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python_folium_example

Example showing how to generate a map with markers, custom markers, circle markers, vega visualizations, Geojson and choropleth maps

rathnas

search-your-pdf-app

Search Your PDF App using Langchain, ChromaDB, Sentence Transformers, and LaMiNi LM Model. This app is completely powered by Open Source Models. No OpenAI key is required.

statistical-tests-in-time-series

To understand time series there are many tests involved. Some of them are Augmented Dickey-Fuller Test which will let us know if data is stationary or non stationary. Second, Granger’s Causality Test.This test will help to conclude if there is any causality involved between variables. Eg: Y is caused because of X. Lastly, Durbin Watson Statistic.This test will let us know if there is any autocorrelation involved by looking into its residuals/lag residuals.

svm-kernal-selection-and-optimization

This effort was to check which kernals were the best performing for non linear data set. Comparison is for linear,RBF and Polynomial kernals.

testing_github

the-school-of-ai-session-6-assignment-lohith0501

the-school-of-ai-session-7-assignment-lohith0501

topic-modeling-using-lda-nlp

Topic modeling is one of the way to extract keywords in a document. This can be primarily implemented using 3 methods such as LDA (Latent Dirichilet Allocation), NMF , LSI. Here i am using LDA for analyzing a text document and extracting its keywords. Here i have used pyLDAVis library for visualization which i felt is the best for visualizing LDA models.

transformers

🤗Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.

udacity-python-course

Collection of all my code submissions for the Udacity intermediate Python course

us-employee-analytics-using-nlp-

This was an effort to cleanse the data using TF-IDF for text parsing and feature engineering

web-development-for-ml-and-dl-using-django

All my practice work on Django for building web apps for Machine learning.

web-scraping-using-scrapy-and-mongodb

These codes will help to extract data from websites

word2vec-model-nlp

Word embedding is useful to create important features for vector representation. Here i have used word2vec model with skipgram model to train on sentences and represent them in vector space.