Data Science Professional
Data Science Master's student at VIT-AP | Published IEEE Researcher | TCS Digital (AI Cloud) Selectee | Specializing in ML, Deep Learning & Knowledge Graphs
Currently exploring LLMs and RAG pipelines | Looking for Data Science / ML Engineer roles (2026)
(+91) 7888873321 • abhishek.k.jalandhar@gmail.com • Amaravati, India
Download Resume / CVTCS National Qualifier Test (NQT) - A National Level Exam Conducted by TCS. Crack Aptitude and coding round and call for TCS Prime role interview. Finally selected for TCS Digital Role (AI Cloud).
Designed the architectural workflow for stock analysis software, integrating real-time data ingestion, prediction modeling, and live graph visualizations, while processing and incorporating social media data.
Lead insurance presentations and community building, collaborative reviewing of insurance policies, and presented policies for education and women's benefits.
Findings: Identified optimal combinations of hyperparameters to significantly improve CNN convergence speed, stability, and generalization on benchmark image datasets.
Convolutional Neural Networks (CNNs) are changing various fields, particularly computer vision. However, achieving optimal CNN performance relies heavily on the selection of hyperparameters like learning rate, optimizer, and activation function. This research empirically investigates how different combinations of these factors collectively impact CNN training dynamics and performance. Through systematic experimentation, we aim to uncover the complex relationships that emerge when these hyperparameters are considered in concert, revealing how dynamic parameter changes affect the overall performance of the model.
Technologies: PyTorch, TensorFlow, OpenCV, NumPy, Scikit-learn
Web Crawling, Graph Networks, Knowledge Management
Intelligent Knowledge Graph for Data Science Learning. Designed and developed a specialized knowledge graph platform using web crawling techniques, implementing a log-weighted graph network with token system for concept classification. Crawled over 50 pages and currently mapped up to 20 concepts, with architecture ready to exceed this in the future.
Python, TensorFlow, PyTorch, Pandas, NumPy, Matplotlib, Seaborn
No-Code Data Science Platform with drag-and-drop modules for data analysis, preprocessing, and model building. Integrated with TensorFlow, PyTorch, Pandas, and NumPy.
R, Shiny, Statistical Analysis
No-Code Statistical Analysis Tool built in R with interactive features for statistical analysis and data visualization.
Python, Linear Regression, Yahoo Finance, Sklearn
Stock price prediction model using a KNN regressor in machine learning. Achieved 77% accuracy on 5 years of OHLVC historical stock data.
Python, BeautifulSoup, yfinance, Streamlit
Website for stock price analysis and comparison between two stocks.
Python
R
SQL
Java
TensorFlow
PyTorch
Scikit-learn
Tableau
Pandas
NumPy
MongoDB
Neo4j
MySQL
Git
AWS
Oracle Cloud
CGPA: 8.69, Focus areas: data preprocessing, statistical analysis, machine learning, deep learning
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Call: (+91) 7888873321 Email: abhishek.k.jalandhar@gmail.com