RIDE SHARING DATA PRESENTATION

Ride sharing

Introduction

It has been an exciting and rewarding learning experience for us over the past few weeks. And working together as a team has quite been challenging and we all have like mind to achieve a desired result .

During this programme, we were introduced to key concepts and tools that form the foundation of effective data analysis. One of the most important frameworks we explored was the Data Analysis Cycle, which guides analysts in transforming raw data into meaningful insights that support decision-making.

As part of this learning journey, we carried out a hands-on project as a team using a RIDE SHARING DATA ANALYSIS through the pivot table we were working on, The project focuses on applying essential data analysis techniques in Microsoft Excel, including data cleaning, data modelling, and data visualization.

This presentation highlights the analytical process we followed, the dashboards created, and the insights generated from the dataset. It also demonstrates how structured data analysis can help businesses better understand their sales performance and customer purchasing patterns.

Executive Summary

The Ride Sharing Data Project provides a comprehensive analysis of ride patterns, customer behaviour, and operational performance. A total of 8,500 rides were completed, generating approximately $600.94K in revenue, with an average customer rating of 3.73 and an average ride duration of 54 minutes. The system recorded a 5.27% no-show rate, indicating moderate service inefficiencies.

Key findings reveal that credit cards are the most preferred payment method, highlighting a strong shift toward cashless transactions. In terms of vehicle demand, Sedans and SUVs dominate user preference, suggesting customers prioritize comfort and availability over lower-cost shared options.

Location analysis shows that Suburbs and Mall areas are the busiest pickup and drop-off points, indicating high demand in residential and commercial hubs. Traffic insights indicate that medium traffic conditions generate the highest ride volume, while low traffic conditions result in slightly longer but smoother trips.

Weather conditions also impact performance, with rainy conditions associated with slightly higher customer ratings, while extreme conditions like thunderstorms show a drop in satisfaction.

Overall, the analysis suggests opportunities to optimize fleet allocation, improve pricing strategies, and enhance service efficiency, particularly by focusing on high-demand vehicle types, key locations, and traffic patterns.

Project Objectives

The primary objectives of this project were to:

• Monitor Overall Business Performance • Understand Customer Behaviour & Demand Patterns • Evaluate Operational Efficiency • Create clear and interactive visualizations to present sales trends and performance • Generate meaningful insights and recommendations based on the analysis

Work Done (Methodology)

To achieve the objectives of this project, several Excel tools and techniques were applied:

1. Data Validation

Rules and restrictions were applied to ensure that only valid entries were accepted in specific columns, reducing the possibility of incorrect data input.

2. Conditional Formatting

Conditional formatting was used to highlight patterns, duplicates, and anomalies in the dataset, making it easier to detect inconsistencies.

3. Sorting and Filtering

Sorting and filtering tools were used to organize the dataset, identify duplicates, and examine specific records for deeper analysis.

4. Data consolidation

This is the process of bringing data from multiple sources into one source.

These tools enabled efficient data transformation and supported accurate dashboard reporting.

5. Data cleaning

The first thing that was checked was if the data was cleaned and yes it was cleaned. After which a extraction of a new column is done and named (weekdays) from an existing column (ride date time) using the =weekday function. The weekend/weekday column was also added using (if E2>5,”Weekend”,”Weekday”) function.

Pivot Table 1&2

The pivot table analysis highlights key aggregations across ride-sharing operations, focusing on ride volume, revenue, duration, and user behavior across different categories:

• Ride Volume & Revenue
• Total completed rides: 8,500
• Total revenue generated: $600.94K
• No-show rides contributed the highest fare total (~$60.94K), followed by completed rides (~$60.20K) and canceled rides (~$58.73K).
• Vehicle Type Distribution
• Sedans (3,964 rides) and SUVs (3,049 rides) dominate usage.
• Lower usage observed for Shared (1,032 rides), Motorcycle (984 rides), and Electric (971 rides).
• Payment Method Breakdown
• Credit Card is the most used (~$4.95K).
• Followed by Cash (~$3.03K) and Mobile Wallet (~$2.03K).
• Location-Based Demand
• Highest ride activity in Financial District (1,552 rides) and Suburbs (1,487 rides).
• Indicates strong demand in both commercial and residential areas.
• Traffic Level Impact
• Medium traffic shows highest ride activity (~271.95 mins total duration, ~$302.05K revenue).
• Low traffic: moderate activity (~162.24 mins, ~$179.85K).
• High traffic: lowest activity (~106.10 mins, ~$119.04K).
• Weather Impact
• Clear weather has the longest ride duration (~327.30 mins total).
• Rainy conditions show moderate duration (~106.63 mins).
• Snowy and Thunderstorm conditions have the shortest durations (~26 mins each).
• Customer Ratings
• Ratings are fairly consistent (≈3.66–3.76).
• Rainy weather has the highest rating (~3.76), while Thunderstorm has the lowest (~3.66).
• Time-Based Analysis
• Weekdays (7,200 rides) significantly outweigh weekends (2,800 rides), indicating higher demand during working days.

