README.md

Co2-Emissions-Forecasting-Using-Regression

Implemented a research paper on regression analysis from scratch to predict Co2 emissions of different vehicles.

Achieved an accuracy of 99.67% using Random Forest Regressor and improved accuracy by 1.24%.

Research Paper link: https://www.researchgate.net/publication/379941902

This repository contains a machine learning project to predict CO2 emissions using multiple regression models, including:

• Linear Regression
• Decision Tree Regressor
• Random Forest Regressor
• K-Nearest Neighbors (KNN) Regressor
• XGBoost Regressor

Preprocessing Steps

1. Handling Missing Values:

   • Checked for missing data and filled or dropped values as necessary.

2. Feature Engineering:

   • Created derived features such as Fuel Consumption per Cylinder and CO2 per Liter.
   • Combined redundant features like city and highway fuel consumption.

3. Feature Scaling:

   • Standardized numerical features for models sensitive to scaling (e.g., KNN, SVR).

4. Categorical Encoding:

   • Used one-hot encoding for categorical features like Transmission and Fuel Type.

5. Train-Test Split:

   • Split the dataset into 80% training and 20% testing sets.

Model Accuracies

Model	Accuracy (%)
Random Forest	99.67
K-Neighbors Algorithm	99.11
Linear Regression	99.03
Decision Tree	98.23
XGBRegressor	84.16

Visualization

Below is a bar graph comparing the accuracies of the models:

How to Use

Clone this repository:

git clone https://github.com/Laasyakshara25/Co2-Emissions-Forecasting-Using-Regression.git