Data • AI/ML • Backend Engineering

I build production-style data systems, ML pipelines, and analytics platforms that turn raw data into actionable insights.
Currently pursuing my Master’s in Information Systems and focused on becoming a Data Engineer | AI/ML Platform Engineer at the intersection of data engineering, backend systems, and machine learning.

• Strong in SQL, Python, and system design
• Love building end-to-end projects (API → DB → ML → Deployment)
• Interested in Data Engineering, ML Platforms, and Analytics Infrastructure
• Teaching Assistant for Business Intelligence & Analytics
• Always learning, always shipping

Python • JavaScript • SQL • R • C

ETL pipelines • Data cleaning & transformation • Data modeling • Analytics-ready schemas • Warehousing concepts

FastAPI • RESTful services • API design • Backend architecture • Git • Linux

PostgreSQL • Oracle • Relational design • Indexing • Query optimization

AWS (S3, EC2, IAM) • Docker • Jenkins • GitHub • CI/CD basics

pandas • NumPy • scikit-learn • Machine Learning • Feature engineering • Model evaluation • Tableau • Power BI

Data Structures & Algorithms • OOP • Problem solving • System thinking

Production-style financial analytics & ML prediction system

• Built FastAPI backend serving ML predictions
• Designed PostgreSQL data warehouse for stock history
• Implemented feature engineering + model training pipelines
• Dockerized full stack with reproducible deployments
• REST APIs for real-time forecasting

Tech: Python, FastAPI, PostgreSQL, Docker, ML
🔗 Repo: https://github.com/Anusha3997/FinSight

End-to-end machine learning pipeline for movie revenue classification

• Cleaned + engineered features from raw movie datasets
• Trained multiple models with cross-validation & hyperparameter tuning
• Feature importance + confusion matrix analysis
• Modular scripts for training, evaluation, and inference
• Reproducible ML workflow

Tech: Python, Scikit-learn, Pandas
🔗 Repo: https://github.com/Anusha3997/Movie-Success-Prediction

Retail database design + business analytics project

• Designed normalized relational schema (3NF)
• Created PK/FK constraints, indexes, and integrity rules
• Wrote complex SQL queries (joins, aggregations, window functions)
• Built analytics for revenue, customers, and product insights
• Dockerized PostgreSQL setup

Tech: PostgreSQL, SQL, Docker, ER Modeling
🔗 Repo: https://github.com/Anusha3997/Tungsten-Gaming-Store

Exploratory data analysis on global aviation incidents

• Data cleaning and transformation
• Time-series trend analysis of crash frequency
• Root-cause and survival insights
• Visual dashboards and statistical summaries

Tech: Python, Pandas, Matplotlib
🔗 Repo: https://github.com/Anusha3997/Airplane-Crash-Analysis

Business intelligence & behavioral analytics project

• Analyzed smart device usage patterns
• Built user activity metrics and KPI dashboards
• Generated insights for customer engagement strategy
• End-to-end analytics workflow

Tech: R, SQL, Tableau
🔗 Repo: https://github.com/Anusha3997/Bellabeat-Fitness

• Large-scale data analysis projects using Python and SQL (health, aviation datasets)
• SQL-backed web applications focusing on data consistency and secure transactions
• BI dashboards built with Tableau and Power BI for KPI-driven decision-making

• IBM Machine Learning with Python
• Google Data Analytics (Coursera)
• IBM Generative AI – Prompt Engineering Basics

LinkedIn: https://www.linkedin.com/in/anusha-nagula-11611b4b