https://github.com/mohammadmoataz2/analysis-and-ml-projects-using-python

This repository contains a collection of analysis and machine learning projects implemented in Python. The projects cover various domains and utilize different techniques to gain insights from data and build predictive models.
https://github.com/mohammadmoataz2/analysis-and-ml-projects-using-python

anaylsis artificial-intelligence machine-learning pandas-python python seaborn sklearn sklearn-library

Last synced: 3 months ago
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This repository contains a collection of analysis and machine learning projects implemented in Python. The projects cover various domains and utilize different techniques to gain insights from data and build predictive models.

• Host: GitHub
• URL: https://github.com/mohammadmoataz2/analysis-and-ml-projects-using-python
• Owner: MohammadMoataz2
• Created: 2024-05-16T07:59:09.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-16T10:18:16.000Z (about 1 year ago)
• Last Synced: 2025-01-21T10:14:02.815Z (4 months ago)
• Topics: anaylsis, artificial-intelligence, machine-learning, pandas-python, python, seaborn, sklearn, sklearn-library
• Language: Jupyter Notebook
• Homepage:
• Size: 38.3 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Analysis and ML Projects using Python

## Description

This repository contains a collection of analysis and machine learning projects implemented in Python. The projects cover various domains and utilize different techniques to gain insights from data and build predictive models.

## Folder Structure

- **Machine Learning:**

  - **Supervised:**

    - **Diabetes Classification:**

      - **Description:** This project focuses on predicting the risk of diabetes based on various health indicators such as age, pregnancies, glucose level, blood pressure, BMI, etc. The analysis delves into the relationships between these features and the diabetes outcome, providing insights into the factors affecting the condition. A machine learning model is developed to classify individuals into diabetic or non-diabetic categories.

      ![image](https://github.com/MohammadMoataz2/DiabetesPlatform/assets/123085286/3a251494-965a-4f05-b5df-ed7fda1154d8)

    - **Machine Failure Classification:**

      - **Description:** This project revolves around predicting machine failures using sensor data such as temperature, pressure, etc. The analysis investigates patterns leading to machine failures and identifies critical factors influencing the occurrence of failures. A machine learning model is built to classify machines as either functional or prone to failure.

      ![image](https://github.com/MohammadMoataz2/DiabetesPlatform/assets/123085286/51829fee-b1f7-4d75-80f1-0679254638b5)

  - **Unsupervised:**

    - **Image Compression using KMeans:**

      - **Description:** This project demonstrates image compression using the KMeans clustering algorithm. It explores the application of unsupervised learning techniques to reduce the dimensionality of image data, resulting in compressed representations while preserving important visual information.

      ![image](https://github.com/MohammadMoataz2/DiabetesPlatform/assets/123085286/92347754-a450-4b43-8d26-2cbc515ce10f)

- **Analysis:**

  - **Fast Food Analysis:**

    - **Description:** This analysis focuses on exploring nutritional information from fast-food restaurants. It covers various attributes such as calories, fat content, cholesterol, etc., for different menu items. The analysis identifies trends such as the restaurants with the lowest calorie averages and examines the relationship between nutritional components. Additionally, menus are constructed for each restaurant, detailing meal compositions and nutritional values.

    ![image](https://github.com/MohammadMoataz2/DiabetesPlatform/assets/123085286/77ccf9d8-9953-4657-861f-46bbbdf2a833)

  - **Student Performance Analysis:**

    - **Description:** This analysis investigates factors influencing student performance based on demographic and educational variables such as gender, parental education, etc. It examines the correlation between these factors and students' math scores, providing insights into the educational landscape. Performance metrics are analyzed to understand the influence of various factors on student achievements.

![image](https://github.com/MohammadMoataz2/DiabetesPlatform/assets/123085286/86378811-699a-4edc-995a-5d85e8f16512)

**Note:** Each project folder may contain detailed documentation, code, and data relevant to the respective analysis or machine learning task.

## Contributing

Contributions to this repository are welcome! If you have any analysis or machine learning projects implemented in Python that you'd like to share, feel free to fork this repository and submit a pull request with your additions.