This project delves into the critical issue of mental health, leveraging two distinct datasets to gain insights into its various facets.

Dataset 1: Health, Lifestyle, and Socio-Economic Factors
This dataset explores the correlation between mental health and factors such as age, education, employment, marital status, income, and lifestyle choices. Through visualizations like bar charts and heatmaps, we uncover trends such as: - Higher prevalence of depression among individuals with lower income and education levels. - The impact of lifestyle choices like smoking, alcohol consumption, diet, sleep, and physical activity on mental well-being.

Dataset 2: Country-wise Mental Health Prevalence
This dataset provides a global perspective on mental health, analyzing the prevalence of various mental disorders across different countries and years. Time series plots reveal trends in the prevalence of disorders like schizophrenia, bipolar disorder, eating disorders, anxiety, and depression. Additionally, we identify countries with the highest and lowest overall mental disorder prevalence.

Machine Learning for Prediction
To further our understanding, we employ machine learning algorithms to predict mental health outcomes. Using techniques like logistic regression, decision trees, and random forests, we assess the predictive power of different features. Notably, a Random Forest Regressor model demonstrates promising results in predicting mental fitness based on country-level data.

Conclusion
This project sheds light on the multifaceted nature of mental health, highlighting the influence of individual and societal factors. By combining data analysis and machine learning, we aim to contribute to a better understanding of mental health and inform potential interventions.