Auto MPG Dataset

 

🚗 Auto MPG Dataset

🔹 Dataset Overview

FeatureDescription
Dataset Name    Auto MPG Dataset
Domain    Regression / Predictive Modeling
Number of Instances    398
Number of Attributes    8 (including target)
Task    Predict fuel efficiency (MPG)

📖 Context

The Auto MPG dataset is a classic dataset used in machine learning to predict a car’s fuel efficiency.

  • Collected from 1970s–1980s automobiles

  • Widely used for:

    • Regression tasks

    • Feature analysis

    • Model comparison

🎯 Target Variable

  • mpg (Miles Per Gallon)
    👉 Represents fuel efficiency
    👉 Higher value = more fuel-efficient car

📊 Features (Attributes)

No.AttributeDescription
1mpg 🎯        Fuel efficiency (target variable)
2cylinders        Number of engine cylinders
3displacement        Engine size
4horsepower        Engine power
5weight        Vehicle weight
6acceleration        Time to accelerate (0–60 mph approx.)
7model_year        Year of manufacture
8origin        Country of origin (1=USA, 2=Europe, 3=Asia)
9car_name        Name of the car (categorical/text)

⚠️ Data Characteristics

  • Some values (especially horsepower) may contain missing values (?)

  • car_name is textual → usually dropped or encoded

  • origin is categorical (numeric-coded)

🧹 Preprocessing Requirements

Typical steps:

  • Handle missing values (e.g., replace ? in horsepower)

  • Convert data types (string → numeric)

  • Drop irrelevant columns (car_name)

  • Encode categorical variables if needed

  • Feature scaling (optional)

📈 Example Use Cases

  • Predict fuel efficiency (MPG)

  • Analyze:

    • Effect of weight on mileage

    • Impact of horsepower

  • Compare regression models

📥 Dataset Source

Commonly available from:

  • UCI Machine Learning Repository

  • Kaggle

  • Scikit-learn datasets (via seaborn) 

                import seaborn as sns
                df = sns.load_dataset('mpg')

Download  auto-mpg.csv
Upload the file to colab
Run the python code below and understand the data file

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


# Reading the data file
df = pd.read_csv("/content/auto-mpg.csv")
#print the datframe
print(df)
#printing the shape
print(df.shape)
#printing top 10 and bottom 10 rows
print(df.head(10))
print(df.tail(10))
#printing the columns
print(df.columns)
#print a feature value
print(df['mpg'])
# Selecting one input feature and target feature (dependent) for 150 rows only
X = df['displacement'].values.reshape(-1,1)
y = df['mpg'].values.reshape(-1, 1)

#visualizing these values
plt.scatter(X, y)
plt.xlabel("Displacement")
plt.ylabel("Miles per gallon")
plt.title("Scatter Plot of Miles per gallon vs displacement")
plt.show()

#printing the statistical summary
print(df.describe())

Output

      mpg  cylinders  displacement horsepower  weight  acceleration  \
0    18.0          8         307.0        130    3504          12.0   
1    15.0          8         350.0        165    3693          11.5   
2    18.0          8         318.0        150    3436          11.0   
3    16.0          8         304.0        150    3433          12.0   
4    17.0          8         302.0        140    3449          10.5   
..    ...        ...           ...        ...     ...           ...   
393  27.0          4         140.0         86    2790          15.6   
394  44.0          4          97.0         52    2130          24.6   
395  32.0          4         135.0         84    2295          11.6   
396  28.0          4         120.0         79    2625          18.6   
397  31.0          4         119.0         82    2720          19.4   

     model year  origin                   car name  
0            70       1  chevrolet chevelle malibu  
1            70       1          buick skylark 320  
2            70       1         plymouth satellite  
3            70       1              amc rebel sst  
4            70       1                ford torino  
..          ...     ...                        ...  
393          82       1            ford mustang gl  
394          82       2                  vw pickup  
395          82       1              dodge rampage  
396          82       1                ford ranger  
397          82       1                 chevy s-10  

[398 rows x 9 columns]
(398, 9)
    mpg  cylinders  displacement horsepower  weight  acceleration  model year  \
0  18.0          8         307.0        130    3504          12.0          70   
1  15.0          8         350.0        165    3693          11.5          70   
2  18.0          8         318.0        150    3436          11.0          70   
3  16.0          8         304.0        150    3433          12.0          70   
4  17.0          8         302.0        140    3449          10.5          70   
5  15.0          8         429.0        198    4341          10.0          70   
6  14.0          8         454.0        220    4354           9.0          70   
7  14.0          8         440.0        215    4312           8.5          70   
8  14.0          8         455.0        225    4425          10.0          70   
9  15.0          8         390.0        190    3850           8.5          70   

   origin                   car name  
0       1  chevrolet chevelle malibu  
1       1          buick skylark 320  
2       1         plymouth satellite  
3       1              amc rebel sst  
4       1                ford torino  
5       1           ford galaxie 500  
6       1           chevrolet impala  
7       1          plymouth fury iii  
8       1           pontiac catalina  
9       1         amc ambassador dpl  
      mpg  cylinders  displacement horsepower  weight  acceleration  \
388  26.0          4         156.0         92    2585          14.5   
389  22.0          6         232.0        112    2835          14.7   
390  32.0          4         144.0         96    2665          13.9   
391  36.0          4         135.0         84    2370          13.0   
392  27.0          4         151.0         90    2950          17.3   
393  27.0          4         140.0         86    2790          15.6   
394  44.0          4          97.0         52    2130          24.6   
395  32.0          4         135.0         84    2295          11.6   
396  28.0          4         120.0         79    2625          18.6   
397  31.0          4         119.0         82    2720          19.4   

     model year  origin                    car name  
388          82       1  chrysler lebaron medallion  
389          82       1              ford granada l  
390          82       3            toyota celica gt  
391          82       1           dodge charger 2.2  
392          82       1            chevrolet camaro  
393          82       1             ford mustang gl  
394          82       2                   vw pickup  
395          82       1               dodge rampage  
396          82       1                 ford ranger  
397          82       1                  chevy s-10  
Index(['mpg', 'cylinders', 'displacement', 'horsepower', 'weight',
       'acceleration', 'model year', 'origin', 'car name'],
      dtype='object')
0      18.0
1      15.0
2      18.0
3      16.0
4      17.0
       ... 
393    27.0
394    44.0
395    32.0
396    28.0
397    31.0
Name: mpg, Length: 398, dtype: float64

              mpg   cylinders  displacement       weight  acceleration  \
count  398.000000  398.000000    398.000000   398.000000    398.000000   
mean    23.514573    5.454774    193.425879  2970.424623     15.568090   
std      7.815984    1.701004    104.269838   846.841774      2.757689   
min      9.000000    3.000000     68.000000  1613.000000      8.000000   
25%     17.500000    4.000000    104.250000  2223.750000     13.825000   
50%     23.000000    4.000000    148.500000  2803.500000     15.500000   
75%     29.000000    8.000000    262.000000  3608.000000     17.175000   
max     46.600000    8.000000    455.000000  5140.000000     24.800000   

       model year      origin  
count  398.000000  398.000000  
mean    76.010050    1.572864  
std      3.697627    0.802055  
min     70.000000    1.000000  
25%     73.000000    1.000000  
50%     76.000000    1.000000  
75%     79.000000    2.000000  
max     82.000000    3.000000

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