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deepakachyutha/README.md

Deepak Battula

Machine Learning Engineer • AI Research Enthusiast • Data Science

LinkedIn • Portfolio • Email

About

Final-year Computer Science Engineering (Artificial Intelligence) student with interests in Machine Learning, Explainable AI, Reliability-Aware AI Systems, and Financial Machine Learning.

My work focuses on building interpretable and robust machine learning systems with applications in risk modeling and decision support.

Research Interests

  • Machine Learning Reliability
  • Explainable AI (XAI)
  • Decision Support Systems
  • Intelligent Transportation Systems
  • Financial Machine Learning

Featured Projects

Reliability-Aware Traffic Severity Prediction

Research framework for vehicular accident severity prediction using Random Forests, Relative Reliability Index (RRI), and explainable safety recommendations.

Technologies: Python, Scikit-Learn, Pandas, SHAP

IBM Employee Attrition Prediction

End-to-end machine learning pipeline for employee attrition prediction with class balancing and explainability.

Repository:
https://github.com/deepakachyutha/IBMEmployees-ML

Technologies: Python, Scikit-Learn, Pandas, SHAP, SMOTE

Titanic Survival Prediction

Predictive modeling project developed using the Kaggle Titanic dataset.

Repository:
https://github.com/deepakachyutha/Titanic-ML

Technologies: Python, Pandas, Seaborn, Scikit-Learn

AI-Powered Software Specification Generator

LLM-based system for generating structured software specifications, APIs, database schemas, and architecture documents.

Market Regime Detection & Forecasting

Ongoing research project exploring machine learning approaches for financial market state classification.

Technical Skills

Languages

  • Python
  • SQL

Machine Learning

  • Scikit-Learn
  • Feature Engineering
  • Model Evaluation
  • Explainable AI
  • Decision Trees

Data Analysis

  • Pandas
  • NumPy
  • Matplotlib
  • Seaborn

Tools

  • Git
  • GitHub
  • Jupyter Notebook

Experience

AI Application Developer Intern
Rejolt EdTech | Sep 2025 – Feb 2026

  • Conducted machine learning experimentation on traffic and accident datasets.
  • Developed predictive models for severity analysis.
  • Contributed to reliability-aware and explainable AI research.

Certifications

  • Siemens Data Science Master Program – AICTE EduSkills
  • Google AI-ML Virtual Internship – AICTE EduSkills

GitHub Statistics

"Interested in Machine Learning Engineering, Data Science, and AI Research opportunities."

Popular repositories Loading

  1. Titanic-ML Titanic-ML Public

    This Titanic Survical Prediction project applies Supervised Machine Learning to predict passenger survival from the Titanic dataset. It focuses on practical data preprocessing, binary classificatio…

    Jupyter Notebook

  2. IBMEmployees-ML IBMEmployees-ML Public

    A data science project that predicts employee attrition using real-world HR data. Tackles imbalanced datasets using SMOTE and Class Weights, explains predictions with SHAP and Permutation Importanc…

    Jupyter Notebook

  3. achived-portfolio achived-portfolio Public template

    Forked from timlrx/tailwind-nextjs-starter-blog

    ARCHIVED: This was my first attempt at building a portfolio with Next.js. This repository is no longer maintained. For my latest work and the active version of my portfolio, please visit the new re…

    TypeScript

  4. new-portfolio new-portfolio Public

    This is a website portfolio and digital base of Deepak Battula. This repository contains the code for my personal website, where I write about my projects in AI, data science and Machine Learning.

    TypeScript

  5. my-react-blog my-react-blog Public

    Full-stack blogging platform built with React, Flask, and MongoDB. Academic project demonstrating authentication, API integration, and modular architecture.

    Python

  6. A-Reliability-Aware-and-Explainable-Framework-for-Traffic-Severity-Prediction A-Reliability-Aware-and-Explainable-Framework-for-Traffic-Severity-Prediction Public

    Machine learning framework for traffic risk prediction and reliability-aware safety interpretation using large-scale accident data.

    Jupyter Notebook