<![CDATA[Malnutrition remains a significant global health problem, linked to a substantial proportion of child deaths worldwide. According to the United Nations, malnutrition is responsible for 45% of deaths in children under five. The World Food Programme estimates that over 820 million people globally suffer from hunger, with malnutrition playing a crucial role in this crisis. This study uses Python for data analysis and visualization, integrating time-series analysis and deep learning to forecast global malnutrition trends. The system processes data from 1970 to 2022, normalizes it, and trains a model comprising Conv1D and LSTM layers. The predictions are visualized using Plotly and displayed in a Flask web application, offering interactive features for exploring the data. The results highlight a notable decline in malnutrition-related deaths in both developing and developed nations, reflecting the success of previous interventions. However, developing countries continue to report a higher number of diseases and conditions associated with malnutrition, underscoring the need for further targeted interventions.]]>
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