Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/harmanveer-2546/mental-health-eda-prediction-

Mental health includes our emotional, psychological, and social well-being. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others, and make healthy choices. Mental health is important at every stage of life, from childhood and adolescence through adulthood.
https://github.com/harmanveer-2546/mental-health-eda-prediction-

correlation cross-validation exploratory-data-analysis kfold linearregression matplotlib mental-health mental-health-awareness mental-illness normalization pandas plotly preprocessing python seaborn subplots test-train-split visualization

Last synced: about 2 months ago
JSON representation

Mental health includes our emotional, psychological, and social well-being. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others, and make healthy choices. Mental health is important at every stage of life, from childhood and adolescence through adulthood.

Awesome Lists containing this project

README

        

## Mental health

Mental health includes our emotional, psychological, and social well-being. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others,
and make healthy choices. Mental health is important at every stage of life, from childhood and adolescence through adulthood.

#### Why is mental health important for overall health?

Mental and physical health are equally important components of overall health. For example, depression increases the risk for many types of physical health problems, particularly
long-lasting conditions like diabetes, heart disease, and stroke. Similarly, the presence of chronic conditions can increase the risk for mental illness.

In this notebook, I intend to perform analysis among different datasets. First, we will have a comprehensive analysis of the data using the Plotly library. Then, with the regression algorithm, we consider one of the variables as a target
and model it. Of course, it is better to use clustering in this data, which I will do in the next notebook.

### Conclusion :

As we have seen, the maximum regression accuracy for this model was almost 70%. Since correlation between variables is very important in a regression project, perhaps the required correlation was not present in this dataset. I think the use of clustering and PCA or even SOM
can give a good result in this data set. thank you for your attention. If this notebook was useful for you, please vote for this notebook.