Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/patriciavalentine/bolt-ride-analytics

This is a project focused on analyzing the operations of a Bolt Ride. I used MySQL for data cleaning, formatting, and basic analysis, followed by Tableau for visualization and to create a dashboard.
https://github.com/patriciavalentine/bolt-ride-analytics

bolt mysql tableau

Last synced: about 2 months ago
JSON representation

This is a project focused on analyzing the operations of a Bolt Ride. I used MySQL for data cleaning, formatting, and basic analysis, followed by Tableau for visualization and to create a dashboard.

Awesome Lists containing this project

README 

        
# 📊 BOLT RIDE ANALYSIS

## Project Overview

This project involves a comprehensive analysis of Bolt Ride operations, utilizing data from two tables representing activities in January and February.

The dataset encompasses various operational metrics, including Order Time, Pickup Address, Ride Price, Booking Fee, Tip, Payment Method, Payment Time, Distance, and State, among others. 



> Key insights were extracted through detailed analysis and subsequently visualized using a range of charts. These visualizations were then consolidated into an interactive dashboard to facilitate data-driven decision-making.



### Source of Data

- Kaggle.



### Tools Used

- **MySQL** - for data cleaning, formatting, and basic analysis.

- **Tableau** - for visualizing key metrics like revenue, peak hours, and payment preferences.



## 📂 Files

1. [Raw January Data](https://github.com/patriciavalentine/BOLT-RIDE-ANALYTICS/blob/main/Bolt%20Dataset%20for%20January.csv)

2. [Raw February Data](https://github.com/patriciavalentine/BOLT-RIDE-ANALYTICS/blob/main/Bolt%20Dataset%20for%20February.csv)

3. [Combined Cleaned Data](https://github.com/patriciavalentine/BOLT-RIDE-ANALYTICS/blob/main/Bolt%20Combined%20Data.csv)

4. [MySQL Queries & Analysis](https://github.com/patriciavalentine/BOLT-RIDE-ANALYTICS/blob/main/Bolt%20Operations%20Dataset.sql)

5. [Tableau Visualizations](https://public.tableau.com/views/BoltRideAnalytics/DASHBOARD?:language=en-US&publish=yes&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link)



## Key Inquiry Questions

The project aimed to address the following operational questions regarding Bolt ride services:

1. What are the peak and off-peak hours for Bolt rides?

2. Which pickup locations are the most frequented?

3. How does revenue distribution vary by day, and what temporal trends can be identified?



## 📑 Insights

- Analysis revealed that peak ride activity occurs around 10 a.m., while off-peak hours are observed at approximately 9 p.m.

- The most frequented pickup locations included the Central Business District (CBD) of Nairobi, Jomo Kenyatta International Airport (JKIA), and Donholm.

- In January, the driver's revenue peaked at over KShs 10,000, while the lowest recorded revenue was less than KShs 1,000. In February, the highest revenue surpassed KShs 12,000, while the lowest revenue was approximately KShs 2,500.

- Analysis also revealed that the rides resulted in no tips for drivers, indicating a potential issue with customer satisfaction or service perception.



...and several others as shown in the [Tableau Dashboard](https://github.com/patriciavalentine/BOLT-RIDE-ANALYTICS/blob/main/Tableau%20Dashboard%20Capture.PNG)!



## Recommendations

1. Optimize Shift Schedules: Drivers should consider aligning their schedules with peak ride activity times, primarily around 10 a.m. and early afternoon, as well as late night around 11 p.m. This approach will maximize their earning potential by capturing more rides during high-demand periods.

2. Utilize High-Demand Locations: Drivers should also focus on operating in the most frequented pickup locations, such as the Central Business District (CBD), and Jomo Kenyatta International Airport (JKIA). Positioning themselves strategically in these areas during peak times can increase ride requests and reduce wait times.

3. Enhance Customer Engagement and Service Quality: to improve the likelihood of receiving tips, drivers should focus on delivering exceptional service by: greeting and engaging passengers in friendly and light-hearted conversations, maintaining cleanliness, providing comfort: Offering amenities such as bottled water or adjusting the temperature to passenger preferences can enhance comfort during the ride.

4. Increase Ride Availability and Engagement: To maximize revenue, the driver should focus on increasing ride availability by aiming to drive during peak demand hours and avoiding taking days off when ride requests are likely to be high. The driver could also ensure they stay actively engaged with the app to accept ride requests promptly.



