In this repository you can find my Power BI projects:

• Adventure Works 2020 (from Microsoft Learning)

Diabetes Analytics Dashboard – Power BI 🩺📊 This practice dashboard is built for Data Analytics, Data Visualization, and Data Science learning. It provides meaningful insights into diabetes risk factors using interactive visuals and advanced analytics.

🔹 Key Metrics – Total patients, BMI, glucose, blood pressure, and insulin levels. 🔹 Diabetes Risk Segmentation – Categorized into High, Medium, and Low risk groups. 🔹 Trends & Distribution – Glucose vs. Age, BMI categories, and Blood Pressure analysis. 🔹 Correlation Analysis – Exploring the relationships between glucose, BMI, and diabetes risk. 🔹 Gauge & Pie Charts – Visualizing risk percentage, BMI distribution, and glucose levels. 🔹 Interactive Filters & Drilldowns – Allowing deeper exploration of specific patient groups. 🔹 Predictive Insights – Identifying potential risk patterns through visual analytics.

This project helps in understanding data-driven healthcare insights using Power BI. Thanks to Kaggle for the dataset!

Dataset

This dataset was created by Jagadish eslavath

Contents

Dataset

This dataset was created by irshad ahmed

Contents

Dataset Overview: Contains sales data from Blinkit, including product details, order quantities, revenue, and timestamps. Useful for demand forecasting, price optimization, trend analysis, and business insights. Helps in understanding customer behavior and seasonal variations in online grocery shopping. Potential Use Cases: - Time Series Analysis: Analyze sales trends over different periods. - Demand Forecasting: Predict future product demand based on historical data. - Price Optimization: Identify the impact of pricing on sales and revenue. - Customer Behavior Analysis: Understand buying patterns and preferences. - Market Trends: Explore how different factors affect grocery sales performance. This dataset can be beneficial for data scientists, business analysts, and researchers looking to explore e-commerce and retail trends. Feel free to use it for analysis, machine learning models, and business intelligence projects. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16299142%2Fa633fb36dc370263696b5d2ec940c74f%2FScreenshot%202025-06-16%20082824.png?generation=1750086765806732&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16299142%2F8843129c88c2f57d66006a3ac9d37dc7%2FScreenshot%202025-06-16%20084001.png?generation=1750086777975125&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16299142%2Ffa4f29a8f4cc763a1cc66c7913c077e8%2FScreenshot%202025-06-16%20084007.png?generation=1750086787100561&alt=media" alt="">

The Business Intelligence (BI) Platforms Software market is experiencing robust growth, driven by the increasing need for data-driven decision-making across various industries. The market's expansion is fueled by several key factors, including the rising adoption of cloud-based BI solutions, the growing volume of data generated by businesses, and the increasing demand for advanced analytics capabilities such as predictive modeling and machine learning. The market is segmented by deployment type (cloud, on-premise), organization size (small, medium, large), industry vertical (BFSI, healthcare, retail, manufacturing, etc.), and functionality (reporting, analytics, data visualization). Competition is intense, with established players like Tableau, Microsoft Power BI, and Qlik competing with emerging cloud-native solutions and specialized platforms. The market's evolution towards self-service BI, embedded analytics, and AI-powered insights is shaping vendor strategies and product development. The forecast period (2025-2033) projects sustained growth, with a considerable expansion expected in regions like Asia-Pacific and Latin America, fueled by digital transformation initiatives and increasing technology adoption. However, challenges remain, including the complexity of implementing and integrating BI solutions, the need for skilled data professionals, and data security concerns. The market's future hinges on the continuous innovation in data visualization, AI integration, and the development of user-friendly platforms that democratize data access and analysis across organizations. This will likely lead to further consolidation and partnerships among vendors aiming to offer comprehensive and integrated BI solutions to cater to the diverse and evolving needs of businesses. Let's assume a conservative CAGR of 15% for the forecast period, based on current market trends.

An interactive dashboard to visualize sales performance, product categories, regional performance, and key business KPIs.

