This application is a Streamlit dashboard used for data inspection, normalization, and machine learning classification. The application allows users to upload a dataset, select features, choose a normalization method, and apply various classification models.
- Data uploading through the Streamlit sidebar.
- Data preprocessing with options to handle missing values:
- Do nothing (fills missing values with zero).
- Drop rows with missing values.
- Fill missing values with the column mean.
- Visualization of data via confusion matrix and ROC curve plots.
- Selection of features to include in the model.
- Normalization methods available:
- Z-Score Normalization
- Min-Max Normalization
- Integration of classification models:
- Random Forest
- AdaBoost
- Support Vector Machine (SVM)
- Decision Tree
- Start the Streamlit app by running
streamlit run app.py
in your terminal. - Use the sidebar to upload a CSV file and select the desired preprocessing and machine learning settings.
- Click "Run Classification" to train the model and view the results.