Name: Rudrendu Paul
Type: User
Company: San Francisco
Bio: I am an AI Expert and Applied ML industry professional with over 15 years of experience across multiple industries in the top Fortune 30 companies.
Location: San Francisco, CA (USA)
Blog: https://www.linkedin.com/in/rudrendupaul/
Rudrendu Paul's Projects
Hurst exponent evaluation and R/S-analysis in Python
K-Mean Clustering for grouping products with similar sales pattern from Global Sales Data
Directory of tutorials and open-source code repositories for working with Keras, the Python deep learning library
Regression classification in Healthcare Industry
Machine Learning for Algorithmic Trading, Second Edition - published by Packt
A booklet on machine learning systems design with exercises: https://huyenchip.com/machine-learning-systems-design/toc.html
This repository contains the code and explanation for market mix modelling technique in economics
Analysing the challenges and opportunities in Media Mix Modelling using sales and media spending time series data.
Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS
Source code accompanying O'Reilly book: Machine Learning Design Patterns
Explanation to key concepts in ML
A model explainability walkthrough
A playbook for systematically maximizing the performance of deep learning models.
Multiclass_LSTM
This repository contains Natural Language Processing Projects like Sarcasm Detection, Quora Insincere Questions Classification & Edgar Sentiment Analysis
Algorithmic Marketing based Project to do Customer Segmentation using RFM Modeling and targeted Recommendations based on each segment
[Book-2021] Practical MLOps O'Reilly Book
Data Mining for better resource allocation to better serve San Francisco 311 non-emergencies
Segmenting consumer based buying behavior and applying 80/20 rule to identify top customers/products/geographic locations driving 80% of total $ sales
Business setting up their recommendation system for first time without any product rating history, & Amazon/Netflix type of recommendation system after the website has collected significant product reviews
Python Data Science Handbook: full text in Jupyter Notebooks
Random Forest applied to predict Income Level
Random Forest applied to predict trend of S&P500 Index
iPython NOtebooks on Stats
Stock Price Prediction case study using Keras
SVM applied to predict Income Level