📌 Description This project analyzes online sales data using Power BI, focusing on performance metrics such as Total Sales Amount, Profit, Quantity Sold, and Payment Modes. The dashboard provides detailed visualizations to identify top-performing categories, sub-categories, and locations. It aims to deliver actionable insights for business strategy, marketing decisions, and operational improvements.

The dataset is split across two CSV files:

• Orders.csv – contains customer and order metadata (date, name, location)

• Details.csv – contains order-level details (profit, quantity, payment mode, category)

🧩 Key Features - KPI Cards: Total Amount, Total Profit, Total Quantity, Profit Margin

• Pie Charts: Sales by Category, Sales by Payment Mode

• Donut Chart: Sales by State

• Bar Chart: Sales by Sub-Category

• Map: Quantity sold across Indian States

• Interactive Slicers and Filters

⚒️ Tools & Techniques Power BI Desktop

DAX Calculations

Custom Visual Design for Clean UI/UX

Power BI Dashboard : https://www.mavenanalytics.io/project/3776

The IPL (Indian Premier League) is one of the most popular and widely followed cricket leagues in the world. It features top cricket players from around the world playing for various franchise teams in India. The league is known for its high-scoring matches, intense rivalries, and innovative marketing strategies.

If you are a data enthusiast or a cricket fan, you will be excited to know that there is a dataset available on Kaggle that contains comprehensive information about the IPL matches played over the years. This dataset is a valuable resource for anyone interested in analyzing the performance of players and teams in the league.

The IPL dataset on Kaggle contains information on over 800 IPL matches played from 2008 to 2020. It includes details on the date, time, venue, teams, players, and various statistics such as runs scored, wickets taken, and more. The dataset also contains information on the individual performances of players and teams, as well as the overall performance of the league over the years.

The IPL dataset is a goldmine for data analysts and cricket enthusiasts alike. It provides a wealth of information that can be used to uncover insights about the league and its players. For example, you can use the dataset to analyze the performance of a particular player or team over the years, or to identify trends in the league such as changes in team strategies or the emergence of new players.

If you are new to data analysis, the IPL dataset is a great place to start. You can use it to learn how to use tools such as Excel or Power BI to create visualizations and gain insights from data. With the right skills and tools, you can use the IPL dataset to create interactive dashboards and reports that provide valuable insights into the world of cricket.

Overall, the IPL dataset on Kaggle is an excellent resource for anyone interested in cricket or data analysis. It contains a wealth of information that can be used to analyze and gain insights into the performance of players and teams in one of the most exciting cricket leagues in the world.

This dataset contains points table and player Information. To view more data such as Match stats, Ball_by_ball data & Player innings data, Please visit the below links:

Match stats, Ball_by_ball data: https://www.kaggle.com/datasets/biswajitbrahmma/ipl-complete-dataset-2008-2022

Player innings data: https://www.kaggle.com/datasets/paritosh712/cricket-every-single-ipl-inning-20082022

Thanks to Biswajit Brahmma & Paritosh Anand for their dataset.

This is the filtered dataset of LA Census Tracts from the 500 Cities project 2017 release. This dataset includes 2015, 2014 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2015, 2014), Census Bureau 2010 census population data, and American Community Survey (ACS) 2011-2015, 2010-2014 estimates. Because some questions are only asked every other year in the BRFSS, there are 7 measures from the 2014 BRFSS that are the same in the 2017 release as the previous 2016 release. More information about the methodology can be found at www.cdc.gov/500cities.

I'm excited to share my latest project—an interactive Power BI dashboard that provides a comprehensive analysis of bike sales data from 2019 to 2024!

Key Highlights of the Dashboard:

📈 Sales Trend Analysis: Understand how bike sales have fluctuated over the years, with peaks in specific months that give us clues about seasonal demand. 🏢 Sales by Store Location: See how different cities like New York and Phoenix lead in terms of total sales revenue. 🚴‍♀️ Customer Demographics: Almost equal contributions from male and female customers—showing the broad appeal of our products. 💳 Payment Method Preferences: Breakdown of the most used payment methods, with insights that can help improve our customer experience. 📊 Revenue by Bike Model: A detailed look at which bike models drive the most revenue, helping guide product focus and inventory management. This dashboard was built to provide actionable insights into the sales performance and customer behavior of a large dataset of 100K records. It highlights the power of data visualization in turning numbers into strategic insights!

