The main aim of the project was to investigate the impact of dietary habits on sleep quality. Participants were asked questions about their eating habits and the effects of these factors on sleep quality were analyzed. Categorical data were converted into numerical values and normalization was performed on the data. Since there is a classification problem, models such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Logistic Regression were trained to reach the best accuracy value. It was observed that factors such as skipping meals, food group consumed, and caffeine consumption were partially determinants of sleep quality. The data obtained provide important information on understanding the effects of dietary habits on sleep patterns. In this context, it was concluded that dietary habits should be reviewed and regulated for a healthier sleep pattern. The research contributes to a deeper understanding of the link between nutrition and sleep. Although a certain consistency was observed between the studies, the study design generally produced a poor to fair result, which does not allow the conclusion of a causal relationship. However, nutrition-related variables are associated with sleep quality. Further studies with larger and more balanced data sets are needed to support this finding.
bestekucuk/DataAnalyzing-and-Forecasting
An analysis was conducted on survey data obtained from 240 individuals to explore the influence of sleep on dietary habits. Predictions were made using methods such as K Nearest Neighbor, Support Vector Machine, and Logistic Regression.
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