https://github.com/burhanahmed1/automobiles-machinelearning-analysis

Data Analysis, training Machine Learning models, and Model Evaluation and Refinement for AutoMobiles dataset.
https://github.com/burhanahmed1/automobiles-machinelearning-analysis

data-acquisition data-analysis-project data-science data-wrangling exploratory-data-analysis insights machine-learning machine-learning-models matplotlib model-evaluation-and-refinement numpy pandas python scikit-learn scipy-stats seaborn

Last synced: 9 days ago
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Data Analysis, training Machine Learning models, and Model Evaluation and Refinement for AutoMobiles dataset.

• Host: GitHub
• URL: https://github.com/burhanahmed1/automobiles-machinelearning-analysis
• Owner: burhanahmed1
• License: mit
• Created: 2024-07-01T05:48:16.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-07-26T04:33:21.000Z (4 months ago)
• Last Synced: 2024-07-26T05:37:46.002Z (4 months ago)
• Topics: data-acquisition, data-analysis-project, data-science, data-wrangling, exploratory-data-analysis, insights, machine-learning, machine-learning-models, matplotlib, model-evaluation-and-refinement, numpy, pandas, python, scikit-learn, scipy-stats, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 457 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Automobiles Machine Learning Analysis

## 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:

```bash

git clone https://github.com/burhanahmed1/Automobiles-MachineLearning-Analysis.git

cd Automobiles-MachineLearning-Analysis

pip install -r requirements.txt

```

## Usage

Open the Jupyter notebook:

```bash

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.