Keywords: Mushroom Classification
Mushroom classification is an important task which is traditionally relied on manual expertise. Artificial neural networks (ANN) have shown promising results in automating this process by analyzing various features of mushrooms and accurately classifying them into edible or poisonous categories. Limited availability of mushroom data and the complexity in classifying the mushrooms due to minor visual differences. The purpose of this project is to predict the class of mushroom based on their physical and visual features. The ANN model trained on the mushroom dataset achieved high accuracy in classifying mushrooms as either edible or poisonous.
Aim
To Classify the Mashroom data Using DL Techniques
Objective
Format and preprocess mushroom dataset for ANN input. Develop an ANN model to classify edible and poisonous mushrooms. Train and validate a model for accurate classification results.
ANN Technique used
Jupyter, Google Collab
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