LinearRegressionBeginnersProjects

GitHub Repository

Welcome to LinearRegressionBeginnersProjects! This project is designed as a hands-on exercise for beginners interested in learning about linear regression using PyTorch and other essential data science libraries in Python. Each line of code is thoroughly explained in markdown sections within Jupyter notebooks, making it easier for you to understand every step of the process.

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Table of Contents

1. Introduction
2. Installation
3. Getting Started
4. Project Structure
5. PyTorch Fundamentals
6. Usage
7. Contributing
8. License

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Introduction

This repository focuses on simple linear regression, demonstrating how to create and train models from scratch using PyTorch. This project also covers key data science libraries:

• PyTorch for building and training the model.
• scikit-learn for loading datasets and evaluation metrics.
• NumPy and Pandas for data manipulation.

Every step, from data loading to model evaluation, is accompanied by explanations in markdown cells within the Jupyter notebooks, ensuring an in-depth understanding.

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Installation

To get started, clone this repository and install the dependencies listed in the requirements.txt file.

git clone https://github.com/Sayemahamed/BeginnerLinearRegressionProjects.git
cd LinearRegressionBeginnersProjects
pip install -r requirements.txt

Dependencies

• Python 3.8+
• PyTorch
• scikit-learn
• NumPy
• Pandas

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Getting Started

This repository is structured to provide a guided, hands-on experience with linear regression. Inside the Jupyter notebooks, every line of code is explained, helping beginners gain a clear understanding of how linear regression models work in PyTorch.

1. Load data using scikit-learn.
2. Create a model from scratch with PyTorch.
3. Train the model and evaluate its performance.
4. Visualize results and understand performance metrics.

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Project Structure

LinearRegressionBeginnersProjects/
├── data/                   # Sample datasets (if any)
├── notebooks/              # Jupyter notebooks with line-by-line explanations
├── scripts/                # Standalone Python scripts to run the projects
├── models/                 # Saved model checkpoints
├── requirements.txt        # Project dependencies
└── README.md               # Project README file

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PyTorch Fundamentals

This project introduces key PyTorch concepts such as:

• Tensors for data representation.
• Autograd for automatic differentiation.
• Loss functions and optimizers to guide the learning process.

These fundamentals are covered within the notebooks in a hands-on format, ensuring you understand the purpose of each component.

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Usage

To get started, open the notebooks in notebooks/, where you’ll find step-by-step guidance through:

1. Loading and preparing data.
2. Defining the linear regression model in PyTorch.
3. Training and evaluating the model on sample datasets.

Each notebook is designed as an exercise with ample markdown explanations to help you learn interactively.

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Contributing

We welcome contributions! Feel free to open issues or submit pull requests.

1. Fork the project.
2. Create a new branch (git checkout -b feature-branch).
3. Commit changes (git commit -am 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Open a pull request.

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License

This project is licensed under the MIT License. See the LICENSE file for more details.

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Enjoy learning about linear regression with PyTorch, and happy coding!