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Name: Ramesh Kumar
Type: User
Company: NITIE
Bio: I am a research scholar in the area of industrial engineering
Location: Mumbai
Name: Ramesh Kumar
Type: User
Company: NITIE
Bio: I am a research scholar in the area of industrial engineering
Location: Mumbai
I'm Punith V T, diving into a 100-day data science immersion from Python fundamentals to real-world applications. This space will be a live documentation of my journey, where code meets curiosity. Let's connect, learn, and code together. Click ⭐ on GitHub to stay tuned for updates on my work!
"Unlock the ultimate beginner's guide to data science! Our curated repository is packed with ML, DL, NLP, and Python essentials, offering clear pathways and valuable insights for your journey. Dive into data analysis and AI effortlessly with best-in-class tools and tutorials tailored for beginners."
Explore my diverse collection of projects showcasing machine learning, data analysis, and more. Organized by project, each directory contains code, datasets, documentation, and resources. Dive in, to discover insights and techniques in data science. Reach out for collaborations and feedback.
This tutorial playlist covers data structures and algorithms in python. Every tutorial has theory behind data structure or an algorithm, BIG O Complexity analysis and exercises that you can practice on.
-Developed a supply chain network baseline MIP model for a glass manufacuterer with multiple products, manufacuting facilites, and production costs (Regular/Overtime) to find optimal product flow as per sourcing policies and capacity constraints. -To improve the service levels, developed a multi-objective MIP scenario model which finds the minmum number of warehouses to be built such that 80% of the demand is covered with in 500 miles of the nearest source. -Scenario model suggested to build 5 warehouses with their exact location and product flow information and was able to achieve reduction in transporatation cost by 19.75% with 80% demand served within 500 miles compared to 11% of demand within 500 miles in baseline model. -Coded in Python and performed optimization using Gurobi: pandas, dictionaries, loops, gurobi packages, csv package.
This repository contains Python codes for Regression (Simple/Multi Linear, Polynomial, Support Vector, Decision Tree, and Random Forest), Classification (Logistic, K-Nearest Neighbors, Support Vector Machine, Kernel SVM, Decision Tree, Random Forest) and Clustering (K-means, Hierarchical) models for various real world applications using packages such as Scikit-learn, NumPy, Pandas and Matplotlib.
This work explains how OR and ML in tandem can help us making a cost efficient decisions. I have used a Supply Chain Network Design use case to explain benefits of ML+OR together.
Machine Learning Cheatsheet 2024
Notebook with pyomo tutorial
A transport network optimization problem attempted as a part of the IGSA UW-Madison SUPPLY CHAIN HACKATHON - April 2020
A repository of Pyomo examples.
During the pandemic, the supply chains got highly disrupted and faced a new challenge to sustain service. We have proposed a mathematical model for managing supply chains in a post pandemic situation, also coined as “new normal”. We are trying to design a model and simulate different scenarios while optimizing the network to thrive and fulfill customer demand. The model has been supported with scenario analysis and illustrative examples a packaged drinking water supply chain. The objective is to minimize the supply chain operating cost with respect to the changes in capacity due to pandemic. The fill-rate has also been recorded as a performance matrix for the chain. Particle Swarm Optimization (PSO) has been used to optimize the objective function. This research will help supply chain practitioners and researchers to design networks and carry out study in risk management for pandemic or other similar outbreaks situations.
A collection of Pyomo examples
Config files for my GitHub profile.
A repository of Supply Chain Optimization Programs
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Open source projects and samples from Microsoft.
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Data-Driven Documents codes.
China tencent open source team.