Update README.md

README.md

@@ -11,9 +11,71 @@ pinned: false
 short_description: A futuristic take on carbon footprint + AI.
 ---
 
-# Welcome to Streamlit!
+# 🚀 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.
+
+---
+
+## 🎯 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.
+
+---
+
+## 📊 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.
+
+---
+
+### 🤖 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?
+
+RandomForestRegressor was chosen because:
+- 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.
+  
+---
+
+### 🧠 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.
+
+---
+
+
+
+
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).