Aspiring Machine Learning and Deep Learning practitioner with hands-on experience in Python, data analysis, and Natural Language Processing NLP. Skilled in building and evaluating machine learning models including Recurrent Neural Networks for text classification and predictive modeling. Experienced in data preprocessing, feature engineering, exploratory data analysis, and model evaluation. Passionate about applying data-driven approaches and AI techniques to solve real-world problems.
S S J University Almora
Computer Science
GIC Mansarinala Choura
Science (PCM)
Developed a sentiment analysis model to classify text reviews as positive or negative using Recurrent Neural Networks (RNN).
Implemented text preprocessing techniques including tokenization, padding, and sequence encoding to prepare data for model training.
Designed and trained an RNN-based architecture for effective sequence modeling and sentiment classification.
Improved model performance through hyperparameter tuning and optimization techniques.
Developed an end-to-end text summarization application using a fine-tuned pre-trained T5 Small Transformer model from the Hugging Face platform to generate concise and meaningful summaries from large text inputs
Built a scalable backend using FastAPI for efficient model inference and API handling.
Designed a responsive and user-friendly frontend using HTML, CSS, and JavaScript.
Implemented data preprocessing and cleaning pipelines using Python and Pandas.
Integrated PyTorch for model training, evaluation, and deployment.
Python, Pandas, PyTorch, Hugging Face (T5 Pre-trained Transformer), FastAPI, HTML, CSS, JavaScript
Performed exploratory data analysis on smart card customer transaction data
to identify usage patterns and behavioral insights. Implemented data cleaning, visualization, and statistical analysis to understand customer segmentation and trends.
Genrate Final Output and summary
This resume was shared via Resumeily