My version of SP 500 data analyzer
-
Updated
Jul 10, 2021 - Jupyter Notebook
My version of SP 500 data analyzer
Enables you to backtest or develop financial strategies. The application is built with streamlit and yfinance libraries, and it allows you to select the stocks you would like to inspect from a pre-defined list of stocks.
This is a repository which hosts my question answering engine which answers questions and extracts information using the CORD dataset and uses BERT NLP model
This streamlit app provides an object and method filter which allows the users to quickly get to the app functionality they are interested in.
End to End machine learning project on classification using cloud based deployment
Detection of hate messages in Youtube video comments with NLP and ML ensemble algorithms. Deployment of the ML trained model in a Streamlit application.
Churn Prediction model builder
Using an LLM to answer math word problems common in IRL. Program aided language modeling.
This application provides comprehensive assistance across different domains, from chat to nutrition and ATS.
Extracting the data from phone pe pulse and geo-visualization providing clear cut insights and core business areas and giving a comprehensive and user-friendly solution
A Streamlit-based application for detecting helmets, bikes, and recognizing number plates in a video stream. It uses the YOLOv3 object detection model for detecting bikes and helmets and a CNN model for helmet detection. Additionally, it recognizes number plates in real-time video.
Deep-Learning-Optimization-Algorithms-Streamlit-Application
The repository attempts to use `streamlit` to build a website to visualize the data. The `index.html` is used for wrapping the website under the assignment requirement.
a streamlit application to interact with arabidopsis plant obo for graphviz and other functions
Dashboard para análise de dados de acidentes aeronáuticos.
BardAI | API | ChatBot | Speech to Speech | Text to Text | Image to Text | Voices | Streamlit |EasyOCR |
This project is a machine learning application that uses natural language processing techniques to analyze text data and predict the emotions expressed within the text. The application is built using Python and several machine learning libraries, deploy through StreamLit
Demo Streamlit app to show stock price charts of a given company between specific dates.
Streamlit Sample - PIE Chart that loads data from external JSON | AppSeed
Domain : Manufacturing | Predicting Selling Price and Status(Won or Lost) using Regression and Classification Models
Add a description, image, and links to the streamlit-application topic page so that developers can more easily learn about it.
To associate your repository with the streamlit-application topic, visit your repo's landing page and select "manage topics."