https://github.com/abdelhakim-gh/machine-learning_data-analysis_project

recognizing handwritten numbers & comparing the Life Expectancy vs Fertility in 1960 & 2013 of regions
https://github.com/abdelhakim-gh/machine-learning_data-analysis_project

data-analysis jupyter-notebook machine-learning python r r-studio

Last synced: 8 days ago
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recognizing handwritten numbers & comparing the Life Expectancy vs Fertility in 1960 & 2013 of regions

• Host: GitHub
• URL: https://github.com/abdelhakim-gh/machine-learning_data-analysis_project
• Owner: Abdelhakim-gh
• Created: 2024-02-13T12:06:58.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-02-13T12:25:54.000Z (12 months ago)
• Last Synced: 2024-12-03T04:17:28.981Z (2 months ago)
• Topics: data-analysis, jupyter-notebook, machine-learning, python, r, r-studio
• Language: HTML
• Homepage:
• Size: 2.3 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Machine Learning & Data Analysis Projects

> **Abdelhakim EL Ghayoubi** 

- This is a project I worked on to apply the things I learned.

- I've provided both code and documentation for both projects, as well as a summary for languages `Python` & `R`.

- Data sets are also provided, make sure to analyze them thoroughly.

### Machine Learning Project with `Python`

- This project focuses on recognizing handwritten numbers (digits) using machine learning algorithms. Handwritten digits vary significantly in terms of writing style, size, and orientation.

- The objective is to develop a machine learning model capable of accurately identifying handwritten digits. This model should be trained on a dataset containing different representations of numbers in the form of image grids. Once trained, the model should be able to predict the digit in unseen images with high accuracy.

### Data Analysis Projects with `R`

- This project focuses on comparing the Life Expectancy vs. Fertility in 1960 & 2013 of regions to highlight the regions with stable & high quality of life or maybe predict the potential growing/developed region.

### Tools used: 

- Jupyter note book 

- R studio 

- Python & R (last versions)