# THE 📉 PROCESS



![yellow-black-sign-taxi-placed-top-car-night](https://github.com/user-attachments/assets/883c4474-1304-42bf-b0d8-a7841e6d3763)



## 📖 Table of Content

- [Data Processing](#data-processing)

- [Data Cleaning](#data-cleaning)

- [Basic Data Analysis](#basic-data-analysis)

- [Tableau Visualizations](#tableau-visualizations)



### DATA PROCESSING

- Sample Data:



I started by viewing the first 10 rows of data from both the January and February 2023 tables to get a quick look at what I was working with.



```sql

SELECT * FROM `bolt operations dataset`.january_2023 LIMIT 10;

SELECT * FROM `bolt operations dataset`.february_2023 LIMIT 10;

```



- Structure Overview:



I used the DESCRIBE command to view the column names, data types, and other details for each table.



```sql

DESCRIBE `bolt operations dataset`.january_2023;

DESCRIBE `bolt operations dataset`.february_2023;

```

- Rename Columns:



I identified and corrected a misnamed column (Order time) in both tables to ensure consistency and avoid issues in subsequent queries.



```sql

ALTER TABLE `bolt operations dataset`.january_2023

RENAME COLUMN  `"Order time"` TO `Order time`;



ALTER TABLE `bolt operations dataset`.february_2023

RENAME COLUMN  `"Order time"` TO `Order time`;

```

- Row Counts:



I counted the number of rows in each table to understand the size of the datasets.



```sql

SELECT COUNT(*) FROM `bolt operations dataset`.january_2023;

SELECT COUNT(*) FROM `bolt operations dataset`.february_2023;

```



- Missing Values:



I checked for missing values in key columns like Pickup address, tip, state, and distance for both January and February data. No missing values were found.



```sql

-- For January 2023 table

SELECT COUNT(*) FROM `bolt operations dataset`.january_2023 WHERE `Pickup address` IS NULL;

SELECT COUNT(*) FROM `bolt operations dataset`.january_2023 WHERE tip IS NULL;



-- For February 2023 table

SELECT COUNT(*) FROM `bolt operations dataset`.february_2023 WHERE state IS NULL;

SELECT COUNT(*) FROM `bolt operations dataset`.february_2023 WHERE distance IS NULL;

```



- Duplicate Values:



I searched for duplicate rows based on key identifiers (Pickup address and Payment time). No duplicates were found.



```sql

# For February 2023 table:

SELECT `Pickup address`,  `Payment time`, COUNT(*)

FROM `bolt operations dataset`.january_2023

GROUP BY 1, 2

HAVING COUNT(*) > 1;



# For February 2023 table:

SELECT `Pickup address`,  `Payment time`, COUNT(*)

FROM `bolt operations dataset`.february_2023

GROUP BY 1, 2

HAVING COUNT(*) > 1;

```



### DATA CLEANING:

- Removing Irrelevant Columns:



I removed columns like Cancellation fee, currency, toll fee, and state from both tables as they were not necessary for my analysis.



```sql

-- For January 2023 table:

ALTER TABLE `bolt operations dataset`.january_2023

DROP COLUMN `Cancellation fee`,

DROP COLUMN currency,

DROP COLUMN `toll fee`,

DROP COLUMN state;



-- For February 2023 table:

ALTER TABLE `bolt operations dataset`.february_2023

DROP COLUMN `Cancellation fee`,

DROP COLUMN currency,

DROP COLUMN `toll fee`,

DROP COLUMN state;

```



- Formatting Dates and Times:



I split the Order time and Payment time columns into separate Order date, Time of Order, Payment date, and Time of Payment columns for easier analysis.

