How sifat192/mushroom-classification-app works
This Streamlit app lets a user choose a mushroom classifier, adjust its available hyperparameters, and inspect predictions and evaluation results. At startup, app.py loads mushrooms.csv, label-encodes its categorical columns, and creates a train/test split. The selected Logistic Regression, SVM, or Random Forest model is fitted and evaluated; users can also request confusion-matrix, ROC, and precision-recall plots or display the raw and split data. The README documents these capabilities; the sampled source shows their implementation in app.py.
Main entry points: app.py Streamlit App · mushrooms.csv Mushroom Data
Components 11
User Interface
- Streamlit App
app.py - Data Display
app.py
Data Preparation
- Mushroom Data
mushrooms.csv - Label Encoding
app.py - Train-Test Split
app.py
Classification
- Logistic Regression
app.py - Support Vector Machine
app.py - Random Forest
app.py
Evaluation
- Metric Scores
app.py - Metric Plots
app.py
Other components
- App User
Connections 10
- App User → Streamlit App: selects model
- Streamlit App → Mushroom Data: reads
- Streamlit App → Label Encoding: encodes columns
- Label Encoding → Train-Test Split: passes data
- Streamlit App → Data Display: renders on request
- Streamlit App → Logistic Regression: configures and fits
- Streamlit App → Support Vector Machine: configures and fits
- Streamlit App → Random Forest: configures and fits
- Streamlit App → Metric Scores: computes metrics
- Streamlit App → Metric Plots: draws requested plots