🎓 B.Tech Industrial & Production Engineering | IIT Delhi Project Associate | Ex-Intern @ TACO, IIT KGP

📊 Data Analyst | Operations Research | Supply Chain Analytics | Optimization

I work at the intersection of data, optimization, and business decision-making — turning raw numbers into actionable insights and building models that solve real-world industrial and operational challenges.

My work spans forecasting, mathematical optimization, simulation, machine learning, and NLP, applied to domains like transportation, supply chain, inventory planning, and intelligent systems.

⚡ Currently working on EV battery SOH estimation (ML + thermal data) at IIT Delhi under a Hyundai-funded project, and urban spatial analysis for Tamil Nadu's Regional Plan 2047 at IIT Kharagpur.

💡 I don't just analyze data — I build systems that make decisions smarter.

Role Fit: Data Analyst · Machine Learning · NLP

• Built a binary sentiment classifier trained on 1.6 million tweets from the Sentiment140 dataset (Kaggle).
• End-to-end NLP pipeline: raw tweet → cleaning → stopword removal → stemming → TF-IDF vectorization → Logistic Regression.
• Achieved 77.6% test accuracy (79% training accuracy) and deployed the model as a live Streamlit web app.
• Tools: Python NLTK Scikit-learn Pandas Streamlit

🔗 https://github.com/raushan-yadav/twitter-sentiment-analysis

Role Fit: Data Analyst · Python Developer

• Scraped and structured data of the largest US companies by revenue from Wikipedia using Python.
• Used Requests and BeautifulSoup to extract HTML tables and Pandas to clean, process, and export the data.
• Output: Production-ready CSV dataset with columns — Rank, Company Name, Industry, Revenue, Revenue Growth, Employees, and Headquarters.
• Tools: Python BeautifulSoup Requests Pandas Google Colab

🔗 https://github.com/raushan-yadav/wikipedia-company-web-scraper

Role Fit: Operations Research · Reinforcement Learning · Smart City Analytics

• Comparative study of Mixed Integer Linear Programming (MILP) vs Deep Q-Learning (DQN) for traffic signal control at a 4-way intersection under Indian mixed-traffic conditions.
• Built a custom traffic simulation environment modeling heterogeneous vehicles (cars, bikes, buses, autos) with PCU values, variable arrival patterns, and queue dynamics.
• Implemented Webster's formula for MILP-based fixed-time optimization and trained a DQN agent (PyTorch) with experience replay and target networks for adaptive control.
• RL agent achieved ~15–20% reduction in average delay per vehicle by dynamically adjusting green phases based on real-time queue lengths and waiting times.
• Tools: Python PyTorch PuLP SUMO Reinforcement Learning (DQN) NumPy Matplotlib Google Colab

🔗 https://github.com/raushan-yadav/Traffic-Signal-Optimization

Role Fit: Operations Research · Optimization Analyst · Transportation Analytics

• Developed a freight-prioritized railway scheduling model for Chakradharpur Division, South Eastern Railway (SER).
• Formulated a Mixed Integer Linear Program (MILP) using Working Time Table (WTT) data to minimize priority-weighted delays under single-line and junction constraints.
• Tools: Python PuLP CPLEX Gurobi Pandas NumPy Matplotlib QGIS LaTeX

🔗 https://github.com/raushan-yadav/SER_Division_Scheduling-and-Optimization-using-OR-Model

Role Fit: Data Analyst · Business Analyst · Supply Chain

• Built time-series forecasting models (Prophet) to predict monthly sales trends.
• Developed Power BI dashboards to support inventory and product-level planning.
• Tools: Python Prophet Pandas Power BI Excel

🔗 https://github.com/raushan-yadav/Superstore-Sales-Forecast-Inventory-Intelligence

Role Fit: Operations Research · Industrial Engineering

• Optimized warehouse operations using simulation-based process modeling.
• Achieved a ~4 hours reduction per shift through worker allocation and layout redesign.
• Tools: Python Simulation Modeling Pandas Matplotlib Excel

🔗 https://github.com/raushan-yadav/Warehouse_Network_Optimization

Role Fit: Business Analyst · Supply Chain Analyst

• Analyzed OT%, IF%, and OTIF% service-level KPIs using Power BI.
• Identified logistics bottlenecks impacting customer satisfaction and contract renewals.
• Tools: Power BI Excel DAX SQL

🔗 https://github.com/raushan-yadav/Data-Driven-Supply-Chain-Performance-Analysis

Role Fit: Data Analyst · Business Analyst

• Designed a 3NF normalized relational database for restaurant transactions.
• Performed SQL-based analysis on revenue trends and customer behavior.
• Tools: SQL MySQL Excel ER Diagram

🔗 https://github.com/raushan-yadav/Restaurant-Database-Management-System

Role Fit: Operations Research · Optimization Analyst

• Developed a Python framework for resource-constrained task scheduling.
• Applied OR concepts including precedence constraints, capacity limits, and Gantt visualization.
• Tools: Python PuLP Pandas Matplotlib NumPy

🔗 https://github.com/raushan-yadav/Resource-Allocation-and-Task-Scheduling

Programming & Scripting Python SQL MATLAB LaTeX

Python Libraries Pandas NumPy Matplotlib Seaborn Scikit-learn PyTorch NLTK BeautifulSoup Requests

Machine Learning & NLP Logistic Regression TF-IDF Sentiment Analysis Text Preprocessing Streamlit

Optimization Solvers CPLEX Gurobi PuLP MILP Linear Programming Integer Programming

Analytics & BI Power BI Tableau Excel

Operations Research Optimization Scheduling Simulation Queueing Theory Reinforcement Learning DQN

Supply Chain OTIF Demand Forecasting Inventory Planning KPI Analysis

Version Control Git GitHub

📧 Email: raushanyadav1001@gmail.com 📞 Phone: +91 8789724223 🔗 Linktree: https://linktr.ee/raushan_yadav

⭐ If you find my work useful, feel free to star the repositories.