Predictive Intelligence for SpaceX Falcon 9 Landing Success

Status: COMPLETE | Accuracy: 85.19% | Impact: Production-Grade ML Architecture

Engineered a comprehensive machine learning pipeline analyzing SpaceX Falcon 9 first-stage landing success with multi-model comparison and statistical optimization.

Technical Specifications:

• Integrated real-world SpaceX API data (90 launches, 187+ records)
• Implemented Decision Trees, Random Forest, and XGBoost with systematic evaluation
• Applied hyperparameter tuning and k-fold cross-validation for model optimization
• Discovered critical insight: +0.95 correlation between flight experience and landing success
• Generated interactive Plotly dashboards for stakeholder communication

Technologies: Python • Scikit-Learn • Pandas • NumPy • REST APIs • Machine Learning Pipelines

→ Repository

Advanced Physics Simulation System

Status: COMPLETE | Lines of Code: 5000+ | Theory Applied: Schrödinger Equation

Developed a sophisticated computational system solving quantum mechanics problems with verified accuracy against experimental data.

Technical Specifications:

• Implemented analytical and numerical solutions for particle-in-a-box systems
• Accurately predicted UV-Vis absorption spectra (0% error vs. experimental baseline)
• Numerically verified Heisenberg Uncertainty Principle across quantum states
• Created advanced visualizations for wave-particle duality and quantization effects
• Produced comprehensive documentation (13,000+ lines)

Technologies: Python • NumPy • SciPy • Matplotlib • Seaborn • Computational Physics

→ Repository

Financial Data Pipeline & Visualization Platform

Status: COMPLETE | Data Sources: Multi-API Integration | Visualizations: 6 Professional Dashboards

Built integrated financial data analysis platform demonstrating production-grade data engineering and API orchestration.

Technical Specifications:

• Yahoo Finance API integration with robust error handling and fallback mechanisms
• Web scraping pipeline using BeautifulSoup for multi-source data aggregation
• Advanced time-series analysis with volatility calculations and correlation studies
• Professional visualizations combining interactive Plotly and analytical Matplotlib
• Comprehensive data cleaning and transformation workflows

Technologies: Python • Pandas • NumPy • BeautifulSoup • Plotly • Matplotlib • REST APIs

→ Repository

Advanced Regression Pipeline

Status: COMPLETE | Dataset Size: 13,000+ records | Feature Insights: 8 Critical Drivers

Engineered end-to-end machine learning pipeline for residential property price prediction with advanced feature engineering.

Technical Specifications:

• Comprehensive data preprocessing and feature engineering workflows
• Trained Decision Trees, Random Forest, and XGBoost with systematic comparison
• Implemented cross-validation and hyperparameter optimization strategies
• Identified key price drivers: rooms, land size, building area, proximity to CBD
• Conducted feature importance analysis for actionable business insights

Technologies: Python • Scikit-Learn • Pandas • NumPy • Cross-Validation • Model Evaluation

→ Repository

··· ███▒▒▒ INITIALIZING_NEXT_PROTOCOLS ▒▒▒███ ···

• Oracle Cloud Infrastructure 2025 Certified Generative AI Professional (Oct 2025)
  • Specialization: RAG, Fine Tuning, Vector Databases, LangChain
• IBM Generative AI: Elevate Your Data Science Career (Nov 2025)
  • Credential ID: 8XLJ4LE0OJT1
• Columbia+ Prompt Engineering & Programming with OpenAI (Jul 2025)
  • Credential ID: 154800100 | Focus: LangChain, LLaMA, Advanced GenAI

• IBM Data Science Professional Certification (Nov 2025)
  • Credential ID: YYICIR3BM019
• Kaggle Intermediate Machine Learning (Nov 2025)
• Kaggle Intro to Machine Learning (Nov 2025)
• University of London Machine Learning for All (Oct 2025)

• IBM Python for Data Science, AI & Development (Aug 2025)
• Google Crash Course on Python (Nov 2025)
• Cisco Python Essentials 1 & 2 (Jul 2025)
• Infosys Springboard: Mastering Python (May 2025)

• Google Cloud Introduction to Large Language Models (Nov 2025)
• IBM Databases and SQL for Data Science (Sep 2025)
• MongoDB Basics for Students (Oct 2025)
• Infosys Springboard: Hands-On Version Control with Git (May 2025)

• McKinsey Forward Program (Sep 2025 - Present)
  • Focus: Analytical Thinking, Problem-Solving, Decision-Making
• Google Analytics Certification (Jul 2025)
• Deloitte Data Analytics Job Simulation (Jun 2025)

Remote | Sep 2025 - Present

Developing analytical thinking, problem-solving, and communication skills in data-driven decision-making contexts.

Skills: Critical Thinking, Analytical Reasoning, Communication, Leadership

GirlScript Summer of Code 2025 | Remote | Jul 2025 - Present

Selected contributor actively building features for multiple open-source projects with focus on backend Python logic and full-stack implementation.

Contributions: Python Scripting • Backend Logic • Frontend UI (HTML/CSS) • Open Source Collaboration

Bachelor of Technology (BTech), Computer Science Engineering
VIT Bhopal University | Jul 2025 - Jul 2029

• Specialization: Artificial Intelligence and Machine Learning
• Registration: 25BAI10187 | Class: B11+B12+B13
• Focus Areas: AI Systems, Generative AI, Python Development, Computational Methods

An engineer dedicated to translating raw data into actionable intelligence. Specializing in machine learning pipelines, generative AI systems, and data-driven decision making. Focused on building transparent, interpretable models that bridge the gap between complex algorithms and human understanding.

Currently exploring:

• Advanced transformer architectures and LLM fine-tuning
• Retrieval-Augmented Generation systems and knowledge integration
• Cloud-native ML infrastructure and scalable AI applications
• Ethical AI and responsible machine learning practices

English — Full Professional Proficiency
Hindi — Native Proficiency
Urdu — Native Proficiency
German — Limited Working Proficiency (Duolingo: 63)

• GitHub — github.com/shamiquekhan
• LinkedIn — linkedin.com/in/shamique-khan
• Email — [email protected]

Philosophy: Transparency in Technology
Approach: Data-Driven, Methodical, Iterative
Goal: Building intelligent systems that augment human capability

> STATUS: INITIALIZING_NEXT_PROTOCOLS
> READY_FOR_COLLABORATION
> ALWAYS_LEARNING

Last Updated: December 6, 2025

#MachineLearning #DataScience #AI #Python #GenerativeAI #GitHub #ArtsificialIntelligence #OpenSource #NeuralNetworks #DataEngineering