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During my Data Analytics internship at Burma Burma Restaurant & Tea Rooms, I identified that manually compiling, formatting, and auditing monthly statutory filings across 15+ outlets was consuming critical administrative hours and creating operational bottlenecks.
To solve this end-to-end, So, I built an HR Statutory Compliance Engine & Real-Time Notice Portal to automate the complete workflow. ⚙️📊
The platform brings together:
🔹 Microsoft SharePoint / OneDrive Integration
→ Fetches centralized payroll and employee datasets through APIs with retry handling for network failures and throttling.
🔹 Dynamic Schema & Rule Mapping Engine
→ Automatically maps inconsistent fields such as Emp Code, Empl_Code, and Code into a standardized structure.
🔹 Automated Statutory Form Generation
→ Generates regional compliance registers while preserving government template layouts, rotated headers, formulas, formatting, and row structures.
🔹 Excel Engineering with Python + OpenPyXL
→ Handles large identifiers, prevents scientific notation, applies formulas, manages text wrapping, and dynamically prepares data rows.
🔹 Real-Time Notice & Incident Portal
→ A Google Apps Script-based portal for tracking statutory notices, inspections, licenses, approvals, and closure status.
📈 Business Impact
⚡ < 5 seconds — document generation per outlet
📉 85%+ — reduction in manual processing effort
✅ 0% — template formatting corruption
📊 100% — centralized compliance visibility
☁️ Cloud-synchronized and audit-ready workflows
🛠️ Technology Stack
Python | Pandas | NumPy | OpenPyXL | Streamlit | Microsoft Graph API | SharePoint | OneDrive | Google Apps Script | Gmail | Excel Automation
What I particularly enjoyed about this project was solving the problem at the intersection of Data Analytics + Automation + HR Operations + Regulatory Compliance.
It reinforced an important lesson for me:
Good automation isn't just about reducing clicks it's about building a reliable process that people can trust.
This project strengthened my interest in building data-driven business automation solutions that create measurable operational impact.
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Proud to officially add the AWS Certified Machine Learning Engineer - Associate certification to my toolkit!
Building robust machine learning pipelines and scaling AI workloads securely on the cloud has always been a key focus of mine. Earning this certification has been a great deep-dive into mastering AWS ML infrastructure, MLOps best practices, and production-grade architectures.
Ready to bring these advanced capabilities to upcoming data science and engineering projects! Let's build something impactful. 💡💪
🔗 Verify Certificate:
Validation Number: 74c5808986a64b84addcaa9db725b8bd
Verification Link: https://aws.amazon.com/verification
#AWS #MachineLearning #AI #CloudComputing #DataAnalysis #MLOps
Congratulations on earning the AWS Machine Learning Engineer certification, Hazem khaled! Robust pipelines and cloud automation make all the difference for production-grade AI systems.
Most inbound bottlenecks aren't caused by low lead volume—they happen during triage.
When inquiries roll into a Google Sheet or CRM, operations teams often waste 10–15 minutes per submission reading through messy paragraphs, deciphering budget mismatch, and manually deciding who gets an immediate call vs. a standard rate sheet.
Instead of adding a bulky CRM add-on, I documented a lightweight 3-step automation pipeline built inside Make:
Intake & Extraction: An active listener triggers immediately upon a new submission, extracting core variables: client scope, target deliverables, timeline, and stated budget.
Context-Aware Evaluation (Gemini 2.5 Flash): The payload passes through an AI evaluation node with strict guardrails:
Evaluates budget against minimum agency criteria.
Assigns a triage score: Hot (immediate priority), Warm (nurture), or Cold (disqualified/template reply).
Generates a strict 15-word operational summary—no long paragraphs.
Determines an automated next step (e.g., routing to an account lead or flagging for asynchronous follow-up).
Data Return & Clean Pipeline: Appends the structured classification directly back to the master operational tracker, keeping the sheet filterable and clean.
The outcome: The sales and operations team can filter the entire board by priority status in two clicks, cutting first-touch response time to minutes without manually reading every single submission.
I’ve documented the full scenario map, execution logic, and data schemas below.
Curious how other agency operators and automation leads here handle their lead enrichment layer—are you doing AI scoring at the form level, or later in the CRM pipeline?:
For the budget gate, I'd keep an unstated budget separate from one explicitly below your minimum. Does the Make route ask for clarification when the amount or currency is missing, so a potentially suitable lead doesn't fall into Cold by default?
This interactive dashboard provides a comprehensive view of profit performance across multiple dimensions—time, segments, regions, categories, and sub‑categories.
This dashboard transforms raw business data into actionable insights by allowing stakeholders to quickly identify profitable areas, spot regional trends, and evaluate category performance. It’s designed for clarity, interactivity, and decision‑making support.
#This is an excellent Profit Dashboard which is made in MS Excel with multiple creative functions help in understanding Monthly , Yearly , Area wise Profits.