Data Science Project Repository

Project Overview

This repository is a comprehensive guide for anyone interested in learning and applying data science from scratch. Whether you are a beginner or an experienced data scientist, this project aims to provide a structured and hands-on approach to various data science concepts and techniques.

Table of Contents

1. Introduction
2. Getting Started
   • Prerequisites
   • Installation
3. Project Structure
4. Notebooks
   • 1. Data Exploration
   • 2. Data Cleaning
   • 3. Feature Engineering
   • 4. Model Building
   • 5. Model Evaluation
   • 6. Deployment
5. Machine Learning Projects
   • Random Forest
   • Logistic Regression
   • Ridge and Lasso Regression
   • ElasticNet
   • Bayes Theory
   • Decision Tree
   • Convolutional Neural Networks (CNN)
   • Artificial Neural Networks (ANN)
6. Dataset
7. Contributing
8. License

Introduction

This repository is designed to guide you through the various stages of a typical data science project. It covers fundamental concepts such as data exploration, cleaning, feature engineering, model building, evaluation, and even deployment. Each section is accompanied by Jupyter notebooks that provide hands-on examples and exercises.

Getting Started

Prerequisites

Ensure you have the following prerequisites installed:

• Python (version 3.12)
• Jupyter Notebooks
• Pandas
• NumPy
• Matplotlib
• Scikit-learn
• [Additional libraries as needed]

Installation

Clone the repository to your local machine:

git clone https://github.com/swalehmwadime/G00dlife-datascience

Install the required dependencies:

pip install -r requirements.txt

Project Structure

The repository is organized as follows:

data-science-project/
|-- notebooks/
|-- data/
|-- scripts/
|-- models/
|-- README.md
|-- requirements.txt

• notebooks: Contains Jupyter notebooks for each stage of the data science project.
• data: Placeholder for datasets used in the project.
• scripts: Any utility scripts or helper functions.
• models: Saved models or model artifacts.

Dataset

Describe the dataset used in the project and provide download links if applicable.

Contributing

We welcome contributions! If you find any issues or have suggestions, feel free to open an issue or submit a pull request.
Check Contributing.md for guidlines on how to contribute.

License

This project is licensed under the MIT-licence - see the LICENSE.md file for details.