https://github.com/mkdirer/depression-data-analysis

This project analyzes a Kaggle depression dataset using data preprocessing, clustering, classification, and outlier detection techniques. Python libraries like pandas, numpy, matplotlib, seaborn, and scikit-learn are used to extract insights.
https://github.com/mkdirer/depression-data-analysis

classification clustering matplotlib numpy pandas scikit-learn seaborn vizualization

Last synced: 3 months ago
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This project analyzes a Kaggle depression dataset using data preprocessing, clustering, classification, and outlier detection techniques. Python libraries like pandas, numpy, matplotlib, seaborn, and scikit-learn are used to extract insights.

• Host: GitHub
• URL: https://github.com/mkdirer/depression-data-analysis
• Owner: mkdirer
• License: mit
• Created: 2024-08-10T22:41:09.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-08-10T22:58:00.000Z (5 months ago)
• Last Synced: 2024-10-10T08:01:50.420Z (3 months ago)
• Topics: classification, clustering, matplotlib, numpy, pandas, scikit-learn, seaborn, vizualization
• Language: Jupyter Notebook
• Homepage:
• Size: 7.41 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Depression Data Analysis

## Project Description

This project aims to analyze data related to depression, available on the Kaggle platform. The analysis involves data manipulation, visualization, and basic modeling, data preprocessing, outlier detection, clustering, and classification using Python tools.

## Contents

The project includes the following components:

- **Dataset**: The data used in this project is sourced from [this Kaggle dataset](https://www.kaggle.com/datasets/diegobabativa/depression).

- **Jupyter Notebook**: The file `ED_projekt_1.ipynb` contains the source code.

- **Libraries**: The project utilizes the following Python libraries:

  - `pandas` for data manipulation,

  - `numpy` for mathematical computations,

  - `matplotlib` and `seaborn` for data visualization,

  - `scikit-learn` for modeling.