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.
MIT License
Stars
0
Committers
2
Ecosystems:
NumPy,
scikit-learn
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.
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Jupyter Notebook: The file
ED_projekt_1.ipynbcontains the source code. -
Libraries: The project utilizes the following Python libraries:
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pandasfor data manipulation, -
numpyfor mathematical computations, -
matplotlibandseabornfor data visualization, -
scikit-learnfor modeling.
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