Phongphat Bangkha

This project is part of the course Intelligent System 040613701, which is about creating models of Machine Learning and Neural network using Streamlit Framework.

Streamlit Home Page

GitHub Live Demo

Completed

Overview

This project was developed for my "Intelligent System" university course. It is an interactive web application built with Streamlit that allows users to experiment with live Machine Learning and Neural Network models. The app features two main modules: an ML section that classifies car types (using KNN, SVM, D3, and Ensemble models) based on user-inputted data, and an NN section that uses a Convolutional Neural Network (CNN) to classify images from various TensorFlow datasets. The project is deployed and accessible live on the Streamlit Community Cloud.

Tech Stack

Full-Stack : Streamlit,Python
Models-tool : TensorFlow,Pandas,Numpy,Matplotlib

Features

Interactive ML dashboard to classify car types using KNN, SVM, D3, and Ensemble models
User inputs for ML model selection and car data (e.g., Engine Size, Horsepower)

Interactive NN dashboard to classify data from TensorFlow datasets using a CNN

Live predictions by allowing users to select specific datasets and data points

A 5-page Streamlit application structure (Home, ML Demo, NN Demo, ML Description, NN Description)

Data sourced from Kaggle (for ML) and TensorFlow Datasets (for NN)