This project uses machine learning to predict vehicle fuel efficiency based on various attributes such as horsepower, weight, displacement, and number of cylinders.

📌 Project 2 of 6 | Pushed as part of my academic + real-world ML portfolio 🚀

• ULTIMATE.ipynb: Main notebook with EDA, preprocessing, model training, and evaluation.This ipynb exists in the code folder.

• Python
• Pandas, NumPy
• Scikit-learn
• Matplotlib, Seaborn
• Jupyter Notebook

The dataset used for this project is publicly available on Kaggle: MPG Raw Dataset.
It contains attributes like cylinders, displacement, horsepower, weight, acceleration, and model year to help predict vehicle fuel efficiency (MPG).

• Data Cleaning and Preprocessing
• Correlation and Exploratory Data Analysis (EDA)
• Linear Regression, Random Forest Regressor
• Model Evaluation using RMSE & R² Score

1. Clone this repo
2. Open ULTIMATE.ipynb in Jupyter Notebook / VS Code
3. Run all cells (preferably in a virtual environment)

Accurately predicts miles-per-gallon (MPG) and visualizes performance metrics.

Author: Ganesh Gundekarla
Feel free to connect with me on LinkedIn or check out my other projects on GitHub

fuel-efficiency-prediction/
├── README.md
├── .gitignore
├── requirements.txt            
├── code/                           
│   ├── ULTIMATE.ipynb
│   └── preprocessing.py           
├── docs/                          
│   ├── architecture.md          
│   └── evaluation_report.md       
├── data/                           
│   └── mpg_dataset.csv             
└── assets/                        
    └── correlation_matrix.png