Uber Demand Gap Study
Publicada el 2026-07-29
Descripción de la oferta
This project analyzes 6,745 Uber ride requests collected over a 5-day period (July 11–15, 2016). The objective was to uncover patterns behind ride cancellations, unfulfilled requests, peak demand periods, and driver availability issues. The analysis combines: * Excel Dashboarding * Python (Pandas) Exploratory Data Analysis (EDA) * SQL Business Queries * KPI & Trend Analysis * Business Recommendations ## Business Problem Uber was experiencing a significant number of ride failures due to: * Driver cancellations * No cars available * Supply-demand imbalance * Poor driver distribution between Airport and City locations The goal was to identify: * Why ride requests fail * When failures occur * Where operational inefficiencies exist * How business performance can be improved ## Dataset Information | Attribute | Details | | ---------------- | --------------------- | | Dataset Size | 6,745 Ride Requests | | Time Period | July 11–15, 2016 | | Pickup Locations | Airport, City | | Drivers | 300 Unique Drivers | | Analysis Tools | Excel, SQL, Python | | Records Analyzed | 100% Original Dataset | ### Columns Request ID, Pickup Point, Driver ID, Status, Request Timestamp, Drop Timestamp ### Engineered Features Request Hour, Request Date, Day of Week, Time Slot, Trip Duration (Minutes) ## Tools & Technologies Used ### Excel * Interactive Dashboard * KPI Cards * Pivot Tables * Charts & Visualizations * Conditional Formatting ### Python * Pandas * NumPy * Datetime Operations ### SQL * SQLite * Aggregations * GROUP BY Analysis * Business Queries ## Key Findings ### Overall Ride Status | Status | Count | Percentage | | ----------------- | ----: | ---------: | | Completed | 2,831 | 41.9% | | Cancelled | 1,264 | 18.7% | | No Cars Available | 2,650 | 39.3% | ### Critical Insight Only 41.9% of ride requests were successfully completed. More than 58% of requests failed due to: * Driver cancellations * Lack of available cars ## Demand Pattern Analysis ### Morning Rush (5 AM – 9 AM) **Primary Issue: Driver Cancellations** Drivers frequently cancelled City-to-Airport trips because they anticipated difficulty finding return passengers from the airport. ### Evening Rush (5 PM – 10 PM) **Primary Issue: No Cars Available** Airport passengers struggled to find rides due to insufficient driver presence at the airport. ## Pickup Point Analysis ### Airport * Total Requests: 3,238 * Completion Rate: 41.0% * Major Issue: No Cars Available ### City * Total Requests: 3,507 * Completion Rate: 42.9% * Major Issue: Driver Cancellations ## Peak Demand Hours | Hour | Requests | | ---- | -------: | | 6 PM | 510 | | 8 PM | 492 | | 7 PM | 473 | | 9 PM | 449 | | 8 AM | 423 | Peak demand occurs during commuting and airport travel periods. ## SQL Business Analysis The project includes 7 SQL business queries covering: * Completion Rate by Pickup Point * Peak Demand Hours * Top Cancellation Hours * No Cars Available Analysis * Average Trip Duration * Daily Demand Trend * Most Active Drivers ## Root Cause Analysis ### Problem 1: Airport Supply Shortage **Cause:** Drivers avoid waiting at the airport after completing drop-offs. **Impact:** Large number of evening ride failures. ### Problem 2: Morning Trip Cancellations **Cause:** Drivers cancel airport-bound trips to avoid being stranded at the airport. **Impact:** High cancellation rates during morning commute hours. ## Business Recommendations ### Airport Incentive Program * Introduce surge pricing for airport pickups. * Offer guaranteed return-trip matching. ### Anti-Cancellation Strategy * Apply peak-hour cancellation penalties. * Provide bonuses for airport-bound trips. ### Driver Reallocation * Deploy more drivers in the City during mornings. * Deploy more drivers at the Airport during evenings. ### Fleet Expansion **Current Driver Fleet:** 300 **Recommended Fleet Size:** 420–450 Drivers ### Expected Outcome * Completion Rate > 65% * Reduced cancellations * Better customer experience ## Dashboard Features The Excel dashboard contains: ### Sheet 1: Cleaned Dataset * All processed ride records * Status-based formatting ### Sheet 2: KPI Dashboard * Total Requests * Completion Rate * Cancellation Rate * No Cars Available % ### Sheet 3: Hourly Analysis * Demand by Hour * Completion Trends ### Sheet 4: Time Slot Dashboard * Problem Rate Analysis * Peak Demand Visualization ### Sheet 5: Pickup Point Analysis * Airport vs City Comparison ### Sheet 6: SQL Insights * Query Outputs * Business Findings ### Sheet 7: EDA Results * Statistical Summaries * Trend Analysis ## Dashboard Preview
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Fuente original: freelancer