https://github.com/aicorsair/dataquest-data-science-analysis-projects

A repository dedicated to storing guided projects completed while learning data science concepts with Dataquest.
https://github.com/aicorsair/dataquest-data-science-analysis-projects

classification-models cluster-analysis data-analysis data-analytics data-cleaning data-preparation data-preprocessing data-science data-visualization deep-learning excel feature-engineering machine-learning pandas-dataframe power-bi python-3 regression-models scikit-learn sql web-scraping

Last synced: 3 days ago
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A repository dedicated to storing guided projects completed while learning data science concepts with Dataquest.

• Host: GitHub
• URL: https://github.com/aicorsair/dataquest-data-science-analysis-projects
• Owner: AiCorsair
• Created: 2024-04-27T09:43:53.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-01-11T07:37:19.000Z (about 1 month ago)
• Last Synced: 2025-01-11T08:27:53.819Z (about 1 month ago)
• Topics: classification-models, cluster-analysis, data-analysis, data-analytics, data-cleaning, data-preparation, data-preprocessing, data-science, data-visualization, deep-learning, excel, feature-engineering, machine-learning, pandas-dataframe, power-bi, python-3, regression-models, scikit-learn, sql, web-scraping
• Language: Jupyter Notebook
• Homepage:
• Size: 74 MB
• Stars: 11
• Watchers: 1
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Dataquest-Projects

This repository stores guided projects completed while learning data science concepts with [Dataquest](https://www.dataquest.io/). I worked on a variety of projects using tools such as **Python**, **SQL**, **Excel**, and **Power BI**, practicing data cleaning, analysis, visualization, web scraping, and working with APIs, as well as data modeling and forecasting.

The primary data science libraries I used include **Pandas**, **NumPy**, **Requests**, **BeautifulSoup**, **Matplotlib**, **Seaborn**, **Scikit-Learn**, **TensorFlow**, and **Keras**. In machine learning, I applied various regression, classification, and clustering algorithms (e.g. **OLS**, **Ridge**, **Lasso**, **KNN**, **Decision Tree**, **K-Means**). In deep learning, I focused on building neural networks using both the **Sequential** and **Functional** APIs.

If you'd like to work on any of the available projects, be sure to find and download the project files from the `Datasets` folder, which contains **CSV**, **Data Base**, and **SQL Text** files.