Academic Project:- Cargo Booking Website
Academic Project:- Cargo Booking Website
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Cargo Booking Website | Web Application Project: Developed a full-stack cargo booking web application to simplify freight reservations and shipment management. The platform allows users to register, log in, book cargo shipments, choose services, make secure payments, and receive order confirmations in a structured and user-friendly workflow.
The application was built using ASP.NET (C#) for the frontend, Python for backend processing, and MySQL for database management. It includes modules such as Homepage, About Us, Services, User Authentication (Signup/Login), Booking, Payment Integration (UPI, Debit/Credit Card, Cash on Delivery), Order Confirmation, Testimonials, and Support.
Key features:
Secure user authentication and session management
Dynamic booking form with shipment details and pricing logic
Multiple payment options integration
Database-driven order tracking and confirmation system
Clean UI with a cargo-themed design and responsive layout
This project demonstrates my ability to design and develop a structured web application, integrate frontend and backend technologies, manage databases, and implement real-world business workflows in the logistics domain.
Omni-Channel Market Intelligence System
Omni-Channel Market Intelligence System
Project Title: Omni Market Intelligence (OmniMI) Database System
Project Title: Omni Market Intelligence (OmniMI) Database System
Overview: Developed a robust, end-to-end relational database schema designed for a modern retail environment. The OmniMI system integrates traditional sales data with digital footprints and competitive market intelligence to provide a 360-degree view of retail performance.
Overview: Developed a robust, end-to-end relational database schema designed for a modern retail environment. The OmniMI system integrates traditional sales data with digital footprints and competitive market intelligence to provide a 360-degree view of retail performance.
Technical Stack
Technical Stack
Database: SQL Server (T-SQL)
Tools: SQL Server Management Studio (SSMS), GitHub
Concepts: Schema Design, Data Normalisation (3NF), Integrity Constraints, Indexing, and Performance Tuning.
Key Features
Key Features
Relational Data Modelling: Designed 7 interconnected tables managing Customers, Products, Transactions, and Web Logs using Primary and Foreign Keys.
Omnichannel Integration: Built to track customer behaviour across multiple touchpoints: In-Store, Web, and Mobile App.
Business Intelligence Layers: Created specialised tables for Social Media Sentiment Analysis and Competitor Price Tracking to facilitate data-driven marketing strategies.
Automated Calculations: Implemented PERSISTED computed columns for financial accuracy in calculating order totals and line-item revenue.
Performance Optimisation: Included strategic indexing on high-traffic columns (e.g., customer_id, transaction_ts) to ensure fast query execution even as data scales.
Business Value:
Business Value:
This database allows a retailer to answer critical questions such as:
This database allows a retailer to answer critical questions such as:
"Which sales channel (App vs. Web) has the highest average order value?"
"How does our current pricing compare to competitors in specific regions?"
"Is there a correlation between social media sentiment scores and purchase frequency?"
Saas Retention And Revenue Leakage Tracker
Saas Retention And Revenue Leakage Tracker
Project Description: Developed a high-performance, dark-themed analytics dashboard for SaaS businesses to monitor financial health and customer lifecycle metrics. The application transforms complex subscription data into actionable insights through interactive visualisations, cohort analysis, and real-time risk alerting systems.
Project Description: Developed a high-performance, dark-themed analytics dashboard for SaaS businesses to monitor financial health and customer lifecycle metrics. The application transforms complex subscription data into actionable insights through interactive visualisations, cohort analysis, and real-time risk alerting systems.
Core Responsibilities & Achievements
Core Responsibilities & Achievements
Data Visualisation Engineering: Architected a dynamic MRR Movement Waterfall chart and Revenue Leakage donut charts to visualize growth vs. contraction, providing a clear 360-degree view of monthly revenue shifts.
Advanced Cohort Analysis: Built a month-over-month (MoM) Retention Matrix ($M+0$ to $M+5$) to track user stickiness and identify churn patterns across specific signup cohorts.
Real-time Risk Monitoring: Designed and implemented a Churn & Risk Alert system that flags high-impact accounts ($100-$200 MRR) based on triggers like failed payments, budget cuts, and pricing sensitivity.
KPI Management: Developed an executive summary layer tracking critical SaaS North Star metrics, including Net Revenue Retention (NRR), Gross Churn Rate, and Active Customer counts with period-over-period trend indicators.
UX/UI Optimisation: Crafted a "Dark Mode" first interface focused on high-contrast data readability and scannability for fast-paced decision-making environments.
Technical Skillset Utilised
Technical Skillset Utilised
Frontend: React.js / Next.js
Styling: Tailwind CSS / CSS Modules (Responsive & Accessible Dark Theme)
Charting: Recharts / D3.js (Waterfall & Donut Visualizations)
State Management: Handling complex JSON objects for cohort matrices and financial filtering.
https://github.com/Rebecca-r23/SaaS-Retention---Revenue-Leakage-Tracker
Automated 3-System Company Health Engine
Automated 3-System Company Health Engine
Project Title: Automated 3-Statement Financial Health & Valuation Engine. Overview: Developed a comprehensive, fully integrated financial modelling "engine" that automates the projection of a company's financial health and intrinsic value. The tool transforms raw historical data into a dynamic 5-year forecast, providing actionable insights through automated DCF valuation and executive dashboards.
Project Title: Automated 3-Statement Financial Health & Valuation Engine. Overview: Developed a comprehensive, fully integrated financial modelling "engine" that automates the projection of a company's financial health and intrinsic value. The tool transforms raw historical data into a dynamic 5-year forecast, providing actionable insights through automated DCF valuation and executive dashboards.
Key Responsibilities & Achievements:
Integrated Financial Modelling: Built a dynamic 3-statement model (Income Statement, Balance Sheet, Cash Flow) where all components are linked via circular logic—ensuring that changes in operations (e.g., revenue growth) automatically impact liquidity and debt requirements.
Automated Valuation Engine: Implemented a Discounted Cash Flow (DCF) framework using the Gordon Growth Method to calculate Enterprise Value, including automated sensitivity analysis for WACC and Terminal Growth rates.
Debt & Capital Structure Management: Designed a dedicated Debt Engine to track principal repayments and interest expense schedules, reflecting the real-time impact of financing on the company's bottom line.
Executive Data Visualisation: Created a high-level "Health Dashboard" using advanced Excel/Data techniques to visualize key performance indicators (KPIs) such as EBITDA margins, Free Cash Flow (FCF) trends, and valuation multiples (EV/EBITDA, P/E).
Scenario & Sensitivity Analysis: Engineered the model to handle "What-If" scenarios, allowing stakeholders to instantly see the impact of shifting macro-assumptions (Tax rates, OpEx % of Revenue, Capex intensity) on the company’s final valuation.
Technical Tools Used:
Advanced Financial Modelling: 3-Statement Integration, DCF Valuation, Debt Schedules.
Analysis: Sensitivity Analysis, Scenario Modelling, Financial Ratio Analysis.
Visualisation: Executive Dashboards, Trend Charting.
https://github.com/Rebecca-r23/Automated-3-Statement-Company-Health-Engine
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