Why Power BI? Power BI's flexibility and interactive capabilities made it the ideal tool for visualizing the data, allowing users to drill down into specific details using slicers for bike models and time periods. 💡

Would love to hear your thoughts or any feedback on this project! If you’re interested in how this dashboard was built or want to discuss data visualization, feel free to reach out. Let’s transform data into stories that drive success! 🌟

In this project, I conducted a comprehensive analysis of customer data using Power BI. The objective was to visualize and gain insights from the data, focusing on customer demographics and product categories.

📈The analysis includes the following key visualizations:

Customer Distribution by Age: illustrates the number of customers across different age groups, providing insights into the demographic distribution.

Customer Distribution by Time: This visualization shows the count of customers segmented by year, quarter, month, and day, helping identify trends over time.

Customer Distribution by Gender: displays the distribution of customers by gender, highlighting any significant differences.

Total Amount by Product Category: depicts the total revenue generated by each product category, allowing for easy comparison.

Quantity by Product Category: shows the total quantity of products sold in each category, helping to identify popular items.

The cards display key metrics:

Average Age: 41.39 Total Customers: 1000 Total Quantity Sold: 2514 Total Amount Sold: 465 000$ Total Transactions: 1000 Additionally, I implemented filters for product category, date, gender, quantity, and age, providing users with the ability to refine their analysis.

Findings:

The analysis of customer distribution by age reveals no specific relationship between age and the quantity of products sold. This indicates that purchasing behavior may not be strongly influenced by the customer’s age. There are notable peaks in the quantity sold on May 20, 2023, and again in July, suggesting higher purchasing activity during these periods. The customer distribution by gender shows that 49% of customers are female, while 51% are male. In terms of total amount sold by product category, electronics is the top category, generating the highest revenue, followed by clothing, with beauty ranking last. Similarly, when looking at quantity sold by product category, electronics makes up 33.77%, clothing is slightly higher at 35.56%, and beauty is the smallest category at 3.67%. This project demonstrates the power of Power BI in analyzing customer data and deriving actionable insights. The visualizations created provide a clear understanding of customer behavior and preferences, which can help businesses make informed decisions.

Data Science Dashboard

** Exciting Data Science Dashboard Project Overview **

I'm thrilled to share my latest Power BI dashboard project focusing on the salaries and employment trends of Data Science professionals from 2020 to 2022. Dive into the details of this insightful dashboard that sheds light on various aspects of employee demographics, compensation, and work trends.

** Detailed Visualizations: **

Total Employee Count: Track the growth of the Data Science team over the three-year period. Average Salary Analysis: Explore the average salaries for each year (2020, 2021, and 2022) to identify trends and fluctuations. Company Size Distribution: Visualize the distribution of employees across small, medium, and largesized companies where Data Science professionals worked. Salary Distribution by Job Titles: Gain insights into salary distribution across different job titles through a clustered bar chart. Experience Level and Employment Type: Analyze the distribution of employees based on experience levels and employment types through an informative table. Remote Work Ratio: Understand the proportion of remote work over time through a stacked area chart, correlated with salary levels in USD. Interactive Slicers: Use drop-down slicers for job titles and work years to customize your data exploration experience. **Key Takeaways and Insights: **

Identify hiring trends and patterns in the Data Science field over the three-year period. Understand salary distributions based on job titles and experience levels. Gain insights into the prevalence of remote work and its correlation with salary levels. Explore the impact of company size on employment opportunities and compensation. ** Unlocking Insights for Decision-Making: **

This Power BI dashboard provides valuable insights for HR professionals, hiring managers, and Data Science enthusiasts alike. Use the interactive features to drill down into specific segments and extract actionable insights for strategic decision-making. Leverage the visualizations to inform recruitment strategies, salary negotiations, and workforce planning initiatives. **Ready to Explore the Future of Data Science Employment? **

Dive into this comprehensive dashboard to uncover trends, patterns, and insights that drive the Data Science industry forward. Let's connect to discuss how these insights can inform your business strategies and propel your organization towards success.

