Datacamp_Projects

This repository contains a curated collection of data science projects completed on DataCamp, each exploring different domains, techniques, and datasets using Python and related technologies.

Projects Overview

Below is a list of the projects included in this repository, each located in its own directory:

1. ASL Recognition with Deep Learning

Builds and trains deep learning models to recognize American Sign Language gestures from image data.

2. Analyzing Crime in Los Angeles

Performs data analysis on crime datasets from Los Angeles to uncover patterns and trends.

3. Analyzing Students' Mental Health

Examines survey data to understand factors affecting students' mental health.

4. Building a Retail Data Pipeline

Demonstrates ETL and pipeline engineering for retail transaction datasets.

5. Classify Song Genres from Audio Data

Uses audio features and machine learning to classify music tracks by genre.

6. Clustering Antarctic Penguin Species

Applies clustering algorithms to biological data to distinguish penguin species.

7. Exploring London's Travel Network

Analyzes and visualizes public transport data for London, including journeys and network structure.

• Key files:
  • notebook.ipynb: Main analysis and visualization notebook.
  • tfl_journeys_final.csv: Dataset of journeys.
  • london.jpg, tube.jpg: Visual assets.

8. Exploring the Evolution of Linux

Investigates the development history and community aspects of the Linux operating system.

9. Investigating Netflix Movies

Performs exploratory data analysis on Netflix movie offerings and characteristics.

10. Performing a Code Review

Showcases code review practices and automated quality checks on a sample project.

11. Predictive Modeling for Agriculture

Develops machine learning models to predict agricultural outcomes from environmental data.

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How to Use

1. Clone the repository:

   git clone https://github.com/alyalsayed/Datacamp_Projects.git

2. Explore each project folder: Each directory contains code, datasets, and (where available) notebooks or documentation for running the analyses.

3. Dependencies: Most projects use Python (Jupyter notebooks), and may require libraries like pandas, numpy, matplotlib, scikit-learn, etc. Refer to any requirements.txt or notebook instructions per project.

License

This repository is released under the MIT License.

Author

Created by alyalsayed.

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