A collection of apps powered by the LangChain LLM framework.

Embed Observable notebooks into Streamlit apps!

Gold-Price-forecasting In a personal endevaour to learn about time series analysis and forecasting, I decided to reserach and explore various quantitative forecasting methods.This notebook documents contains the methods that can be applied to forecast gold price and model deployment using streamlit, along with a detailed explaination of the diff…

A sample Streamlit web application for search queries using LangChain and SerpApi.

A Kurtosis package for Python data engineers, deploying a Jupyter notebook along with a configurable set of databases, and a visualization tool (Streamlit)

A sample Streamlit web application for summarizing text using LangChain and OpenAI.

A collection of apps and projects that interact with Google Cloud APIs.

Repository containing portfolio of data science projects completed by me for academic, self learning, and hobby purposes. Presented in the form of iPython Notebooks, and Python files in corresponding modules .

Submission reporsitory for SmartBridge Capstone project. A DL model to classify the given dataset - Autistic Spectrum Disorder screening.

Capstone project by Campusx in DSMP course.

Implementation of the fast neural style transfer algorithm on Keras. Includes Jupyter notebooks, python script and web app.

A reactive notebook interface for Streamlit

Complete Notebooks on how AnimeWorld dataset was created, EDA, and Recommendation Engine.

Convert Jupyter Notebooks to identical Streamlit web apps. A future drop-in replacement to the Jupyter Book project.

A sample Streamlit web application for summarizing documents using LangChain and Chroma.

A collection of Google Colab notebooks for beginners and enthusiasts alike.

Jupyter notebook and Streamlit application for Whisper model from OpenAI

Streamlit demo built around the official StyleGAN-nada colab notebook to experiment quickly with training and testing new models.

This project analyzes household transactions from 2,500 frequent shoppers over two years, including demographics and marketing history. It features a Streamlit dashboard with pages for Data exploration, demographic insights, product data, and sales data. It also has a notebook for data management and processing.

This is a web application that classifies iris flowers based on their sepal length, sepal width, petal length, and petal width. The app is built using Streamlit and the classification model is trained using Jupyter notebook.