KEYWORDS: Decision trees, Data modeling, Random forests, Machine learning, Education and training, Systems modeling, Overfitting, Linear regression, Feature extraction, Performance modeling
Numerous variables contribute to wide swings in the price of homes. Size, proximity to services, and other conveniences all play a role. However, in certain instances, these costs are not based on accurate value and are, instead, inflated. Many of us have probably come into situations in which the price of a home seemed excessively high for reasons that ultimately didn't matter to us. The goal of this work is to establish home values by considering a wide range of tangible characteristics. We need a model that takes into account all relevant factors to arrive at a fair pricing that works for both the buyer and the seller. For this reason, rather of depending on just one Machine Learning algorithm to make home price predictions, a combined approach is proposed. We can make sense of the various algorithmic outputs and formulate a rule that makes use of all these numbers. The cost of a home will be settled according to this guideline. The proposed approach would reduce errors and house price prediction precision also increases.
People worldwide are becoming increasingly conscious about leading a healthy life. However, in the current busy day to day regimes, paucity of time to properly consult nutritionist for a customized food chart is certainly difficult for a lot of people due to which their health takes a back seat. Thus, a trustworthy technique for calorie and nutrient sustenance estimate in food is needed. The patient must record their daily food intake for monitoring, however due to factors including lack of nutrition, knowledge, or self-discipline, it is difficult for patients to analyze or regulate their daily intake. The proposed approach helps dieticians and patients calculate their daily calorie intake. It is a web-based approach employing computer vision for estimating fruit calories and vegetable calories that improves individual’s utilization propensities of wellness. The goal of the proposed work is to create an accurate and user-friendly calorie measurement system that can help individuals make more informed dietary choices and monitor their calorie intake. The proposed system can accurately measure the calories in 95% cases.
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