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agentic-ai-email-processing-system-langGraph
September 24, 2025 – September 24, 2025
Agentic AI System to process legitimate emails
View Projectai-agent-answering-pdf-related-information
July 21, 2024 – August 1, 2024
The code will create an AI agent that leverages the capabilities of a large language model. This agent should be able to extract answers based on the content of a large PDF document and post the results on Slack using OpenAI LLMs
View Projectcomic-gen-data-viz
June 10, 2020 – June 10, 2020
Emotional data stories using Power BI Comics
View Projectanshukpal.github.io
April 4, 2020 – April 17, 2020
anshukpal.github.io — GitHub repository
View Projectcoronavirus-pandemic-viz
April 1, 2020 – December 8, 2022
Understand the growth of numbers globally in a intuitive way - how many recovery, how many confirmed cases , how many could not survive.
View Projectpytorch-practials
March 30, 2020 – March 30, 2020
Matrix Fundamentals and Model Building Techniques using PyTorch
View Projectstreamlit-exploratory-visualization
March 22, 2020 – June 6, 2020
Using streamlit, an open source app framework specifically designed for ML engineers working with Python. It allows you to create a stunning looking application with only a few lines of code.
View Projectrecommendation-engine
June 27, 2019 – December 8, 2022
Building a simple popularity and collaborative filtering model using Turicreate
View Projectann_churn_modelling
June 19, 2019 – June 21, 2019
Churn Modelling using Logistic Regression v/s Deep Learning
View ProjectClustering-Financing-Articles
January 17, 2019 – January 17, 2019
A financial institution news agency has collected 3000 news articles that relates to several matters of financial importance. Before analyzing these unlabeled news, it is only fair to try to partition them into some sort of logical groupings based on their similarities. The objectie of this code is to use appropriate unsupervised machine learning algorithm to form the news clusters based on their similarity. Prior to clustering, performing basic natural language processing steps such as stemming, tokenization and word vectorization for best results.
View ProjectCultural Fit Analysis
The candidate's projects show a strong inclination towards individual exploration and learning in the data science domain. The diversity of projects, from data visualization to AI agents and traditional ML, indicates a broad interest. However, the lack of team-based projects or contributions makes it difficult to assess collaboration or cultural alignment with a team-oriented environment. The projects align well with a Data Scientist role, demonstrating initiative in self-learning and application.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's project descriptions are concise, but no direct communication or collaboration examples are provided.