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Exploratory Data Analysis (EDA) Project: Mental Disorders Dataset

Overview

This project focuses on conducting exploratory data analysis (EDA) on a dataset containing information about various mental disorders. The goal of this analysis is to gain insights into the prevalence of different mental disorders and explore trends over time.

Dataset

The dataset used in this analysis contains information about mental disorders across different entities, years, and demographic groups. It consists of 6420 entries and includes the following columns:

Entity: Name of the entity (e.g., country, region). Code: Code representing the entity (e.g., ISO country code). Year: Year of observation. Schizophrenia disorders: Share of population affected by schizophrenia. Depressive disorders: Share of population affected by depressive disorders. Anxiety disorders: Share of population affected by anxiety disorders. Bipolar disorders: Share of population affected by bipolar disorders. Eating disorders: Share of population affected by eating disorders. The dataset is provided in a CSV format and can be found in the Mh.csv file.

Tools and Libraries

The analysis was performed using Python programming language and various libraries, including:

Pandas NumPy Matplotlib Seaborn

Files

data.csv: The dataset used for analysis. notebook.ipynb: Jupyter Notebook containing the code and detailed analysis. README.md: This file, providing an overview of the project.

Analysis

The analysis includes the following steps:

Data preprocessing: Handling missing values, data cleaning, and data transformation. Descriptive statistics: Summary statistics, distribution of variables, and visualizations. Exploratory data analysis: Visualizing relationships, exploring correlations, and identifying patterns. Key findings and insights: Summarizing the main observations and insights gained from the analysis. Usage

To replicate or explore the analysis:

Clone the repository: git clone https://github.com/anuescapist/mental-wellness-analytics/tree/main. Install the required libraries:

  1. pip install Python3.
  2. jupyter note book.
  3. while working on library frist time you need to install { pandas, numpy, metaplot, Seaborn } before import need to install in jupyter

Open and run the Jupyter Notebook notebook.ipynb to view the analysis.

Conclusion

This EDA project provides valuable insights into the prevalence of different mental disorders across entities and over time. Further analysis or modeling could be conducted based on these insights.

License

This project is licensed under the @anuescapist license.

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exploratory data analysis (EDA) to uncover trends and insights in mental wellness through comprehensive analytics and data visualization

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