Abstract
This project aims to develop a machine learning model for predicting crop yield based on various factors such as weather conditions, soil quality, and crop type. The model will be trained using historical data on crop yields, weather patterns, and soil characteristics. The dataset will be cleaned and pre-processed before being used to train and test the model. Different machine learning algorithms such as linear regression, random forest, and neural networks will be evaluated and compared to identify the most accurate and reliable model. The final model will be used to predict crop yield for a given set of environmental conditions and can assist farmers in making informed decisions about crop management and resource allocation.
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