Predicting Precipitation and Other Weather Conditions With Logistic Regression and Random Forest Classifiers

An issue we have with our current algorithms is the inability to predict weather accurately for any time range further than ten days. Our intentions with this project were to create a model that could get an accuracy within 5% of our current weather prediction algorithms.

Nowadays, we predict the weather to get a better understanding of the days to come. An issue we have with our current algorithms is the inability to predict weather accurately for any time range further than ten days. Our intentions with this project were to create a model that could get an accuracy within 5% of our current weather prediction algorithms. We wanted the model to be accurate in both short-term and long-term conditions. Our current algorithms, while highly accurate in the short term, struggle in the long-term timespan. The importance of accuracy in this model can be boiled down to two main points; convenience, for planning activities, and safety, as perhaps an early warning system in the event of a possibly harmful or fatal weather event. Our method of achieving these goals was to first use a logistic regression model, and later a random forest classifier. In the end we reached a highest accuracy of 83%, which fell just short of our goal of 85%. Despite the inaccuracy, our model showed promising results, and if we had more time or a larger dataset, the accuracy could have been higher. In the end, the results seemed more to be a proof of concept, rather than a demonstration of accuracy and capability.

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Smart Agriculture: Optimizing Soil pH and Crop Recommendations Using Machine Learning

This study explores how hyperparameter tuning enhances the performance of machine learning models, achieving a 99.5% accuracy in crop classification and highlighting the potential of AI-driven optimization for sustainable precision agriculture.

Pracheth G.

Mentored by Henry Cerbone

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Predicting Climate Change Using an Autoregressive Long Short-Term Memory Model

This study aims to create a baseline machine learning model that utilizes an Autoregressive Recurrent Neural network with a Long Short term memory implementation for the purpose of predicting climate.