THEJUS P K

OBJECTIVE // PROFILE_LOG

B.Tech undergraduate in Artificial Intelligence and Data Science with hands-on experience across end-to-end machine learning projects spanning deep learning, NLP, and Python-based data analysis. Known for analytical thinking and practical exposure to real-time streams.

INTERN DESIRED AI / ML / PYTHON
CURRENT CGPA 7.2 / 10
ENROLMENT 2023 - 2027
RECOGNIZED_DOMAINS
Machine Learning Classification, Supervised workflows
01
Deep Learning TensorFlow, Keras, LSTM
02
NLP Architecture TF-IDF, SVM Sentiment pipelines
03
RESUME
COGNITIVE NODE LIVE STREAMS_ACTIVE: 3
SCROLL TO EXPLORE

TECHNICAL SKILLS // MATRIX

Skill Arsenal

LANGUAGES & TOOLS

Python Java C SQL HTML Git GitHub Jupyter Google Colab VS Code

DEEP LEARNING

TensorFlow Keras LSTM LSTM Autoencoder Neural Networks Anomaly Detection

DEPLOYMENT & HARDWARE

Flask Streamlit REST API Arduino Serial Communication IoT Integration Real-Time Monitoring

MACHINE LEARNING

Scikit-learn Supervised Learning Classification Regression Feature Engineering Model Evaluation

NLP & DATA ANALYSIS

TF-IDF Sentiment Analysis Pandas NumPy Matplotlib Data Cleaning EDA

WORK EXPERIENCE // LOG

Field Operations

Machine Learning Intern

Neovent Innovations, Kannur

JUN 2025 — JUL 2025
  • Completed a 4-week ML internship covering data preprocessing, model development, model evaluation, and practical applications of AI techniques within a structured ML pipeline.

  • Applied supervised learning workflows on real-world datasets, covering data cleaning, feature engineering, model training, and performance evaluation using Python.

  • Collaborated with the team on documentation and testing of ML workflows, strengthening proficiency in dataset handling and Python-based predictive analytics.

PROJECTS // ARENA

Deployed Systems

PROJECT_01

LSTM-Based Multi-Sensor Anomaly Detection

Python TensorFlow Keras Arduino Flask Streamlit
  • AI-powered motor health monitoring using LSTM Autoencoder for real-time anomaly detection across 3 multi-sensor time-series streams (MPU6050).

  • Built real-time dashboards using Flask and Streamlit for reconstruction error visualization, live sensor data tracking, and predictive maintenance alerts.

  • Integrated Arduino-based LED alert system and tested/validated against live sensor streams for real-time performance monitoring.

PROJECT_02

Sentiment Analysis of Tweets

Python Scikit-learn TF-IDF NLP Streamlit
  • End-to-end NLP pipeline to classify tweet sentiment across 2 categories (positive/negative), covering text preprocessing, TF-IDF feature extraction, model training, and evaluation.

  • Implemented SVM-based classification model and deployed an interactive Streamlit web application delivering real-time sentiment predictions on unseen text data.

EDUCATION // CHRONOLOGY

Training Protocol

B.Tech in Artificial Intelligence and Data Science

Vimal Jyothi Engineering College, Chemperi, Kannur, Kerala

CGPA: 7.2 2023 — 2027

Higher Secondary Education (12th)

Chembilode Higher Secondary School

Score: 85% 2020 — 2022

SSLC (10th)

Chembilode Higher Secondary School

Score: 95.5% 2019 — 2020

CERTIFICATIONS // VERIFIED

Accreditations

MICROSOFT LEARN

Introduction to Machine Learning

Artificial Intelligence Fundamentals — IBM SkillsBuild

WINGSPAN / FORAGE

Python Fundamentals

Solutions Architecture Job Simulation

NEOVENT INNOVATIONS

Machine Learning Internship Certificate

LANGUAGES & SOFT SKILLS

English
Hindi
Malayalam
Teamwork Communication Problem-Solving Decision-Making Analytical Thinking Attention to Detail Time Management