PROJECT WORK

Duration: October 2024 – December 2024
GitHub Repository: https://github.com/komalvinayak/Ecommerce_Analysis
YouTube Demo: https://youtu.be/jAeH3v5VpAs

Overview
This project compares pricing and discount trends for electronics such as mobiles, headphones, and watches across Amazon, Flipkart, and Jiomart. Product data (name, price, discount) was collected using a Chrome extension and analyzed using Python libraries like Pandas and NumPy. An interactive dashboard built with Plotly Dash enables users to filter by product type, brand, and model, and explore insights through various visualizations.

Key Features

• Built an interactive dashboard with filters and multiple visualization types (line, bar, box plots).

• Compared platform-specific pricing strategies and discount patterns.

• Displayed raw data preview and platform-wise insights.

• Planned enhancements: real-time scraping and predictive analytics.

Technologies Used
Python, Dash, Plotly, Pandas, Numpy

🔗 Live Website: https://sites.google.com/view/rajan-ink-tattoo

GitHub Repository: https://github.com/komalvinayak/Rajan_ink_Website

Designed and built the official website for Rajan Ink Tattoo Studio, a professional tattoo studio. Utilizing Google Sites, I focused on creating a user-friendly experience to showcase their custom tattoo designs, services (including cover-ups and detailed art), and hygienic practices. The website includes information about the artist, studio location, and contact methods. This project demonstrates my ability to translate business needs into a functional and visually appealing online presence.

Key Skills Demonstrated: Web Design, Google Sites, Information Architecture, Visual Design, Client Communication (in understanding studio needs).

Duration: October 2023 – November 2023
GitHub Repository: https://github.com/komalvinayak/Educational_Analysis

Overview
This project focuses on analyzing data from various educational apps to identify those that are most popular, well-rated, and engaging. Key metrics such as app ratings, number of reviews, and install counts were evaluated using Python-based analysis and visualizations.

Key Features

• Cleaned and preprocessed raw app data by handling missing values and formatting numeric fields.

• Identified top-rated, most-reviewed, and most-installed educational apps.

• Performed sorting and filtering to create comparative insights.

• Created visualizations to highlight key app performance indicators.

Technologies Used
Python, Jupyter Notebook, Pandas, Matplotlib

GitHub Repository: https://github.com/komalvinayak/Hospital_analysis
Power BI Dashboard

Overview

This project focuses on optimizing hospital operations through data-driven decision-making using structured datasets from various hospital departments. It was developed during an industrial training program at Pisoft Informatics Pvt. Ltd. Key datasets include OPD, IPD, Lab, Inventory, and Staff Scheduling. The project leverages Power BI, Python, and Excel to generate actionable insights and support hospital administration.

Key Features

• Built dynamic Power BI dashboards with KPIs, filters, and visual trends.

• Performed descriptive, diagnostic, and prescriptive analytics on hospital operations.

• Identified department-wise revenue trends, peak operation hours, and staff workload issues.

• Suggested operational improvements like adding more OPD staff on Mondays and replacing high-cost suppliers.

• Integrated recommendations based on patient demographics and service usage.

Technologies Used

Power BI, Python (Pandas, NumPy, Matplotlib, Seaborn), DAX, Excel