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Streamlit web app using custom ML models (multiple linear regression and one-to-many multiclass kernel SVM) for predicting real estate prices; Scraping and analyzing real estate listings in Serbia
Phishing attacks have grown to be a big problem for people and businesses in the modern digital age.SMS messages are one of the most widely used channels for phishing attempts.This project seeks to investigate the application of cutting-edge machine learning and NLP methods for the identification of phishing SMS (smishing) messages.
streamlit is an open-source app framework for Machine Learning and Data Science teams. Create beautiful data apps in hours, not weeks. All in pure Python
Indian StockPrediction WebApp [See Master Branch] : Build this web application using streamlit& deployed on streamlit which predict stock trend using facebook/meta prophet algorithim ,getting data from yahoo finance
PixelMagic is a web application designed for image processing, enabling users to upload an image and apply various techniques such as Super Resolution and Background Removal. Built with Python and utilizing the Streamlit library, PixelMagic offers a user-friendly interface for enhancing images or removing their backgrounds with ease.
Este projeto consiste em uma aplicação Python para extrair dados de municípios brasileiros do Instituto Brasileiro de Geografia e Estatística (IBGE) e dados de casos de dengue do sistema InfoDengue. Os dados são então transformados e carregados em um banco de dados PostgreSQL para análise posterior e criação de um dashboard.
📊 DataXplorer – An interactive data analysis and visualization app built with Streamlit. Upload datasets, explore data, compute value counts, apply group-by operations, and generate various plots effortlessly.
This project is a web-based application built using Streamlit that predicts the species of an Iris flower based on user-provided features. It leverages a pre-trained machine learning model and feature scaling to make accurate predictions.
🚢 Predict Titanic passenger survival using Machine Learning! This project trains a Logistic Regression model on the Titanic dataset and integrates it with a Streamlit web app for interactive predictions. Includes full code, datasets, and an easy-to-run app.
This web app utilizing OpenAI (GPT-4) and LangChain LLM tools. Application includes an SQLite DB for login/authentication and message storage for later retrieval. Users can upload/embed their own PDF documents for chatbot reference. The user can then interact with a GPT-4 chatbot intended for the user's specified input, context, and scenario.