Balla Sai Dinesh Mani Karthikeya

About

I am a passionate Artificial Intelligence and Machine Learning graduate with a strong interest in Python development, machine learning, and full-stack application development. I have hands-on experience working with Python, NumPy, pandas, Scikit-learn, Flask, React, SQL, and machine learning algorithms. Through academic projects, internships, and practical training, I have developed an understanding of data processing, model development, backend development, and building real-world applications. My technical interests include: Programming: Python, JavaScript, HTML, CSS Machine Learning: Scikit-learn, XGBoost, TensorFlow Web Development: Flask, React, REST APIs Databases: MySQL, PostgreSQL Tools: Git, GitHub, VS Code I am continuously working on improving my programming logic, problem-solving abilities, and technical knowledge through hands-on projects and practical learning. I am currently open to internship and entry-level opportunities where I can apply my skills, gain industry experience, collaborate with experienced professionals, and contribute to meaningful projects. I believe in continuous learning, consistency, and developing practical skills that create real-world impact.

Experience

• AI - ML Intern
  Technocolabs Softwares Inc
  Apr 2026 – May 2026

Education

• Bachelor of Technology
  Amrita Vishwa Vidyapeetham
  CSE-Artificial Intelligence · Oct 2022 – May 2026
• Qualification
  Sri Viswa Jr College
  MPC · 2020 – 2022
• Qualification
  Sri Chaitanya Techno School
  2019 – 2020

Skills

• SQL
• FastAPI
• Bootstrap
• Python
• Machine Learning
• NumPy
• Pandas
• scikit-learn
• React
• Flask
• NLTK

Projects

• Machine Learning-Based Cancer Classification
  Python, Scikit-learn, Pandas, NumPy

  Developed an end-to-end cancer classification pipeline by performing data preprocessing, feature engineering, feature selection, and feature scaling using Pandas, NumPy, and Scikit-learn. Trained and optimized multiple machine learning models including Logistic Regression, Decision Tree, Random Forest, SVM, and KNN using GridSearchCV and 5-fold Cross-Validation for improved predictive performance. Evaluated model performance using Accuracy, Precision, Recall, F1-Score, ROC-AUC, and Confusion Matrix, with visualizations created using Matplotlib and Seaborn for model interpretation.

• Notes Management System
  Flask, MySQL, Bootstrap

  Developed a full-stack Notes Management System using Flask, Python, and MySQL with secure user authentication, categorized note management, search, and complete CRUD functionality. Engineered advanced productivity features including a rich-text editor (CKEditor), AI-powered note summarization, PDF export, note sharing, dark mode, and recent activity tracking to enhance user experience. Implemented robust security and usability features such as email OTP verification, password recovery, trash and restore functionality, pin/favorite notes, responsive UI, and profile management.

Courses & certifications

• Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024] · Udemy · 2025
• TCS iON Career Edge - Young Professional · TCS iON · 2025
• Python Full Stack Trainee · Codegnan

🎯 Hobbies & interests

• badminton