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Simple Machine Learning

Welcome to the Machine Learning Basics - Regression Project repository! This project aims to provide a hands-on experience for understanding the fundamental concepts of machine learning, focusing on regression problems. Whether you're a beginner or an enthusiast looking to brush up on your skills, this project will guide you through the process of building and evaluating a regression model.

  • Table of Contents
  • Introduction
  • Getting Started
  • Project Structure
  • Requirements
  • Installation
  • Usage
  • Contributing
  • License
  • Introduction

Machine learning is a powerful field that enables computers to learn from data and make predictions or decisions. In this project, we will focus on regression, which involves predicting continuous values based on input features. By the end of this project, you will have a solid understanding of the key concepts in regression, feature engineering, model training, and evaluation.

Getting Started

To get started with this project, you should have some basic knowledge of Python and a working understanding of machine learning concepts. If you're new to machine learning, don't worry! We have prepared a set of Jupyter notebooks that will guide you through the entire process step-by-step.

Project Structure

The repository is organized as follows:

├── data/
│   ├── README.md
│   ├── dataset.csv
│   └── ...
│   
├── notebooks/
│   ├── 01_Data_Exploration.ipynb
│   ├── 02_Data_Preprocessing.ipynb
│   ├── 03_Feature_Engineering.ipynb
│   ├── 04_Model_Training.ipynb
│   └── 05_Model_Evaluation.ipynb
│
├── LICENSE
└── README.md

The data directory contains the dataset used in this project, along with a README file providing more information about the dataset. The notebooks directory contains a series of Jupyter notebooks that walk you through the different stages of the machine learning process.

Requirements

Before you begin, ensure you have the following requirements:

  • Python 3.6 or above
  • Jupyter Notebook
  • NumPy
  • Pandas
  • Matplotlib
  • Scikit-learn You can install the required dependencies using the following command:

pip install numpy pandas matplotlib scikit-learn Installation To use this project, you can clone the repository using the following command:

git clone https://github.com/your-username/Simple-ML.git

Usage

Once you have the repository on your local machine, navigate to the notebooks directory, and start exploring the Jupyter notebooks in sequential order. Each notebook is designed to build upon the previous one, helping you understand the regression process step by step.

Contributing

We welcome contributions to this project! If you find any issues or want to enhance the project, feel free to open an issue or submit a pull request. Please make sure to follow the contribution guidelines outlined in the CONTRIBUTING.md file.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.