https://github.com/assem-elqersh/creativa-data-science-bootcamp

Jupyter notebooks from the Creativa Data Science Bootcamp, covering key data science concepts and practices across multiple sessions, from data preprocessing to model building and time series analysis.
https://github.com/assem-elqersh/creativa-data-science-bootcamp

data data-science eda exploratory-data-analysis machine-learning pandas time-series-analysis xgboost xgboost-classifier

Last synced: 14 days ago
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Jupyter notebooks from the Creativa Data Science Bootcamp, covering key data science concepts and practices across multiple sessions, from data preprocessing to model building and time series analysis.

• Host: GitHub
• URL: https://github.com/assem-elqersh/creativa-data-science-bootcamp
• Owner: Assem-ElQersh
• License: mit
• Created: 2024-08-17T20:43:31.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-08-18T10:37:32.000Z (6 months ago)
• Last Synced: 2024-11-18T20:39:16.629Z (3 months ago)
• Topics: data, data-science, eda, exploratory-data-analysis, machine-learning, pandas, time-series-analysis, xgboost, xgboost-classifier
• Language: Jupyter Notebook
• Homepage:
• Size: 1.17 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Creativa Data Science Bootcamp Notebooks

Welcome to the repository containing the Jupyter notebooks from the Creativa Data Science Bootcamp! This bootcamp is designed to guide you through essential data science concepts and hands-on practices in Python.

## Overview

This repository includes notebooks from sessions 2 to 5 of the bootcamp, which cover the following topics:

- **Session 1: Getting Started with Python for Data Science**

  Introducing Python and exploring the different types of data, and different fields that deal with data.

- **Session 2: Pandas and Data Cleaning**  

  Learn how to handle and clean data effectively using Pandas, a powerful data manipulation library.

- **Session 3: Data Preprocessing, EDA, and Feature Engineering**  

  Explore the key steps in preparing and understanding your data, including Exploratory Data Analysis (EDA) and feature engineering.

- **Session 4: Time Series Analysis and Forecasting**  

  Dive into time series data and forecasting techniques using XGBoost, along with building a simple machine learning model from scratch.

- **Session 5: Building a Classification Model**  

  Implement and evaluate a classification model using XGBoost, with steps for data normalization, feature importance analysis, and model tuning.

## How to Use

Each notebook is well-documented with explanations, visualizations, and code comments to make it easy to follow along. You can clone or download the repository and run the notebooks locally using Jupyter or any compatible platform.

## Getting Started

To set up your environment, you can use the following command to install the required dependencies:

```bash

pip install -r requirements.txt

```

## License

This project is licensed under the MIT License - see the [LICENSE](https://github.com/Assem-ElQersh/Creativa-Data-Science-Bootcamp/blob/main/LICENSE) file for details.