Predicting Carbon Emissions with ML: Analyzing lifestyle impacts on carbon footprints using Linear Regression, Random Forest, and GBM models to identify reduction strategies.
This repository hosts a comprehensive analysis aimed at understanding and predicting individual carbon emissions based on a variety of lifestyle factors. Leveraging machine learning techniques, this project explores the intricate relationships between daily habits—such as transportation choices, dietary preferences, and energy usage—and their impact on carbon footprints. The ultimate goal is to uncover insights that can inform sustainable living practices and contribute to the global effort in reducing carbon emissions.
- Objective: To predict carbon emissions using lifestyle data, identifying key factors that contribute to higher emissions and exploring opportunities for reduction.
- Dataset: Includes variables on transport modes, diet types, energy consumption, and more, alongside calculated carbon emissions for individuals.
- Analysis: Incorporates exploratory data analysis (EDA), correlation analysis, outlier detection, and predictive modeling using various machine learning algorithms.
- Exploratory Data Analysis: Visual and statistical exploration of the dataset to understand the distribution of variables and initial insights.
- Correlation and Outlier Analysis: Examination of the relationships between variables and the impact of outliers on the analysis.
- Machine Learning Models: Implementation and evaluation of multiple models, including Linear Regression, Random Forest, and Gradient Boosting Machines (GBM), to predict carbon emissions based on selected predictors.
- Model Comparison and Evaluation: Assessment of model performance through metrics such as Mean Squared Error (MSE) and R-squared (R²) to identify the most effective predictive model.
Contributions to improve the analysis or extend the dataset are welcome. Please refer to the contributing guidelines for more information on how to submit pull requests or report issues.