Akritisen Yadav

About

Computer Science undergraduate specializing in Artificial Intelligence with experience in machine learning, deep learning, and computer vision projects. Comfortable with Python, Java, SQL, TensorFlow, and PyTorch, with hands-on exposure to preprocessing, model training, and evaluation workflows.

Experience

• AI & ML Project-Based Intern
  HCLTech
  Jun 2026 – Present

  Completed an industry-oriented internship focused on Artificial Intelligence and Machine Learning applications. Worked on data preprocessing, feature engineering, model training, and model evaluation techniques. Applied machine learning algorithms to solve real-world business problems and predictive analytics tasks. Gained hands-on experience with the end-to-end machine learning workflow from data collection to deployment.

• Core Java Intern
  Internshala
  Jun 2024 – Jul 2024

  Built modular applications using Object-Oriented Programming principles. Utilized multithreading, collections, exception handling, and file handling. Enhanced debugging efficiency and reduced errors by 15%.

Education

• 2023 NI
  Bachelor of Technology in Computer Science and Engineering (Artificial Intelligence)

  Noida Institute of Engineering and Technology

  Computer Science and Engineering (Artificial Intelligence · Aug 2023

  Computer Science and Engineering (Artificial Intelligence · Aug 2023

Skills

Tools / apps / platforms

Projects

• Healthcare Recommendation System

  Created a recommendation system for doctor suggestions and preventive healthcare solutions. Increased user decision efficiency by 20%. Implemented personalized recommendation logic using machine learning models.

• Agro Intelli

  Designed a machine learning-based crop recommendation system using soil and weather datasets. Boosted prediction accuracy by 25% through data preprocessing techniques. Enabled data-driven decision-making for agricultural planning.

• Facial Emotion Detection System

  Built a real-time facial emotion detection system using CNN and computer vision techniques. Achieved 85% accuracy in emotion classification. Reduced inference time by 20% for real-time deployment. Employed image preprocessing and feature extraction methods.

• Explainable Recommendation System

  Engineered an AI-based recommendation system using user behavior data. Increased recommendation accuracy by 30% using optimized algorithms. Utilized Explainable AI techniques to enhance model interpretability. Performed data preprocessing and feature engineering for better performance.

🏆 Achievements & awards

• National Finalist – CASE Quest, IIT Roorkee (Top 5%)
• Smart India Hackathon Participant
• GeeksforGeeks Hackathon Participant