Results-driven Python Developer with strong expertise in data handling and management, specializing in offline-ready web applications built with Flask. Demonstrated success in developing a full-scale Part Lifecycle Management (PLM) system, architected around Excel as the primary data store. Skilled in implementing structured, multi-step workflows with precise control over data flow — including conditional updates, draft persistence, file path tracking, and data locking post-submission. Proven ability to design and manage complex data structures across multiple Excel sheets, ensuring accuracy, traceability, and role-specific accessibility. Adept at integrating backend logic, frontend interactivity, and automated document/email generation to build reliable, self-contained systems for local environments.
Core Features Authentication & User Management 8-Step Lifecycle Workflow Data Handling & Storage Logic Automated Email System Offline Packaging & Deployment Tech Stack Outcome
Engineered a robust, fully offline PLM (Part Lifecycle Management) system for managing mechanical part workflows using JPC (Job Process Card) numbers. Designed with Flask and Excel-backed storage, the system streamlines part tracking, inspection, documentation, and submission — all accessible through a browser-based interface on a local intranet.
Data Science Projects
1 -Zomato Sales Data Analysis : Performed various Data Analysis techniques on data set like Data Collection , Data Gathering ,Data Visualization and Type Casting using libraries Pandas,Numpy,Matplotlib and Seaborn to find hidden patterns and trends.
2- Loan Approval Prediction : Performed Data Collection , Data Cleaning and Data Visualization to find hidden patterns and also applies two Machine Learning Techniques which are Logistic Regression and Random Forest Classifier using libraries Numpy,Pandas,Matplotlib,Seaborn and SciKit Learn.
3-Covid-19 Data Analysis : Performed Descriptive Analysis and Statistical Analysis on data set by making different graphs like Line Graph , Scatter Plot , Bar Chart and Histogram and also performed Choropleth(Video) and WordCloud using Plotly and Matplotlib.
4-Soccer Player Price Prediction : A Data Science and Machine Learning Project in which the goal was to predict the price of a soccer player using multiple parameters using Linear Regression. Libraries & Tools used here are Pandas, NumPy, MatplotLib, Seaborn, SkLearn. We have performed several tasks like Data Cleaning, Data Pre-Processing, Data Visualisation, Data Insights, Machine Learning Model Building.
Started my journey as a volunteer/coordinator in CESA (Computer Engineering Student Association) and served as Coordination Head of Core Committee in CESA(2023-24).