Excel
Project
PowerBI
Project
SQL
Project
.png)
Looker
Project
.png)
QuickSight
Project
.png)
Tableau
Project
.png)
Others
Projects
Track key metrics like profit, riders, and profit margins. Interactive visuals reveal seasonal revenue trends insights for 2021-2022 🚴♂️💡
Explore our Pilot Program's Churn Model 🔍! Discover how we use data to predict and address employee turnover.
🚗 Explore cutting-edge insights into Uber user behavior with our interactive dashboard, powered by MageAI.
📊 Dive into this dashboard for a detailed analysis of thyroid care delivery, trends, and geographic distribution across regions.
Explore a detailed analysis of commercial sales and personal finance trends 📊, showcasing data-driven insights from 2014 to 2017🌍.
📊 Dive into detailed Google Ads analytics! Explore trends in CTR, conversions, and costs from 2022 to 2024. Maximize your ad impact!
.png)
It provides a breakdown by loan grade, purpose, state, and other variables such as loan term and homeowner status.
🩺 Analyzing patient records, revenue trends, profits, and hospital performance with insightful graphs and charts 🏥 📈
🏨 Analyze hotel booking trends from 2015-2017, focusing on customer types, revenue, cancellations, and market segments 🌍
The dashboard aggregates supply chain data for Atliq Mart, presenting key metrics such as OT, IF, OTIF, LIFR, and VOFR.
The dashboards break down attrition by departments, gender, performance, and other demographic variables, providing attrition.
Optimizing wellness with data 💼! The dashboard tracks & rewards low absenteeism & healthy habits in the workplace.🏆🌿
Maximize sales with tailored offerings! Dive into data-driven strategies for coffee, chicken, and more across different store types. 📊💼
It provides a breakdown by loan grade, purpose, state, and other variables such as loan term and homeowner status.
The dashboard highlights sales distribution by pizza size and identifies top-selling pizza varieties, essential for inventory.
This dashboard is likely used by coffee shop managers or business analysts to monitor performance and make data-driven decisions.
Concerns about air quality are at the forefront for many today. This deep dive into data gives us a snapshot of major air pollutants in the U.S.
Analyzing Airbnb's influence on NYC's lodging landscape through big data visualization.
The charts and maps presented act as navigational instruments through the city's, offering an examination of the Airbnb market.
This Dashboard pertains to road accident statistics for a specific year.
The fluctuating sales and order trends underscore the need for an in-depth analysis to identify market behavior.
The focus here is on the relationship between credit history, co-applicants, and loan term length on loan approval outcomes.
Project aims to provide investors with comprehensive insights, enabling informed investment decisions aligned.
It highlights the massive scale of the pandemic with data selectors to view specific aspects like confirmed cases or deaths and detailing.
Unveiling a dynamic Power BI dashboard 📊 for savvy hotel revenue growth analysis, smart parking insights, and seasonal trend 💹
The dashboard provides insights into individual agent performance, such as call answer speed, call duration, and customer satisfaction rates.
The profit section indicates the ability to filter by different attributes such as category, sub-category, region, and by sales or profit.
The pivot table or a dashboard that is part of a larger analysis on Uber data. It includes various metrics such as unique start and stop counts.
⚡📊 EV Performance Lab: A Power BI Dashboard showcasing the surge in electric vehicle adoption with key market insights & trends. 🚗💡
The IPL is a professional Twenty20 cricket league in India, which is one of the most popular sporting events in the country.
The airlines involved range from major carriers like Delta Air Lines Inc. and United Air Lines Inc. to smaller companies like Mesa Airlines Inc.
Harnessing data insights, we navigate trends, predict behaviors, and craft targeted interventions to foster lasting customer loyalty.
It includes various statistics related to the pandemic's impact globally. The graphic displays key figures such as the death rate.
The visualization series appears to focus on global health metrics, particularly life expectancy, and may include related health.
Analyzing sales data to discern trends and consumer behavior for targeted marketing and business growth through data cleansing.
Data analysis project uses SQL to gain customer behavior insights for marketing strategies and business growth optimization.
Retail data analysis using SQL to identify sales trends, evaluate strategy, and enhance marketing and inventory decisions.
