Classifiyng mushrooms into poisonous and nonpoisonous

About the code

• Language used: Python
• Packages used: pandas, numpy, sklearn

About the data

• Data source: UCI ML https://archive.ics.uci.edu/dataset/73/mushroom
• 8124 data poitns
• 23 features

Process

1. Data cleaning:

   • Convert categorical features to dummy variables
   • Convert response to binary
   • Split data into groups manually (not using a package)

2. Train models:

   • Cross validation was used within GridSearchCV
   • Models trained: Random Forest, Support Vector Machine, XGBOOst, Neural Network

3. Assess model performance on test data

   • Performance assessed with: Accuracy, F1 and ROC_AUC