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kats

Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.

keras-resources

Directory of tutorials and open-source code repositories for working with Keras, the Python deep learning library

langchain-tutorials

Overview and tutorial of the LangChain Library

leetcode-questions-companywise

Contains Company Wise Questions sorted based on Frequency and all time

live-time-series

llamaindex

Simple ChatBot using llamaIndex

loan-delinquency---analytics-vidhya-hackathon

Loan delinquency - Analytics vidhya hackathon

machine-learning-with-python

Python code for common Machine Learning Algorithms

ml-hackathon-hdfc

ML Hackathon conducted by Hackerearth for HDFC

ml-model

ML model for various use cases along with AutoML

ml-nlp-projects

model-interpretability

How to interpret your machine learning model

nlp-recipes

Natural Language Processing Best Practices & Examples

notebooks_sparkml

ontogpt

LLM-based ontological extraction tools, including SPIRES

pandas-bokeh

Bokeh Plotting Backend for Pandas and GeoPandas

parkinson

Random forest

plotly

post-tuto-deployment

Build and deploy a machine learning app from scratch 🚀

privategpt

Interact privately with your documents using the power of GPT, 100% privately, no data leaks

prophet

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

pycaret

pyclassnotes

dectree

pygooglenews

If Google News had a Python library

pytorch-transformers

👾 A library of state-of-the-art pretrained models for Natural Language Processing (NLP)

salary-classification

scikit-learn-tips

:robot::zap: Daily scikit-learn tips

scipy-lecture-notes

Tutorial material on the scientific Python ecosystem

segmentation-clustering

Recency, Frequency, and Monetary are three behavioral attributes and are quite simple, in that they can be easily computed for any database that has purchase history, and are easy to comprehend, yet very powerful in their predictive ability.

spam-classification_nlp

NLP