After extracting the dates and times, I dropped the original combined columns.



```sql

ALTER TABLE `bolt operations dataset`.january_2023

ADD COLUMN `Order date` DATE,

ADD COLUMN `Time of Order` TIME;



-- To temporarily disable the safe update mode (to enable several updates at once):

SET SQL_SAFE_UPDATES = 0;



UPDATE `bolt operations dataset`.january_2023

SET `Order date` = STR_TO_DATE(SUBSTRING(`Order time`, 1, 10), '%d.%m.%Y'),

    `Time of Order` = STR_TO_DATE(SUBSTRING(`Order time`, 12, 5), '%H:%i');



-- Deleting the former Order Date & Time column

ALTER TABLE `bolt operations dataset`.january_2023

DROP COLUMN `Order time`;



ALTER TABLE `bolt operations dataset`.january_2023

ADD COLUMN `Payment date` DATE,

ADD COLUMN `Time of Payment` TIME;



UPDATE `bolt operations dataset`.january_2023

SET `Payment date` = STR_TO_DATE(SUBSTRING(`Payment time`, 1, 10), '%d.%m.%Y'),

    `Time of Payment` = STR_TO_DATE(SUBSTRING(`Payment time`, 12, 5), '%H:%i');



-- Deleting the former Payment Date & Time column

ALTER TABLE `bolt operations dataset`.january_2023

DROP COLUMN `Payment time`;



# Repeating the same for February:

ALTER TABLE `bolt operations dataset`.February_2023

ADD COLUMN `Order date` DATE,

ADD COLUMN `Time of Order` TIME;



-- To temporarily disable the safe update mode (to enable several updates at once):

SET SQL_SAFE_UPDATES = 0;



UPDATE `bolt operations dataset`.february_2023

SET `Order date` = STR_TO_DATE(SUBSTRING(`Order time`, 1, 10), '%d.%m.%Y'),

    `Time of Order` = STR_TO_DATE(SUBSTRING(`Order time`, 12, 5), '%H:%i');



-- Deleting the former Order Date & Time column

ALTER TABLE `bolt operations dataset`.february_2023

DROP COLUMN `Order time`;



ALTER TABLE `bolt operations dataset`.february_2023

ADD COLUMN `Payment date` DATE,

ADD COLUMN `Time of Payment` TIME;



UPDATE `bolt operations dataset`.february_2023

SET `Payment date` = STR_TO_DATE(SUBSTRING(`Payment time`, 1, 10), '%d.%m.%Y'),

    `Time of Payment` = STR_TO_DATE(SUBSTRING(`Payment time`, 12, 5), '%H:%i');



-- Deleting the former Payment Date & Time column

ALTER TABLE `bolt operations dataset`.february_2023

DROP COLUMN `Payment time`;



-- To re-enable Safe Update Mode (that I disabled earlier):

SET SQL_SAFE_UPDATES = 1;

```



### BASIC DATA ANALYSIS:

- Revenue Analysis:



I calculated the total revenue by payment method for each month, as well as the total revenue per day.



```sql

-- Total Revenue by payment method for January:

SELECT `Payment method`, SUM(`Ride price`) AS `Total Revenue`

FROM `bolt operations dataset`.january_2023

GROUP BY 1;



-- Total Revenue by payment method for February:

SELECT `Payment method`, SUM(`Ride price`) AS `Total Revenue`

FROM `bolt operations dataset`.february_2023

GROUP BY 1;



-- Total revenue per day in January:

SELECT DATE(`Order date`) AS `Day`, SUM(`Ride price`) AS `Total revenue`

FROM `bolt operations dataset`.january_2023

GROUP BY `Day`

ORDER BY `Total revenue` DESC;



-- Total revenue per day in February:

SELECT DATE(`Order date`) AS `Day`, SUM(`Ride price`) AS `Total revenue`

FROM `bolt operations dataset`.february_2023

GROUP BY `Day`

ORDER BY `Total revenue` DESC

LIMIT 5;

```



- Peak Order Times:



I determined the most common hours for ride orders in both January and February.



```sql

-- January

SELECT HOUR(`Time of Order`) AS `Hour`, COUNT(*) AS `Order count`

FROM `bolt operations dataset`.january_2023

GROUP BY `Hour`

ORDER BY `Order count` DESC;



-- February

SELECT HOUR(`Time of Order`) AS `Hour`, COUNT(*) AS `Order count`

FROM `bolt operations dataset`.february_2023

GROUP BY `Hour`

ORDER BY `Order count` DESC;

```



- Popular Pickup Locations:



I identified the top 5 most common pickup locations for each month.



```sql

# January:

SELECT `Pickup address`, COUNT(*) AS `Pickup count`

FROM `bolt operations dataset`.january_2023

GROUP BY `Pickup address`

ORDER BY `Pickup count` DESC

LIMIT 5;



# February:

SELECT `Pickup address`, COUNT(*) AS `Pickup count`

FROM `bolt operations dataset`.february_2023

GROUP BY `Pickup address`

ORDER BY `Pickup count` DESC

LIMIT 5;

```



- Ride Price Analysis:



I calculated the average, minimum, and maximum ride prices for each month.



```sql

-- January:

SELECT 

    AVG(`Ride price`) AS `Avg price`, 

    MIN(`Ride price`) AS `Min price`, 

    MAX(`Ride price`) AS `Max price`

FROM `bolt operations dataset`.january_2023;



-- February:

SELECT 

    AVG(`Ride price`) AS `Avg price`, 

    MIN(`Ride price`) AS `Min price`, 

    MAX(`Ride price`) AS `Max price`

FROM `bolt operations dataset`.february_2023;

