The Mental Health Prediction project is a data science and machine learning attempt that aims to predict mental health treatment based on a variety of factors. This project uses classification algorithms to examine and predict the possibility of mental health therapy, such as whether or not to see a physiatrist based on a dataset containing a variety of psychological and demographic characteristics.
- Data Analysis: Comprehensive analysis of mental health dataset.
- Visualization: Graphical representation of data insights.
- Predictive Modeling: Used machine learning algorithms to predict mental health treatment.
- Prediction Result: Generated results by inputing required parameters.
The dataset used in this project contains several features related to mental health treatment including Gender, family history, stress, mood swings, days indoor etc. The dataset is split into training and testing sets to evaluate model performance.
Enter the parameters in the last cell and then run the project, it will show the prediction according to the input parameters.
If you would like to contribute to this project, please fork the repository and submit a pull request. Ensure that your changes are well-documented and tested.