Overall, the pivot tables reveal that demand is concentrated around specific vehicle types, locations, and moderate traffic conditions, while digital payments and weekday usage dominate customer behavior

DATA VISUALISATION

Dashboard 1&2

The ride-sharing dashboard provides a high-level overview of operational performance, customer behavior, and key demand drivers across the system.

At a glance, the platform recorded 8,500 completed rides, generating approximately $600.94K in total revenue. The average customer rating stands at 3.73, indicating moderate user satisfaction, while the average ride duration is 54.03 minutes. A 5.27% no-show rate suggests some inefficiencies in ride fulfillment.

From a vehicle preference perspective, Sedans (3,964 rides) and SUVs (3,049 rides) are the most in-demand options, significantly outperforming Electric, Motorcycle, and Shared ride types. This indicates a strong user preference for comfort and standard ride options.

In terms of location demand, Financial District (1,552 rides) and Suburbs (1,487 rides) emerge as the most active pickup and drop-off areas, highlighting key commercial and residential hotspots.

Payment behavior shows that credit cards are the dominant method, followed by cash and mobile wallets, reinforcing a trend toward digital payments.

Looking at traffic conditions, medium traffic levels generate the highest ride activity and revenue, while high traffic sees the lowest engagement. Additionally, ride duration tends to be slightly longer in low-traffic conditions.

Weather analysis reveals that clear weather results in the longest ride durations, while adverse conditions like snow and thunderstorms significantly reduce trip length. However, customer ratings remain relatively stable, with rainy conditions receiving slightly higher ratings.

Finally, weekday demand (7,200 rides) is significantly higher than weekend demand (2,800 rides), indicating that ride usage is largely driven by work-related and routine travel.

Overall, the dashboard highlights opportunities to optimize fleet allocation, focus on high-demand locations, enhance digital payment systems, and improve operational efficiency during peak traffic conditions.

A dashboard consolidates large volumes of data into key metrics such as totals, averages and percentages.

Instead of reviewing thousands of rows in Excel, users see summarized insights instantly.

Example: Total Loan Volume, Most Used Transaction Type, Loan by Account Type.

Slicers: are visualization filter, instead of creating multiple separate reports, slicers allow one dashboard to serve multiple analytical purposes.

INSIGHT

An insight is a meaningful understanding or pattern discovered from analyzed data that helps explain what is happening and why.

In relation to Pivot Tables, an insight is the interpretation of summarized data after it has been grouped, filtered, and aggregated.

Some of the findings include:

· Credit Card is the Most Preferred Payment Method

Credit card payments (~0.68K) lead significantly over cash (~0.47K) and mobile wallets (~0.28K), indicating a strong user preference for cashless and convenient transactions.

· Suburbs and Malls Are High-Demand Drop-off Locations

Locations like Suburbs (~1487 rides) and Malls (~1459 rides) record the highest drop-offs, highlighting key demand hubs tied to residential and commercial activity.

· Medium Traffic Conditions Generate Highest Ride Volume

Medium traffic accounts for the highest ride activity (~271.95K mins) compared to low and high traffic conditions. Also, average ride duration is slightly higher in low traffic (~54.59 mins), suggesting longer but smoother trips.

RECOMMENDATION

Also suggest what should be done about it. A recommendation is a data-driven action proposed based on the insight derived from analysis.

· Credit Card is the Most Preferred Payment Method

Promote cashless incentives (e.g., discounts for card payments).

Partner with banks or fintech apps for cashback offers.

Improve mobile wallet integration to grow its adoption.

· Suburbs and Malls Are High-Demand Drop-off Locations

Position more drivers around these hotspot areas.

Introduce geo-targeted promotions (e.g., mall ride discounts).

Collaborate with malls for pickup/drop-off partnerships.

· Medium Traffic Conditions Generate Highest Ride Volume

Optimise pricing strategies for medium-traffic periods (balanced demand).

Use route optimisation tools to reduce time in high traffic.

Inform riders with ETA transparency during peak congestion.

Conclusion

The Ride Sharing Data Project successfully delivers a data-driven understanding of operational performance, customer preferences, and key factors influencing ride demand. The analysis shows that the platform is performing strongly in terms of ride volume and revenue generation, with consistent customer ratings and manageable no-show rates.

Key patterns indicate that Sedans and SUVs dominate customer choice, while credit card payments lead transaction methods, reflecting a preference for convenience and comfort. Demand is largely concentrated in commercial and residential hubs such as the Financial District and Suburbs, and is significantly higher during weekdays, emphasizing routine and work-related usage.

External factors such as traffic and weather conditions also play a crucial role. Medium traffic levels drive the highest ride activity, while extreme weather conditions tend to reduce ride duration and slightly impact customer satisfaction.

Overall, the project highlights clear opportunities for improvement, including better fleet distribution in high-demand areas, optimization of pricing strategies, and reduction of no-show rates. By leveraging these insights, ride-sharing operations can enhance efficiency, improve customer experience, and maximize revenue growth.