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🚗 Auto MPG Dataset — Exploratory Data Analysis (EDA)

📌 Project Overview

This project explores the Auto MPG dataset from the UCI Machine Learning Repository, which contains specifications and fuel efficiency data for cars manufactured between 1970–1982.

The goal is to:

• Understand trends in fuel efficiency across years, origins, and engine types
• Uncover relationships between MPG, engine displacement, horsepower, weight, and acceleration
• Identify patterns that explain how automotive engineering and market trends evolved during the dataset period

This notebook demonstrates data cleaning, visual exploration, and insight generation — essential skills for any data analyst or data scientist.

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📂 Dataset Information

The dataset contains 398 entries with the following attributes:

Column	Description
mpg	Miles per gallon (fuel efficiency)
cylinders	Number of engine cylinders
displacement	Engine displacement (cubic inches)
horsepower	Engine horsepower
weight	Vehicle weight (lbs)
acceleration	Time to accelerate from 0–60 mph (seconds)
model year	Year of manufacture
origin	1 = American, 2 = European, 3 = Japanese
car name	Model name of the car

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🛠️ Methodology

1. Data Loading & Inspection

   • Checked data types, null values, and initial structure

2. Data Cleaning

   • Replaced missing horsepower values (originally '?') with median values
   • Detected and treated outliers using the IQR method

3. Exploratory Data Analysis

   • Univariate analysis: Histograms, KDE plots for each numeric feature
   • Categorical analysis: Count plots for cylinders, origin, and model year
   • Bivariate analysis: Scatter plots and correlations to explore feature relationships

4. Feature Scaling

   • Applied MinMax scaling for normalized visualizations (only for comparison, not used in core insights)

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📊 Key Insights

• Fuel Efficiency Trends: Fuel efficiency improved in later years, influenced by the 1973 oil crisis and changing regulations.

• Engine Size vs MPG: Cars with higher displacement and horsepower tend to have lower MPG.

• Vehicle Origins:

  • American cars dominate the dataset but tend to be less fuel-efficient
  • Japanese cars consistently show higher MPG
  • European cars occupy a middle ground

• Acceleration: Most vehicles have moderate acceleration times (14–16 seconds), reflecting balanced engineering priorities of the era.

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🎨 Visualizations

Some of the key plots include:

• MPG Distribution — skewed right, indicating fewer highly fuel-efficient cars
• Displacement Distribution — smaller engines more common, but still relatively large by modern standards
• Horsepower Distribution — most cars between 70–110 HP, with a secondary performance peak
• Origin Counts — custom-colored (Red = USA, Blue = Europe, Yellow = Japan)
• Correlation Heatmap — shows strong negative correlation between weight and MPG

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🚀 Next Steps

• Develop a predictive regression model for MPG
• Explore clustering to group vehicles by performance and efficiency
• Compare trends to modern datasets for historical context

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💻 Tech Stack

• Python 3
• Pandas, NumPy
• Matplotlib, Seaborn
• Jupyter Notebook

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📜 References

• UCI Machine Learning Repository — Auto MPG Dataset
• Seaborn Documentation

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Author: Srijan Sen 📧 [email protected] | 🔗 LinkedIn | 💻 GitHub

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If you want, I can also add inline preview images of your plots into this README so it visually pops on GitHub. Would you like me to do that?