Overview / Usage

Machine learning has revolutionized the way we approach problems and has opened up new possibilities for solving complex issues. One such problem is the detection of edible mushrooms by analysing their physical charecteristics.

This project involves identifying edible mushrooms using various features such as cap shape, cap color, gill size, spore print color, habitat, and other characteristics. Machine learning models such as logistic regression, decision trees, random forest, SVM, and XGBoost can be used to build prediction models to classify mushrooms as either edible or poisonous based on these features. The accuracy of each model can be compared to identify the best performing model.

Methodology / Approach

✅ The dataset used in this project is the Mushroom Dataset by ULRIK THYGE PEDERSEN.

✅ It contains 8124 entries of mushrooms and their physical charecteristics with edible or poisonous label.

✅ Use a heatmap to analyse co-relation

✅ Analyse relations between other charecteristics and edibility

✅ Used model using intel oneAPI

Technologies Used

✅ Python 3.7 or higher

✅ Scikit-learn

✅ XGBoost

✅ Pandas

✅ NumPy

✅ Matplotlib

✅ Seaborn

Repository

https://github.com/arungeekay/Mushroom-Edibility-Decision-using-OneDAL