An end-to-end analytics project covering the full journey from raw data to business intelligence. Raw Quick Commerce transactional data is cleaned and explored in Python, then brought to life in a three-page Power BI dashboard covering sales performance, delivery operations, and customer behavior.
- Cleans and preprocesses raw transactional data for reliable analysis
- Conducts exploratory data analysis to surface distribution patterns and data quality issues
- Defines business KPIs using DAX and builds calculated measures in Power BI
- Delivers an interactive, multi-page dashboard to support operational decision-making
Python — Pandas, NumPy, Matplotlib Power BI — Power Query, DAX, Data Modeling
- Loaded the raw dataset and reviewed structure, data types, and summary statistics
- Handled missing values through group-wise median imputation
- Removed duplicate entries and corrected mismatched data types
- Conducted outlier analysis across all numerical columns
- Exported a clean, analysis-ready dataset for Power BI
Built a three-page interactive report:
Page 1 — Business Overview
Revenue, order volume, and average order value at a glance. Breakdowns by city, company, payment method, and top-performing product categories.
Page 2 — Delivery Performance
Partner ratings, average delivery distance, on-time delivery rate, and company-level comparisons for delivery time, rating, and discount rates.
Page 3 — Customer Insights
Customer satisfaction scores, high-value order segmentation, items per order, category-level demand, and revenue split by age group.
- Company and City slicers for cross-filtering across all pages
- 15+ custom DAX measures
- KPI cards for at-a-glance monitoring
- Page navigation for seamless report browsing
- Consistent visual design across all three pages
Data Cleaning · EDA · Data Transformation · DAX Measure Creation · Data Modeling · Dashboard Design · Business Intelligence Reporting · KPI Analysis
This project walks through a complete analytics pipeline — from Python-based preprocessing and exploratory analysis to a polished, interactive Power BI report — producing actionable insights into Quick Commerce operations and customer purchasing patterns.