Karthikeya Nadakuditi

Computer Science undergraduate · Software Development & Problem Solving

Vijayawada, Andhra Pradesh, India

@karthikeya_n

LinkedIn GitHub

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🎓 B.Tech in Computer Science and Engineering (Data Science) · Graduating 2027

About

Computer Science undergraduate with a strong academic foundation and projects in full-stack development and machine learning. Looking to start a career in a challenging environment where technical skills and collaboration can be applied to build impactful solutions.

Education

2027

B.Tech in Computer Science and Engineering (Data Science)

Prasad V. Potluri Siddhartha Institute of Technology

Data Science · 2023 – 2027

Data Science · 2023 – 2027
2023

Intermediate (MPC)

Narayana Junior Coolege, Vijayawada

Science · 2021 – 2023

Science · 2021 – 2023
2021

SSC

Swarna Bharathi Smart School, Vijayawada

2020 – 2021

2020 – 2021

Skills

<3 months

Oop Mse CSS3 Java React HTML5 Python FastAPI Node.js XGBoost Lightgbm Express.js JavaScript Tailwind CSS Data Cleaning Advanced DBMS Classification Decision Trees Machine Learning Computer Networks Linear Regression Feature Engineering Exploratory Data Analysis API Integration Understanding Data Structures and Algorithms

Tools / apps / platforms

<3 months

Git MySQL GitHub MongoDB Postman VS Code

Projects

Student Healthcare Prediction

Python, NumPy, Pandas, Scikit-learn, XGBoost, LightGBM, Matplotlib

Built a machine learning classification model to predict student healthcare outcomes by preprocessing data, encoding categorical features, and performing exploratory data analysis. Implemented and compared XGBoost and LightGBM classifiers using Stratified K-Fold Cross Validation, evaluating model performance based on classification accuracy to identify the best-performing algorithm.
Bengaluru House Price Prediction

ML Algorithms, numpy, pandas, scikit-learn

Developed a machine learning model to predict Bengaluru house prices by performing data cleaning, feature engineering, and outlier removal on housing data. Trained and optimized Linear Regression, Lasso Regression, and Decision Tree models using GridSearchCV with K-Fold Cross Validation, selecting the best model based on R2 Score and MSE.
Real-Time AI Crypto Market Analytics Platform

React.js, Node.js, Express.js, MongoDB, FastAPI, Socket.IO, JWT, Tailwind CSS, LLM

Developed a scalable full-stack crypto analytics platform providing real-time market data for 17,000+ cryptocurrencies, interactive charts, portfolio tracking, and AI-powered insights. Improved performance using WebSocket-based live updates, intelligent caching, and exchange API integration for real-time portfolio analytics.

🏆 Achievements & awards

Secured 2nd place in coding contest in Sitar2K26
LeetCode Rating: 1705
CodeChef Rating: 1482
Codeforces Rating: 858

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