📊 Machine Learning GUI Project (Streamlit App)

This project is an interactive Machine Learning GUI application built using Streamlit. It allows users to upload datasets and perform end-to-end machine learning workflows without writing code.

🔹 Key Features

Upload CSV or Excel datasets Choose model type: Classification, Regression, or Clustering Data preview and row sampling Comprehensive preprocessing tools: Missing value imputation (Simple, KNN, Iterative) Encoding (Label Encoding, One-Hot Encoding) Scaling (StandardScaler, MinMaxScaler) Outlier handling (Winsorization) Feature transformations (Log, Power, Polynomial) Feature selection and dimensionality reduction: RFE with Random Forest PCA

Machine learning models:

Classification:

Random Forest, Decision Tree, Logistic Regression

Regression:

Random Forest, Decision Tree, Linear Regression

Clustering:

KMeans, Agglomerative, DBSCAN Model evaluation with metrics and visualizations Interactive data visualization (Line, Bar, Box, Scatter, Pie, Distribution plots) Undo and reset functionality for each processing step

🔹 Technologies Used

Python Streamlit Pandas, NumPy Scikit-learn Matplotlib, Seaborn SciPy

🔹 Purpose

This project is designed to help students, data analysts, and beginners experiment with machine learning pipelines visually, understand preprocessing techniques, and evaluate models interactively.