RENEWABLE ENERGY PRODUCTION FORECAST
The focus of this project is to conduct a thorough analysis and make predictions about the future of renewable energy production in the United States. By utilizing data from the U.S. Energy Information Administration (EIA) and the Renewable Energy World Wide dataset, we aim to get valuable insights on the present state and future growth prospects of renewable energy. The aim is to predict upcoming trends in renewable energy production, providing vital information for policymakers, investors, and others with an interest in the energy sector. The project highlights the imperative necessity for a shift from fossil fuels to renewable energy sources.
CATTLEGUARD: AI-POWERED UAV FOR CATTLE THEFT PREVENTION
A innovative approach has been devised to tackle the widespread problem of livestock theft in large-scale farming operations. Detecting suspicious actions that indicate theft is achieved by harnessing the capabilities of Convolutional Neural Network (CNN) models. The models, which have undergone training using a large dataset of photos, are implemented on unmanned aerial vehicle (UAV) surveillance systems to do live monitoring. Featuring a remarkable accuracy rate of 90%, this technique emerges as a highly promising tool for efficient livestock management and theft prevention.
EZ REVIEW
Yet to be written.
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SOCIAL MEDIA ADS EFFECTIVENESS ANALYSIS
Machine learning algorithms were utilized to analyze the impact of social media marketing. A thorough review of a dataset titled "Social_Network_Ads" was performed. The findings were highly informative, as 60% of the individuals in the dataset were shown to be unaffected by these adverts. This substantial proportion emphasizes how important it is for commercials to be more captivating. There is a clear indication that there is ample opportunity to enhance the design and content of social media advertisements in order to successfully attract audience attention and impact their behavior.
RECIPE RECOMMENDATION SYSTEM
The "Healthy Recipe Recommendation Using Nutrition and Ratings Model" is a pioneering initiative that combines data mining techniques with nutritional research to create a customized recipe recommendation system. The technique employs an extensive dataset, including user ratings, specific nutritional information, and varied gastronomic preferences. The system suggests nutritious dishes that are in line with individual preferences and dietary objectives, encouraging the adoption of healthier eating habits and well-informed culinary decisions. The research findings improve comprehension of the relationship between nutritional makeup and user preferences, emphasizing the growing prevalence of health-conscious dietary preferences. The application of cosine similarity models in the recommendation system has shown considerable effectiveness, accurately matching recipe suggestions with user-defined nutritional needs.