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vikasrkarjigi/README.md

πŸ‘‹ Hey there! I'm Vikas Ravikumar Karjigi

πŸŽ“ Master's in Data Science @ Illinois Institute of Technology (GPA: 4.0/4.0)
πŸ’Ό Ex-Boeing | Data Scientist | Data Analyst | Data Engineer | Machine Learning Enthusiast
πŸ“ Turning data into decisions β€” one pipeline, model, and dashboard at a time.


πŸš€ About Me

I'm a data enthusiast with 2+ years of experience in developing scalable platforms, automating ETL workflows, and creating intelligent solutions that deliver real-world impact. My projects and professional work span predictive analytics, real-time dashboards, machine learning, and cloud-based data engineering pipelines. I love solving complex data problems and turning raw data into actionable intelligence.

My experience spans:

  • 2+ years at Boeing crafting high-performance platforms with predictive analytics.
  • Projects that fuse data science, visualization, and cloud engineering.
  • A track record of performance boosts β€” from 98% faster processing to 25% improved query efficiency.

πŸ› οΈ Skills & Expertise

Area Tools & Tech
Languages Python, R, SQL, Bash, Java
ML & Analysis Pandas, NumPy, Scikit-learn, TensorFlow, Statsmodels
Visualization Power BI, Tableau, Matplotlib, Plotly, Seaborn
Cloud & Big Data AWS (S3, EC2, Lambda, Glue), Apache Kafka, Spark, Docker, Linux
Databases MySQL, PostgreSQL, MongoDB, DynamoDB
Other Tools Git, Excel, Jupyter, GitHub Actions

πŸ’Ό Experience Highlights

πŸ›« Boeing India β€” Programmer Analyst (2022–2024)

  • ⏱️ 98% faster processing via algorithmic optimization and parallelism.
  • πŸ“Š 25% boost in database performance through advanced SQL tuning.
  • πŸ” Resolved 500+ security issues and led team-wide secure coding initiatives.

🏐 Open Avenues Foundation β€” Data Science Consultant (2024)

  • Built an automated volleyball highlight system with OpenCV + SVM (88% F1 score).
  • Achieved 30% noise reduction using statistical smoothing techniques.
  • Delivered a complete video pipeline for real-time sports content.

πŸ” Spotlight Projects

πŸ’Ή Real-Time Stock Market Insights

A real-time data pipeline using Apache Kafka + AWS DynamoDB to analyze high-frequency stock trends.
πŸ“ˆ From ingestion to dashboard β€” 100% serverless.

⚰️ Forecasting U.S. Mortality (CDC Data)

SARIMA-based time series forecasting on U.S. mortality data (1999–2018).
πŸ“‰ RΒ² = 0.88 | SMAPE < 4% | Residuals pass all diagnostics.

🏐 AI Volleyball Highlights

Automatically generate highlight reels from full-match videos using ML + OpenCV.
🎬 Optimized for real-time deployment and professional output.

🏏 Cricket Analytics Dashboard

Scraped match data from 1000+ T20 games to generate interactive Power BI dashboards.
πŸ“Š Reduced decision time by 50% for selectors and analysts.


πŸ“ˆ GitHub Activity

Vikas's GitHub stats Top Langs


🌐 Let’s Connect!


β€œData is a story β€” I build the tools to tell it.”

Pinned Loading

  1. shell-fuel-blend shell-fuel-blend Public

    Solution for Shell.ai Hackathon 2025 – Fuel Blend Property Prediction using ML and data-driven modeling.

    Jupyter Notebook

  2. Automated-Sports-Content-Generation Automated-Sports-Content-Generation Public

    πŸ€–πŸ ML-powered system for automated volleyball highlight detection with smooth OpenCV-based video transitions.

    Python 2

  3. InquiryEngine_ai-powered-email-assistant InquiryEngine_ai-powered-email-assistant Public

    πŸ€– Automate email responses with Django and AIβ€”extracts inquiries, generates replies, and manages history with ease.

    Python

  4. realtime-stock-analytics-kafka realtime-stock-analytics-kafka Public

    πŸ“‘ Real-time stock streaming and analytics using Kafka, EC2, and DynamoDB with dynamic Python visualizations.

    Jupyter Notebook

  5. cricket-data-analytics-python-powerbi cricket-data-analytics-python-powerbi Public

    πŸπŸ“Š End-to-end cricket data analytics project using web scraping, Python, Pandas, and Power BI for interactive dashboards and insights.

    1

  6. elasticnet-coefficient-visualizer elasticnet-coefficient-visualizer Public

    Forked from Fall2024CS584/Project1

    πŸ§ πŸ“Š ElasticNet regression implemented from scratch in Python β€” ideal for multicollinearity and feature selection with visual performance evaluation.

    Python