VehicLens: Optimized Regression Modeling of Automobile Attributes

Introduction

This repository contains the analysis and machine learning model implementation for the automobile dataset. The goal is to predict various automobile attributes using different machine learning techniques.

Table of Contents

1. Data Import and Cleaning
2. Exploratory Data Analysis (EDA)
3. Model Evaluation
4. Over-fitting, Under-fitting, and Model Selection
5. Ridge Regression
6. Grid Search

Technologies Used

• Programming Language: Python
• Libraries: Pandas, NumPy, Scikit-Learn
• Tools: Jupyter Notebook

R-squared (R²) Values:

• Linear Regression: 0.3636
• Multiple Linear Regression: 0.6619
• Polynomial Regression (degree 5): 0.5568
• Ridge Regression (best alpha=10000): 0.8412

Getting Started

To get started with this project, clone the repository and install the necessary dependencies:

git clone https://github.com/burhanahmed1/Automobiles-MachineLearning-Analysis.git
cd Automobiles-MachineLearning-Analysis
pip install -r requirements.txt

Usage

Open the Jupyter notebook:

jupyter notebook AutoMobile-ML.ipynb

Dataset

The dataset used in this analysis is AutoMobile-Dataset-3.csv, which contains various features related to automobiles such as make, body style, engine type, horsepower, and price.

R^2 scores

R^2 scores of the Linear Regression model created using different degrees of polynomial features, ranging from 1 to 4.

R^2 values of Ridge Regression model for training and testing sets with respect to the values of alpha.

Contributing

Contributions are welcome! Please fork this repository and submit pull requests.

License

This project is licensed under the MIT License.