In this report, i will be analyzing the major multinational companies across Brasil using several some economic and company performance indicators by year and by month from 2016 to 2018.

Average Review Performance by product category in compares to the Total revenues. All these reveals some interesting insights.

Global Revenue & Customer Insights with Power BI

Just wrapped up an interactive Power BI Dashboard analyzing 2011 retail data! This project highlights key trends in global revenue, demand, and customer behavior.

🔎 Key Insights: ✅ Monthly Revenue Trends 📉 ✅ Country-Wise Demand 🌍 ✅ Customer Revenue Segmentation 🛒 ✅ Seasonal Analysis with Filters 🌸❄

💡 Skills Applied: 🔹 Power BI for Data Visualization 🔹 DAX for Advanced Calculations 🔹 Data Transformation with Power Query 🔹 Data Storytelling for Business Insights

🚀 Business Impact: ✔ Identify growth opportunities ✔ Understand customer preferences ✔ Optimize sales strategies

Completed a job simulation where I strengthened my PowerBI skills to better understand clients and their data visualisation needs. Demonstrated expertise in data visualization through the creation of Power BI dashboards that effectively conveyed KPIs, showcasing the ability to respond to client requests with well-designed solutions. Strong communication skills reflected in the concise and informative email communication with engagement partners, delivering valuable insights and actionable suggestions based on data analysis. Leveraged analytical problem-solving skills to examine HR data, particularly focusing on gender-related KPIs, and identified root causes for gender balance issues at the executive management level, highlighting a commitment to data-driven decision-making.

SQL Create Context

Uncovering Implications and Insights

By Huggingface Hub [source]

About this dataset

This dataset contains a collection of questions and answers that have been contextualized to reveal subtle implications and insights. It is focused on helping researchers gain a deeper understanding of how semantics, context, and other factors affect how people interpret and respond to various conversations about different topics. By exploring this dataset, researchers will be able to uncover the underlying principles governing conversation styles, which can then be applied to better understand attitudes among different groups. With its comprehensive coverage of questions from a variety of sources around the web, this dataset offers an invaluable resource for those looking to sleep analyze discourse in terms of sentiment analysis or opinion mining

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

How to Use This Dataset

This dataset contains a collection of contextualized questions and answers extracted from various sources around the web, which can be useful for exploring implications and insights. To get started with the dataset:

• Read through the headings on each column in order to understand the data that has been collected - this will help you identify which pieces of information are relevant for your research project.
• Explore each column and view what types of responses have been given in response to particular questions or topics - this will give you an idea as to how people interpret specific topics differently when presented with different contexts or circumstances.
• Next, analyze the responses looking for any patterns or correlations between responses on different topics or contexts - this can help reveal implications and insights previously unknown to you about a particular subject matter. You can also use any data visualization tools such as Tableau or PowerBI to gain deeper understanding into the results and trends within your data set!
• Finally, use these findings to better inform your project by tailoring future questions around any patterns discovered within your analysis!

Research Ideas

• To understand the nature of public debates and how people express their opinions in different contexts.
• To better comprehend the implicit attitudes and assumptions inherent in language use, providing insight into discourse norms on a range of issues.
• To gain insight into the use of rhetorical devices, such as exaggeration and deceptive tactics, used to influence public opinion on important topics

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv | Column name | Description | |:--------------|:-----------------------------------------------------------------------------| | context | The context in which the question was asked and the answer was given. (Text) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Huggingface Hub.