Data Analyst
© 2024
All Rights Reserved
Designed By Anush Bharathwaj
It presents various data visualizations related to coffee sales, such as total sales, footfall, average bill per person, and the average number of orders per person.
There are also charts showing quantity ordered by hour, category distribution based on sales, order size distribution, footfall over various locations, and top 5 products based on sales.
A weekend order analysis is included as well. The data seems to offer insights into consumer behavior and sales trends for a business focused on coffee and related products.
The spreadsheet details individual coffee shop transactions, tracking sales data across products, prices, and locations for analysis.
(Cleaned By PowerQuery)
The pivot table summarizes sales, showing product popularity and revenue contribution for strategic business decisions.
The dashboard "Cup by Cup" presents an intricate analysis of coffee consumption patterns. It displays a total sales figure of $6,998,812.33 and 149,116 footfalls, with an average bill of $4.69 per person and approximately 1.44 items per order.
The consumption is broken down by the hours of the day, showing a peak in the morning hours. It highlights the sales distribution across various categories, predominantly coffee at 60%, followed by drinking chocolate and coffee beans.
Footfall data is segmented by location with Lower Manhattan leading. The top 5 selling products are graphically represented, alongside a focus on weekend sales patterns across different days.
Recommended Analysis:
• How do sales vary by day of the week and hour of the day?
• Are there any peak times for sales activity?
• What is the total sales revenue for each month?
• How do sales vary across different store locations?
AtliQ Hardware is a consumer electronics company expanding rapidly but is not able to compete with other companies using data as most of their report still exists in Excel.My goal is to implement an advanced analytics solution using Power BI that will enable company to get insights to make decisions
User-empathetic landing page as a part of dashboard design
Data modelling involving 10+ tables
In this project, the goal is to be one report which could be
used by stakeholders from sales, marketing, finance and
executive team. The focus is made on the following
1. Robust Data Modeling
2. User-empathetic Report design
3. Drillable Insights
I learned the following things in this project
1. Power Query (Basic and Advanced Operations)
2. Basic and complex DAX formulas
3. Data modelling involving 10+ tables
4. Choosing the right visuals and formatting
5. Dashboard designing principles
6. Using bookmarks
7. Deploying in Power BI service
8. Stakeholder Feedback Implementation
9. Sales, Marketing, Finance & Supply Chain metrics
A comprehensive study on loan applications, using vibrant visuals to present insights into approval rates based on credit history, co-applicant status, loan duration, and applicant attributes such as gender, marital status, education, and income.
It highlights patterns and disparities in loan sanctions across various demographic and financial categories.
The visuals link credit history, co-applicant presence, and loan term length to loan approval outcomes, revealing key decision factors.
The chart examines loan approval rates across gender, marital status, education, and income brackets, indicating demographic influences on lending.
The project is an in-depth analysis of loan application outcomes using Tableau visualizations. It explores the influence of credit history, co-applicant status, and loan terms on the likelihood of loan approval.
The study further investigates applicant demographics, including gender, marital status, education, and income, providing a clear understanding of their impact on loan decisions.
It also examines how these factors correlate with the approval process, highlighting potential biases or trends in lending practices. By presenting the data in a visual format, the study makes complex information easily accessible for insights into financial lending patterns.
Recommended:
1.How does credit history correlate with loan approval rates?
2.What impact does having a co-applicant have on the likelihood of loan approval?
3.Are there significant differences in loan approval rates between different loan terms?
4.Does gender play a role in loan approval, and if so, how?
5.How does marital status affect loan approval rates?
6.What is the relationship between the level of education and loan approval outcomes?
This project visualizes global data across environmental, governance, and social indicators, showcasing trends and regional differences over time.
Interactive maps and charts facilitate comparisons and insights into areas such as emissions, energy use, and government effectiveness, highlighting progress and challenges from 1990 to 2020.
Time series line chart shows regional forest depletion as a percentage of GNI, useful for environmental economic impact studies.
Global map overlay indicates regions for environmental, governance, societal factors; valuable for geopolitical and sustainability analysis.
The ESG_VIRTUSA_PROJECT is a data visualization initiative that presents an extensive analysis of environmental, social, and governance metrics globally.
Leveraging a combination of line graphs, bar charts, and geographical mappings, it reveals temporal trends in critical areas like forest depletion, renewable energy consumption, and government expenditure on education. The project allows for an intuitive comparison between regions and over a 30-year span, from 1990 to 2020.
