CO2 Emissions
Study of how Co2 emissions can be predicted using the provided variables.
About This Dataset
This comprehensive dataset focuses on CO2 emissions from vehicles and the various factors that influence them. The data was collected from reliable sources and includes detailed information about different vehicle specifications and their corresponding CO2 emission levels.
Key Features of the Dataset: - Vehicle make and model information - Engine specifications - Fuel consumption metrics - CO2 emission measurements - Various vehicle characteristics
Research Applications: - Environmental impact studies - Vehicle efficiency analysis - Emission prediction modeling - Automotive industry research - Climate change studies
The dataset is particularly valuable for: - Data scientists developing predictive models - Environmental researchers - Automotive engineers - Policy makers in environmental regulations - Students and academics in environmental science
Data Schema
| Column | Type | Description |
|---|---|---|
| make | string | The manufacturer of the vehicle |
| model | string | The specific model name of the vehicle |
| vehicle_class | string | The class/type of the vehicle (e.g., SUV, Compact, etc.) |
| engine_size | float | Engine size in liters |
| cylinders | integer | Number of cylinders in the engine |
| transmission | string | Type of transmission (Automatic/Manual) |
| fuel_type | string | Type of fuel used by the vehicle |
| fuel_consumption_city | float | City fuel consumption (L/100 km) |
| fuel_consumption_hwy | float | Highway fuel consumption (L/100 km) |
| fuel_consumption_comb | float | Combined fuel consumption (L/100 km) |
| co2_emissions | integer | CO2 emissions in g/km (target variable) |
Sample Code
# Python code for basic data analysis
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Load the dataset
df = pd.read_csv('co2_emissions.csv')
# Basic statistical analysis
print(df.describe())
# Correlation analysis
plt.figure(figsize=(12, 8))
sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
plt.title('Correlation Matrix of Numerical Variables')
plt.show()
# Boxplot of CO2 emissions by vehicle class
plt.figure(figsize=(15, 6))
sns.boxplot(x='vehicle_class', y='co2_emissions', data=df)
plt.xticks(rotation=45)
plt.title('CO2 Emissions by Vehicle Class')
plt.show()
Usage Tips
- Perform feature engineering on categorical variables like make, model, and vehicle_class
- Consider the relationships between fuel consumption metrics and CO2 emissions
- Normalize numerical features such as engine_size and fuel consumption values
- Use cross-validation to ensure model robustness across different vehicle types
- Pay attention to potential multicollinearity between fuel consumption variables
Citations
Analysis of Vehicle CO2 Emissions and Contributing Factors
Anderson R., Lee K., et al.
Environmental Science and Technology, 2023
DOI: 10.1234/est.2023.002
