Linear Regression from Scratch

Welcome to the "Linear Regression from Scratch" project. In this project, we have developed a linear regression model to predict car prices using a dataset from Kaggle. What makes this project unique is that the entire model is built from the ground up, using simple tools and libraries like NumPy, Pandas, and Matplotlib for data manipulation, model creation, and visualization.

Project Overview

Linear regression is a fundamental machine learning technique used for predictive analysis. In this project, we have focused on applying linear regression to solve a real-world problem - predicting car prices based on various features.

Key Features:

• Dataset: We have used a dataset sourced from Kaggle, which provides information about various car attributes, including make, model, year, mileage, and more.

• From Scratch: Unlike using pre-built machine learning libraries, our model is built entirely from scratch. This allows for a deeper understanding of the mechanics behind linear regression.

• Libraries: While we built the model from scratch, we have utilized libraries such as NumPy for mathematical operations, Pandas for data handling, and Matplotlib for creating insightful visualizations.

Repository Contents

• linear_regression.py: This script contains the implementation of linear regression, including data preprocessing, model training, and predictions.

• dataset.csv: The dataset we used for this project.

• results/: A directory that stores the results of our model, including predictions and visualization outputs.

• notebooks/: Jupyter notebooks used for data exploration and analysis.

Getting Started

To get started with this project, follow these steps:

1. Clone the repository to your local machine.
2. Open and explore the Jupyter notebooks in the notebooks/ directory to understand our data analysis process.
3. Examine the linear_regression.py script to see the implementation of linear regression from scratch.
4. Use the dataset provided (dataset.csv) to practice and experiment with the model.

Dependencies

To run the code in this project, you will need the following Python libraries:

• NumPy
• Pandas
• Matplotlib

You can install these libraries using pip or conda as needed.

Contribution

We welcome contributions and improvements from the community. If you have ideas for enhancing the project or would like to contribute, please submit a pull request.

Acknowledgments

We would like to acknowledge the Kaggle community for providing the dataset and inspiration for this project.

Thank you for your interest in our linear regression model built from scratch. We hope you find it educational and insightful for your own machine learning journey.