392 vehicles · Model years 1970–1982 · UCI Machine Learning Repository
Overview
Predictor
Distributions
Correlation
Box Plots
Trends
Regression
Diagnostics
Neural Network
Between 1970 and 1982, the American auto industry faced its biggest upheaval since the Model T.
Two oil crises forced carmakers to rethink everything — engine size, weight, country of origin.
Average fuel economy nearly doubled in just twelve years.
The 392 vehicles in this dataset capture that transformation in numbers.
+81%
MPG improvement, 1970 → 1982
Average rose from 17.7 to 32.0 MPG as regulations tightened and engines shrank
52%
Japanese efficiency advantage
Japanese cars averaged 30.5 MPG vs 20.0 MPG for American-made vehicles
−7.6
MPG lost per 1,000 lbs of weight
The lightest cars (under 2,225 lbs) averaged 32 MPG — the heaviest just 15 MPG
'80
Peak efficiency year
1980 models averaged 33.8 MPG — the highest in the dataset, driven by post-crisis downsizing
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Descriptive Statistics
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Average MPG by Country of Origin
MPG Trend by Origin over the Years
How Each Measurement Is Spread
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How Features Move Together
Correlation Heatmap (Pearson r)
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Strength of relationship with MPG
Pairwise Scatter Plots
MPG Spread Within Groups
MPG by Number of Cylinders
MPG by Country of Origin
MPG by Model Year — Median with Interquartile Range
How Things Changed Year by Year
MPG by Country of Origin over Years
Horsepower and Weight — both fell as efficiency rose
VIF above 10 means this feature overlaps heavily with others — displacement shares most of its information with weight and cylinders.
Residuals vs Fitted — should be a random cloud around zero
Normal Q-Q Plot — dots on the line means residuals are normally distributed
Residual Distribution — should look like a bell curve
Cook's Distance — red bars are unusually influential data points
Predict MPG for Any Vehicle
Move the sliders to describe a vehicle — the model updates its prediction instantly.
3 cyl8 cyl
68455
46 hp230 hp
1,613 lbs5,140 lbs
8 sec (fast)25 sec (slow)
'70 (dataset start)'95 (extrapolated)
⚠ Years beyond 1982 are extrapolated — outside the training data. The model assumes the efficiency trend continued linearly, which may not reflect real-world changes (e.g. fuel economy plateaued in the late 1980s).
--MPG
9 MPG55 MPG
How Your Vehicle Compares to the Dataset Average
Feature profile — percentage position within the dataset range
A neural network learns by passing data through layers of connected nodes. Each connection has a
weight — a number the network adjusts during training to reduce its prediction error.
Unlike linear regression, it can capture curved, non-linear relationships in the data.
Configure the architecture below, hit Train, and watch it learn in real time.
Network Architecture
Epoch 0 / 100
Live Architecture Diagram
Training Loss — falling means the network is learning
Neural Net vs Linear Regression vs Actual MPG
Explore How the Data Separates
Pick any two features for the axes. Points are coloured by MPG — red = low, green = high.
After training, switch colour mode to see where the neural network's predictions agree or disagree with reality.
Train the network to unlock "NN Predicted MPG" and "NN Error" colour modes.
Predict with the Trained Network
Train the network first, then use these sliders to predict.