
My Portfolio and Certifications
Education:
B. Tech in Chemical Engineering - From IIT Madras
B.S in Data Science and AI - From IIT Madras
Key Skills:
AI/ML
Data Structure and Algorithm
Software Engineering
Python
Advanced Mathematics
Statistics
Various AI Tools - Tensorflow, etc
Various Analytics Tools - Numpy etc.
Competitive Programming Profile:
My Codeforces platform profile - http://codeforces.com/profile/meetarmman
Learning & Certification Profile (From Coursera)
Applied Machine Learning using Python (From University of Michigan) - Completed.
IBM Data Science Professional Certification - In-progress
Foundation of Data Structure and Algorithm (From University of Colorado) - In-progress
Mathematics for Machine Learning (From Imperial College of London) - In-progress
Summer/Fall Internship:
I am happy to announce that last fall I have done a two months of internship with Calibo India.
During my internship at Calibo, I contributed to the development of its data platform, focusing on enhancing its out-of-the-box algorithm capabilities. This platform enables users to apply pre-built machine learning algorithms to solve diverse business challenges efficiently. My primary role involved implementing key supervised learning algorithms, including Linear Regression, Naïve Bayes Classifier, and Decision Tree, to strengthen the platform’s analytical capabilities. Calibo’s platform is a fully integrated, cloud-based, self-service digital solution designed to empower enterprises by simplifying and accelerating software development and data engineering. It streamlines the journey from ideation to productization through an advanced technology stack, automation, and orchestration capabilities
Self-Led Projects:
A. Chatbot for Question Answering (NLP Project) – In Progress
Technologies: Python, BART Transformer, Seq2Seq, Luong Attention, Google NQ Corpus
Designing and developing an NLP-based chatbot using a Seq2Seq architecture with Luong Attention mechanism.
Leveraging Facebook's bart-base transformer and Google’s Natural Questions dataset.
Implemented advanced data cleaning and preprocessing techniques (regex, LanguageTool) to handle HTML tags, special characters, and typos.
Structured and tokenized QA pairs with padding for model training.
Currently evaluating the model using BLEU and ROUGE metrics.
Key focus areas: attention-based learning, data preparation, and transformer-based modelling.
Platform: Kaggle
B. House Price Prediction using Machine Learning
Technologies: Multiple Linear & Lasso Regression | Python, Scikit-learn, Pandas, NumPy | Platform: Kaggle
Developed a regression model to predict house prices using real-world housing data based on features like location, size, and number of rooms.
Built an end-to-end ML pipeline: data cleaning, feature engineering, imputation (SimpleImputer), scaling (StandardScaler), and encoding.
Trained and evaluated Multiple Linear Regression and Lasso Regression models.
Achieved RMSE: 21,407.10 and R²: 0.9498 with Lasso after hyperparameter tuning.
Addressed data skewness through log transformation and optimized Lasso alpha to improve model generalization.
Strengthened understanding of regression techniques, pipeline creation, and model evaluation metrics.
Area of Interest:
Deep Learning
Computer Vision
Data Science
Quantum Computing
Advanced Mathematics
Feedback
Here are few feedback shared by some Collaborators, Seniors and Industry Leaders with whom I got an opportunity to work or share thoughts or collaborate. Really thankful for your feedback and thoughts.
Neuronforge is a transformative platform for innovation and insightful AI explorations.
Alex Johnson
San Francisco
The creativity and technical prowess at Neuronforge has inspired my own projects immensely; a fantastic resource for anyone interested in AI and cognitive technologies.
Maria Lee
New York
