DrewErskine / clean_Water

Streamlit ML Classification App

Overview

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.

Features

• 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

How to Use

1. Start the Streamlit app by running streamlit run app.py in your terminal.
2. Use the sidebar to upload a CSV file and select the desired preprocessing and machine learning settings.
3. Click "Run Classification" to train the model and view the results.