---
title: GreenPrint AI
emoji: 🚀
colorFrom: red
colorTo: red
sdk: docker
app_port: 8501
tags:
- streamlit
pinned: false
short_description: A futuristic take on carbon footprint + AI.
---

# 🌿 GreenPrint AI

> A futuristic take on carbon footprint + AI.

## Overview

**GreenPrint AI** is a user-friendly web app that predicts your **carbon footprint** based on daily activities and suggests actionable steps to reduce it. From energy consumption to travel and food habits, the app personalizes insights using machine learning.

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## Aim

To develop a carbon footprint detection system that:
- Takes input as user activities (e.g., electricity use, transport habits, meat consumption).
- Predicts the carbon footprint in kilograms of CO₂ equivalent.
- Recommends practical, low-carbon alternatives.

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## Step-by-Step Project Workflow

### Step 1: Dataset Creation

- A synthetic dataset named `synthetic_carbon_footprint.csv` was generated using realistic formulas for CO₂ emissions from:
  - **Electricity consumption**
  - **Travel (personal & public)**
  - **Dietary choices (meat)**
  - **Plastic usage**
- These were computed using coefficients (e.g., kg CO₂ per kWh, km, gram of meat).
- The script `Creating_Dataset.ipynb` contains the exact formula-based generation logic.

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### Step 2: Model Training

- Model Used: RandomForestRegressor from scikit-learn
- Training done using `Running_Model.ipynb`
- Preprocessing involved:
  - Feature scaling (`StandardScaler`)
  - Ensuring input columns align with model_columns.pkl
  - Splitting dataset into training/testing sets
- Evaluation metrics:
  - **R² Score** for goodness-of-fit
  - **Root Mean Squared Error (RMSE)** for prediction accuracy
- The trained model was saved as `carbon_model.pkl`

### Why Random Forest Regressor?

- It handles non-linear relationships and interactions between features better than linear models.
- It’s robust to overfitting due to its ensemble nature.
- Outperformed linear models in testing, giving: Higher R² and Lower RMSE
- Offers feature importance insights, useful for explainability in a sustainability context.
  
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###  Step 3: Feature Metadata Handling

- A `model_columns.pkl` file was created to store the expected feature column order.
- This prevents column mismatch during inference in the Streamlit frontend.

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