𝗢𝗻𝗹𝗶𝗻𝗲 𝗦𝘂𝗽𝗲𝗿 𝗦𝘁𝗼𝗿𝗲 𝗦𝗮𝗹𝗲𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 📊

Hello Kaggle Community!👋 Check out my new Unguided Power BI End-to-End Practice Project. The objective is to conduct a complete analysis of historical online sales data to identify trends, patterns, and anomalies impacting revenue growth. Quantify the impact of key performance indicators (KPIs) on overall sales performance and provide data-driven recommendations. Deliver a detailed report outlining findings, insights, and strategic recommendations to stimulate revenue growth and enhance business performance. 📈📊

I decided to try something new this time by recording myself and giving an overview of the entire project as if I were presenting to senior stakeholders. I thought it would help me improve my storytelling skills, according to the current industry. There's definitely a lot of room for improvement and your invaluable feedback will be instrumental in identifying those areas.

DataAnalytics #DataAnalysis #DataAnalyst #OnlineStoreSales #PowerBI #DataStoryTelling #BusinessIntelligence #DataVisualization #DataDrivenInsights

McKinsey Solve Global Assessment Dataset (2018–2025)

🧠 Context

McKinsey's Solve is a gamified problem-solving assessment used globally in the consulting firm’s recruitment process. This dataset simulates assessment results across geographies, education levels, and roles over a 7-year period. It aims to provide deep insights into performance trends, candidate readiness, resume quality, and cognitive task outcomes.

📌 Inspiration & Purpose

Inspired by McKinsey’s real-world assessment framework, this dataset was designed to enable: - Exploratory Data Analysis (EDA) - Recruitment trend analysis - Gamified performance modelling - Dashboard development in Excel / Power BI - Resume and education impact evaluation - Regional performance benchmarking - Data storytelling for portfolio projects

Whether you're building dashboards or training models, this dataset offers practical and relatable data for HR analytics and consulting use cases.

🔍 Dataset Source

• Data generated by Oluwademilade Adeniyi (Demibolt) with the assistance of ChatGPT by OpenAI Structure and logic inspired by McKinsey’s public-facing Solve information, including role categories, game types (Ecosystem, Redrock, Seawolf), education tiers, and global office locations The entire dataset is synthetic and designed for analytical learning, ethical use, and professional development

🧾 Dataset Structure

This dataset includes 4,000 rows and the following columns: - Testtaker ID: Unique identifier - Country / Region: Geographic segmentation - Gender / Age: Demographics - Year: Assessment year (2018–2025) - Highest Level of Education: From high school to PhD / MBA - School or University Attended: Mapped to country and education level - First-generation University Student: Yes/No - Employment Status: Student, Employed, Unemployed - Role Applied For and Department / Interest: Business/tech disciplines - Past Test Taker: Indicates repeat attempts - Prepared with Online Materials: Indicates test prep involvement - Desired Office Location: Mapped to McKinsey's international offices - Ecosystem / Redrock / Seawolf (%): Game performance scores - Time Spent on Each Game (mins) - Total Product Score: Average of the 3 game scores - Process Score: A secondary assessment component - Resume Score: Scored based on education prestige, role fit, and clarity - Total Assessment Score (%): Final decision metric - Status (Pass/Fail): Based on total score ≥ 75%

✅ Why Use This Dataset

• Benchmark educational and regional trends in global assessments
• Build KPI cards, donut charts, histograms, or speedometer visuals
• Train pass/fail classifiers or regression models
• Segment job applicants by role, location, or game behaviour
• Showcase portfolio skills across Excel, SQL, Power BI, Python, or R
• Test dashboards or predictive logic in a business-relevant scenario

💡 Credit & Collaboration

• Data Creator: Oluwademilade Adeniyi (Me) (LinkedIn, Twitter, GitHub, Medium)
• Collaborator: ChatGPT by OpenAI
• Inspired by: McKinsey & Company’s Solve Assessment

This project involves analyzing a dataset of 2,240 customers from Maven Marketing to improve marketing strategies. By segmenting customers based on demographics and behavior, evaluating the success of past campaigns, and assessing channel performance, the goal is to uncover actionable insights that can drive future marketing efforts. The analysis will focus on identifying customer preferences, optimizing campaign strategies, and maximizing ROI. Using tools like Excel and Power BI, the project aims to create data-driven solutions for better customer engagement and business growth.

