()
In [12]:
Price_array = Price_array.reshape(-1, 1)

Plot HP Vs. Price¶

In [13]:
plt.plot(HP_array,Price_array,'o')
plt.axis([0,800,0,100000])
plt.ylabel('''Retail Price (£)''')
plt.xlabel('Engine: Horse Power')
plt.show()

Fit a Linear Regression Model to This Data¶

In [14]:
line_fitter = LinearRegression()
In [15]:
line_fitter.fit(HP_array, Price_array)
Out[15]:
LinearRegression()
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LinearRegression()
In [16]:
PricePredict = line_fitter.predict(HP_array)
plt.plot(HP_array,Price_array,'o')
plt.plot(HP_array, PricePredict)
plt.axis([0,800,0,100000])
plt.ylabel('''Retail Price (£)''')
plt.xlabel('Engine: Horse Power')
plt.show()

Equation of the Line¶

In [17]:
gradient = line_fitter.coef_[0]
y_intercept = line_fitter.intercept_

print("Gradient:", gradient)
print("Y-Intercept:", y_intercept)

print("Cost of Car = ", gradient, " * Horsepower + ", y_intercept)

Gradient: [401.36907559]
Y-Intercept: [-60160.43766107]
Cost of Car =  [401.36907559]  * Horsepower +  [-60160.43766107]
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