I'm a data analyst building a strong technical foundation in Excel, SQL, and Python, with a specialization in data cleaning and exploratory data analysis (EDA).
- 🌍 Based in United Arab Emirates
- 📊 Currently focused on: Data cleaning, Excel analytics, and hospital data analysis
- 🎯 Goal: Become a confident, job-ready Junior Data Analyst
- 📚 Always learning and improving through hands-on projects
Real-Time Food Price Analytics across 10 Pakistani Cities
1,100 Records · 35 Food Items · 9 Categories · PKR 13.4M Total Revenue Tracked
This project delivers a comprehensive Excel-based Food Price Intelligence System for Pakistan's major urban markets. It tracks, analyzes, and visualizes food prices per kilogram across 10 cities, enabling market-level insights on pricing trends, volatility, and revenue distribution.
The dashboard was built entirely in Microsoft Excel using structured data modeling, pivot tables, and advanced charting — no code required.
The main dashboard shows KPI cards (Total Records, Revenue, Avg Price/Kg), a revenue breakdown by city, and average price by category — all in a dark-themed, professional layout.
Left: Bar chart comparing total revenue (PKR) across all 10 cities — Hyderabad leads at PKR 1.6M.
Right: Pie chart showing average price distribution by food category — Beverage and Meat hold the largest shares.
| Metric | Value |
|---|---|
| 📋 Total Records | 1,100 transactions |
| 💰 Total Revenue (PKR) | 13,423,064 |
| ⚖️ Avg Price / Kg | PKR 372 |
| 🏆 Top Revenue City | Hyderabad — PKR 1,608,801 |
| 🐟 Most Expensive Item | Fish (Pomfret) — PKR 1,113/kg avg |
| 📊 Items Priced Above Avg | 29.9% |
📁 PK_Food_Price_Intelligence/
│
├── 📊 PK_FOOD_PRICE_PER_KG-project1.xlsx ← Core dataset (1,100 records)
├── 📊 Source_Market_Survey_PK.xlsx ← Raw market survey data
│
├── 📈 chart_data.xlsx ← Chart source data
├── 📈 City_Coverage_Performance.xlsx ← City-level performance metrics
├── 📈 Monthly_KPI_tracker.xlsx ← Monthly KPI tracking
│
├── 🔍 gap_Analysis.xlsx ← Price gap & market gap analysis
├── 🔍 Price_Level.xlsx ← Price level classification
├── 🔍 Price_Review_per_Kg.xlsx ← Per-kg price review breakdown
├── 🔍 Volatility_of_price_in_PK_FOOD_PRICE.xlsx ← Price volatility analysis
│
├── 🖼️ Pk_Food_project_dashboard.png ← Dashboard screenshot
├── 🖼️ Chart_Data.png ← Revenue & category charts
│
└── 📄 README.md ← You are here
| City | Total Revenue | % Share | Avg Price/Kg | Status |
|---|---|---|---|---|
| Hyderabad | 1,608,801 | 12.0% | 417.6 | 🏆 HIGHEST |
| Karachi | 1,568,588 | 11.7% | 421.0 | ✅ ACTIVE |
| Islamabad | 1,460,893 | 10.9% | 343.8 | ✅ ACTIVE |
| Quetta | 1,459,146 | 10.9% | 399.7 | ✅ ACTIVE |
| Faisalabad | 1,382,163 | 10.3% | 347.9 | ✅ ACTIVE |
| Rawalpindi | 1,342,984 | 10.0% | 409.4 | ✅ ACTIVE |
| Lahore | 1,279,284 | 9.5% | 349.4 | ✅ ACTIVE |
| Sialkot | 1,174,601 | 8.8% | 368.8 | ✅ ACTIVE |
| Multan | 1,114,521 | 8.3% | 325.3 | ✅ ACTIVE |
| Peshawar | 1,032,082 | 7.7% | 338.3 | |
| TOTAL | 13,423,064 | 100% |
| Rank | Category | Avg Price/Kg | Max | Min | Records |
|---|---|---|---|---|---|
| 1 | Beverage | 911.6 | 1,185.1 | 638.1 | 48 |
| 2 | Meat | 847.8 | 1,102.1 | 593.5 | 174 |
| 3 | Oil | 721.7 | 938.1 | 505.2 | 65 |
| 4 | Condiment | 450.7 | 585.9 | 315.5 | 62 |
| 5 | Dairy | 413.8 | 537.9 | 289.7 | 94 |
| 6 | Pulses | 256.6 | 333.5 | 179.6 | 102 |
| 7 | Fruit | 185.6 | 241.3 | 129.9 | 184 |
| 8 | Grain | 141.9 | 184.5 | 99.3 | 180 |
| 9 | Vegetable | 94.9 | 123.4 | 66.5 | 191 |
The master data file containing all 1,100 price records across 10 cities and 35 food items. This is the single source of truth for all analysis.
Raw survey data collected from ground-level market sources across Pakistan, feeding into the cleaned dataset.
City-by-city performance metrics including revenue share, record count, and active status tracking.
Time-series KPI monitoring to track how average prices, volumes, and revenue evolve month over month.
Identifies the price gap between the cheapest and most expensive cities/items, highlighting where arbitrage or supply chain inefficiencies exist.
Classifies each food item into price tiers (Low / Medium / High / Premium) based on their per-kg cost relative to the national average.
Detailed review of per-kilogram pricing broken down by item, city, and category — useful for procurement and policy analysis.
Statistical analysis of price fluctuations across the dataset, identifying which food categories and cities experience the most price instability.
Pre-aggregated data tables used to power the charts and visual elements in the main dashboard.
- 🏆 Hyderabad generates the highest revenue (12%) and has the highest average price per kg (PKR 417.6)
- 🐟 Fish (Pomfret) is the single most expensive item at PKR 1,113/kg on average
- 🥤 Beverages are the priciest category overall (PKR 911.6/kg avg), followed by Meat and Oil
- 🥦 Vegetables are the most affordable category (PKR 94.9/kg avg) with the highest record count (191)
⚠️ Peshawar has the lowest total revenue and the lowest average price per kg — a potential under-served market- 📊 Nearly 30% of items are priced above the national average, indicating significant price dispersion
- Microsoft Excel — Data modeling, pivot tables, conditional formatting, charts
- Data Source — Pakistan_Food_Prices_Cleaned (primary survey data)
- Visualization — Bar charts, pie charts, KPI scorecards, dark-theme dashboard layout
Built as part of an Excel Analytics portfolio project demonstrating end-to-end data analysis, dashboard design, and market intelligence reporting using Microsoft Excel.
Last updated: 2025 | Data covers 10 major Pakistani cities
- Excel Fundamentals & Advanced Formulas
- Data Cleaning Techniques
- SQL Basics
- Python for Data Analysis (pandas, numpy)
- Data Visualization (Matplotlib, Seaborn)
- Power BI Dashboards
- Real-world capstone project
