Data Science project: Poisonous/Edible Mushrooms Classifier

Data collection

The mushrooms dataset provided on uci depository will be used for the purposes of classification.

Data Exploration

This can helps us see the data differently before we apply machine learning methods to the data.

Learners: Rules based Classification

There are total of 8124 observations (rows) of mushrooms and 23 features (columns) in the dataset each representing a single mushroom. The first column is the target variable containing the class labels, identifying whether the mushroom is edible or not. The other 22 columns are the features that describe the mushroom in some observable way. For example, gill size is either represented by broad (b) or narrow (n), and veil color can be brown (n), orange (o), white (w), or yellow (y).

Model Training

Because the target variable contains discrete values, we'll need to train a classifier.

Model Evaluation

The model can be validated after we have the initial result set and try to improve it if the result is not desirable by the model.