Introduction The following analysis is on the case study that I have been working on as a junior data analyst in marketing department on bikeshare trip data. For the case study I have used Power Query and PowerBI to perform analysis.

About the Company Divvy is the Chicago based bike sharing company, which launched bike sharing program in 2016, and received great response from their audiences. Their competitive advantage was the flexibility in pricing strategy: single-ride passes, full-day passes and annual membership. Customers who purchased Single-ride and full day were casual riders and those who purchased annual passes were members.

They offered 3 types of bike rides - Electric, classic and docked. The main goal of the company is to convert the casual riders to members, develop the marketing strategy that targets new customers as well as casual riders and to use digital media to influence casual riders.

Project stakeholders • Director of marketing • Marketing analytics team • Executive Team

Key tasks • Data validation • Data collection • Data manipulation • Data transformation • Data analysis • Data visualization • Recommendations

Project Objectives • How do annual members and casual riders use cyclistic bikes differently? • Why would casual riders buy cyclistic annual memberships? • How can cyclistic use digital media to influence casual riders to become members?

Methodology The data has been made available by Motivate International Inc. under this https://divvy-tripdata.s3.amazonaws.com/index.html. The analysis has been done from January 2023-December 2023 in CSV format. With the help of PowerBI, 12 months of data was merged and transformed, removing the duplicates and formatting. The extra data columns were removed, ‘ride length’ was calculated implanting Power Query formula using ‘started’ and ‘ended’ columns. ‘Day of week’ was assigned through ‘started’ Column using Power Query. New columns were added to separate time and date and were named as ‘Start Hour’ and ‘End hour’. Later, closing and loading the data for analysis. New measures were added to the data table using DAX function, to calculate ‘Average ride length’, ‘Number of rides’, ‘Casual riders count’ and ‘member riders count’.
Before we dive into the data visualization, lets get a grip of the types of bikes company is providing and their functions. Overall there are 3 kinds – Electric, Classic and docked.

Electric Bike • Equipped with motor and battery. • Assist while pedaling. • Best to cover more ground and uphill climbs.

Classic bike • Traditional bike without any battery or motor. • Easy to use. • Best for workout.

Docked Bike • Station-based system bikes. • Fixed station. • Best for planned rides.

Analyze • The data shows that casual riders count was 2M which accounted 36% of the company’s audience. • 1.1M of casual riders opted electric bike which is more than 50% of them, 876,881 of casual riders opted for classic bike and 78,287 of casual riders opted for docked bike. • There has been an increase in casual riders after Q1, which was gradually falling by the end of Q4. This also means that summer is the highest peak for casual riders with highest use of electric bikes followed by classic bikes. Chances of tourists are higher in those casual riders.

• Time of the day: Member riders number increased between 5 AM – 9 AM and then again saw a peak between 3PM- 5PM, which clearly state the office hour, college/university timing. • Casual riders were also seen using by 6AM but there was a gradual increase in their usage till 5PM. • Comparing the usage of bikes between casual riders and member riders, members were using bikes more during weekdays, assuming because of work, whereas casual riders were using bikes more during weekend. • Q2 and Q3 are the busiest time of the year for the company for both members and casual riders.

Recommendation In this case, my suggestion as a junior data analyst of marketing team to the company would be: • Understanding the target market and user preferences, company should focus on coming up with the marketing strategy that is focusing on locals being casual riders as well as tourist being casual riders. • For locals, two different promotion strategies should be made, one for classic bike and another for electric bike. As classic bikes are used more by casual riders, keeping the fitness category, the promotion should include points/reward system which they can redeem anytime in the month especially if they choose the weekend they get extra benefit like a free ride, a tracker on the bike which encourage when to do next and how long should be the ride. • For electric bike users, keeping the distance in mind, the promotion should include points/reward system which they can redeem anytime in month such as they get free next ride if they sign up and can enjoy one day free ...