```



I also grouped the ride prices into ranges (e.g., under 200, 200-500, etc.) to understand the distribution of ride costs.



```sql

-- January

SELECT 

    CASE

        WHEN `Ride price` < 200 THEN 'Under 200'

        WHEN `Ride price` BETWEEN 200 AND 500 THEN '200-500'

        WHEN `Ride price` BETWEEN 500 AND 1000 THEN '500-1000'

        ELSE 'Over 1000'

    END AS `Price range`,

    COUNT(*) AS `Ride count`

FROM `bolt operations dataset`.january_2023

GROUP BY `Price range`

ORDER BY `Ride count` DESC;



-- February

SELECT 

    CASE

        WHEN `Ride price` < 200 THEN 'Under 200'

        WHEN `Ride price` BETWEEN 200 AND 500 THEN '200-500'

        WHEN `Ride price` BETWEEN 500 AND 1000 THEN '500-1000'

        ELSE 'Over 1000'

    END AS `Price range`,

    COUNT(*) AS `Ride count`

FROM `bolt operations dataset`.february_2023

GROUP BY `Price range`

ORDER BY `Ride count` DESC;

```



- Payment Method Preferences:



I examined the frequency of different payment methods used by customers in each month.



```sql

-- January:

SELECT `Payment method`, COUNT(*) AS Paymentmethod_count

FROM `bolt operations dataset`.january_2023

GROUP BY `Payment method`

ORDER BY paymentmethod_count DESC;



-- February:

SELECT `Payment method`, COUNT(*) AS Paymentmethod_count

FROM `bolt operations dataset`.february_2023

GROUP BY `Payment method`

ORDER BY paymentmethod_count DESC;

```



- Distance Analysis:



I computed the average, minimum, and maximum distances traveled by riders.



```sql

-- January:

SELECT 

    AVG(distance) AS avg_distance, 

    MIN(distance) AS min_distance, 

    MAX(distance) AS max_distance

FROM `bolt operations dataset`.january_2023;



-- February:

SELECT 

    AVG(distance) AS avg_distance, 

    MIN(distance) AS min_distance, 

    MAX(distance) AS max_distance

FROM `bolt operations dataset`.february_2023;

```



- Union of Tables:



I used a UNION ALL to combine the data from January and February into a single view.



```sql

SELECT * FROM `bolt operations dataset`.january_2023

UNION ALL

SELECT * FROM `bolt operations dataset`.february_2023;

```



- Creating a New Table:



I then created a new table called combined_data to permanently store the combined dataset for future analysis.



```sql

CREATE TABLE `bolt operations dataset`.combined_data AS

SELECT * FROM `bolt operations dataset`.january_2023

UNION ALL

SELECT * FROM `bolt operations dataset`.february_2023;



-- CONFIRMING THE NEW TABLE:

SELECT * FROM `bolt operations dataset`.combined_data;

```



### TABLEAU VISUALIZATIONS



You can view all the Tableau Visualizations [here!](https://public.tableau.com/views/BoltRideAnalytics/DASHBOARD?:language=en-US&publish=yes&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link)



In the Tableau dashboard,

*I selected a combination of KPIs and charts that provide a comprehensive view of the key performance indicators and critical operational metrics.*



![Tableau Dashboard Capture](https://github.com/user-attachments/assets/dd449550-da29-4285-9d6f-188f6d3248a0)



The KPIs — *total revenue, total distance covered, total rides, total days, and total amount of tips received* — serve as fundamental metrics that give an immediate understanding of overall performance. These KPIs are crucial for quickly assessing the effectiveness of operations and revenue generation.



> For the visualizations, I used:



- Horizontal **bar graphs** to display the top 5 peak and off-peak hours, which are critical for understanding demand patterns and optimizing resource allocation.



- A **histogram** of revenue by payment method that offered insights into customer preferences, aiding in strategic decision-making related to payment processing.



- A **treemap** showing the top 5 popular pickup locations highlights key areas of demand, which is essential for targeted marketing and operational focus.



- **Line graphs** for revenue growth over time that provided a clear view of financial performance trends, which is invaluable for forecasting and strategic planning.



## CONCLUSION



> The analysis of the Bolt Ride operations provides valuable insights into ride activity patterns and revenue generation for drivers. By identifying significant peak times and popular pickup locations, drivers can optimize their availability to capture more rides during high-demand periods.



By adopting the targeted recommendations, drivers can improve their service efficiency, enhance customer satisfaction, and ultimately strengthen their position in the competitive online taxi market.



## THE END 🧑‍💻 .

### Thank you!