Subrat Pati's Projects
A curated list of references for MLOps
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
AWS userdata everytime run update issue solve
🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
An attention-based Recurrent Neural Net multi-touch attribution model in a supervised learning fashion of predicting if a series of events leads to conversion (purchase). The trained model can also assign credits to channels. The model also incorporates user-context information, such as user demographics and behavior, as control variables to reduce the estimation biases of media effects.
Constructing a Consumer Purchase Intention Index Using Twitter Data Mining and Sentiment Analysis
Analytical Web Apps for Python, R, Julia, and Jupyter. No JavaScript Required.
Collection of useful data science topics along with code and articles
Source code accompanying book: Data Science on the Google Cloud Platform, Valliappa Lakshmanan, O'Reilly 2017
Code for the paper "Data Feedback Loops: Model-driven Amplification of Dataset Biases"
An implementation of our CIKM 2018 paper "Deep Conversion Attribution with Dual-attention Recurrent Neural Network"
A playbook for systematically maximizing the performance of deep learning models.
John Hopkins course
:art: Diagram as Code for prototyping cloud system architectures
This contains projects based on Algorithmic Marketing like Marketing Mix Modeling, Attribution Modeling & Budget Optimization, RFM Analysis, Customer Segmentation, Recommendation Systems, and Social Media Analytics
Unstructured Code with interesting analysis
GAM (Global Attribution Mapping) explains the landscape of neural network predictions across subpopulations
Code for our NeurIPS 2022 paper
Multi Touch Attribution: Simulation Code
Autocompletion with Deep Learning on Jupyter Notebook
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
A step-by-step tutorial to learn Data Science
Lime: Explaining the predictions of any machine learning classifier
The Language Interpretability Tool: Interactively analyze NLP models for model understanding in an extensible and framework agnostic interface.
Learn how to responsibly deliver value with ML.
R based marketing attribution and basic budget optimization using Markov Chains