Pinned Repositories
book-recommendation-system
Implement a recommender system to suggest Books
ChatWorkSafeBC
deep-learning-specialization-coursera
Deep Learning Specialization by Andrew Ng on Coursera.
document_representations_tests
legal_analytics
This is Legal Data set to build a classification model for interesting cases.
ML-Bootcamp
NLP
NLP-with-Python
Scikit-Learn, NLTK, Spacy, Gensim, Textblob and more
NLP_syntactic_analysis
HMMs and Viterbi algorithm for POS tagging
predict_amazon_review_rating
The problem is to build a text classifier to predict rating based on the amazon review text.
shishirkmr's Repositories
shishirkmr/book-recommendation-system
Implement a recommender system to suggest Books
shishirkmr/legal_analytics
This is Legal Data set to build a classification model for interesting cases.
shishirkmr/ChatWorkSafeBC
shishirkmr/deep-learning-specialization-coursera
Deep Learning Specialization by Andrew Ng on Coursera.
shishirkmr/document_representations_tests
shishirkmr/ML-Bootcamp
shishirkmr/NLP
shishirkmr/NLP-with-Python
Scikit-Learn, NLTK, Spacy, Gensim, Textblob and more
shishirkmr/NLP_syntactic_analysis
HMMs and Viterbi algorithm for POS tagging
shishirkmr/predict_amazon_review_rating
The problem is to build a text classifier to predict rating based on the amazon review text.
shishirkmr/ProphetTimeSeries
shishirkmr/pyarmor
A tool used to obfuscate python scripts, bind obfuscated scripts to fixed machine or expire obfuscated scripts.
shishirkmr/Quora-Insincere-Questions-Classification
An existential problem for any major website today is how to handle toxic and divisive content. Quora wants to tackle this problem head-on to keep their platform a place where users can feel safe sharing their knowledge with the world. Quora is a platform that empowers people to learn from each other. On Quora, people can ask questions and connect with others who contribute unique insights and quality answers. A key challenge is to weed out insincere questions -- those founded upon false premises, or that intend to make a statement rather than look for helpful answers. In this competition, Kagglers will develop models that identify and flag insincere questions. To date, Quora has employed both machine learning and manual review to address this problem. With your help, they can develop more scalable methods to detect toxic and misleading content. Here's your chance to combat online trolls at scale. Help Quora uphold their policy of “Be Nice, Be Respectful” and continue to be a place for sharing and growing the world’s knowledge.
shishirkmr/telecom_churn_analysis
This project is to identify usage based churn prediction using pca and logistic regression
shishirkmr/TimeSeries
shishirkmr/Toxic-Comment-Classification-Challenge
iscussing things you care about can be difficult. The threat of abuse and harassment online means that many people stop expressing themselves and give up on seeking different opinions. Platforms struggle to effectively facilitate conversations, leading many communities to limit or completely shut down user comments. The Conversation AI team, a research initiative founded by Jigsaw and Google (both a part of Alphabet) are working on tools to help improve online conversation. One area of focus is the study of negative online behaviors, like toxic comments (i.e. comments that are rude, disrespectful or otherwise likely to make someone leave a discussion). So far they’ve built a range of publicly available models served through the Perspective API, including toxicity. But the current models still make errors, and they don’t allow users to select which types of toxicity they’re interested in finding (e.g. some platforms may be fine with profanity, but not with other types of toxic content). In this competition, you’re challenged to build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate better than Perspective’s current models. You’ll be using a dataset of comments from Wikipedia’s talk page edits. Improvements to the current model will hopefully help online discussion become more productive and respectful. Disclaimer: the dataset for this competition contains text that may be considered profane, vulgar, or offensive.
shishirkmr/Visualizations