It aims to provide stakeholders with clear insights into sustainability and policy effectiveness, promoting informed decision-making regarding environmental stewardship, social responsibility, and governance practices. The analytical framework underscores significant developments and areas necessitating attention.
Recommended Analysis:
1. How has the net forest depletion as a percentage of GNI changed across different regions from 1990 to 2020?
2.Which regions have shown the most significant improvement in government expenditure on education?
3.What trends are evident in greenhouse gas (GHG) net emissions/removals by land use, land-use change, and forestry (LULCF) over the past three decades?
4.How does the mean annual exposure to PM2.5 air pollution compare across regions, and what are the implications for public health policy?
The dashboards provide a real-time view of COVID-19's impact, showing vaccination rates, case counts, and testing across regions and demographics in India, as well as a global perspective on confirmed cases.
State-specific distribution of ICMR testing labs and gender-wise vaccination data displayed in bar graphs for targeted public health analysis.
Geographic visualization of COVID-19 death toll by state with gradation, aiding in identifying areas with the highest fatalities.
These comprehensive dashboards serve as a dynamic tool for analyzing COVID-19 data. For India, they detail state-wise vaccine administration, death rates, and laboratory testing capacity, breaking down demographics by gender and age. Globally, they offer a visual comparison of confirmed cases and deaths, showcasing top affected countries and trends over time.
By utilizing color-coded maps and charts, these dashboards facilitate a clear understanding of the pandemic's spread and the response effectiveness.
Recommended Analysis:
1.Which Indian states have the highest vaccination rates and how do they correlate with COVID-19 case counts?
2.How effective has the vaccination rollout been in reducing death rates across India?
3.What is the gender distribution of vaccinated individuals in India?
4.How do positive COVID-19 test rates compare among Indian states with different testing capacities?
5.Which countries are currently experiencing the highest rates of new COVID-19 cases?
6.How has the trend of daily confirmed COVID-19 cases evolved globally since the beginning of the pandemic?
This business intelligence dashboard provides a detailed analysis of profit distribution across regions, states, and cities, alongside profit trends and growth changes over time, enabling strategic financial decisions.
The map shows profit distribution across states, indicating regional market strength and potential for business focus areas.
The line chart depicts monthly profit growth, highlighting seasonality and potential market shifts or operational influences over time.
The dashboard visualizes profit analytics for a company, displaying data through various graphical representations. It shows profit margins by regions and states, identifies profitable cities, and traces profit trends and growth changes over a four-year period. Such detailed insights empower businesses to understand market dynamics, regional performance, and temporal trends in profitability.
The dashboard aids in pinpointing geographic areas with high earnings and those needing attention, optimizing resource allocation, and strategic planning for market expansion.
Recommended Analysis:
1.Which region consistently showed the highest profit over the last four years?
2.How did profit trends fluctuate between different regions from 2017 to 2020?
3.Which state emerged as the most profitable in the most recent year?
4.Are there any cities that show a pattern of consistent growth in profits?
5.How did specific events or seasons affect profit growth in various regions?
6.What is the correlation between the number of sales and profit in different regions?
7.Can we predict future profit trends based on historical data from the most and least profitable cities?
This pivot table analysis delves into ride-sharing data to unlock patterns in urban transportation. Tracking key metrics such as start and stop locations, miles traveled, and average speed, the project reveals the dynamics of city transit and identifies efficiency benchmarks for optimizing ride-sharing services.
For rideshare trips, focusing on frequency, distance, and speed metrics categorized by business and personal travel, potentially to optimize service efficiency.
The spreadsheet implies meticulous data cleaning: Red highlights indicate modifications to date formats, ensuring consistency for accurate temporal analysis.
The 'Uber Analytics' project leverages a pivot table to dissect a substantial dataset detailing urban rides. It meticulously categorizes rides by origin, destination, distance, and purpose, affording a granular view of mobility patterns. Insights into the most frequented routes, average speeds per trip category, and discrepancies in start-stop counts offer stakeholders data-driven leverage to enhance service allocation.
Distinct segments such as business and personal travel are contrasted, highlighting usage variances that inform strategic decisions like pricing and marketing. The analysis serves as a foundation for improving user experience and operational efficiency within the ride-sharing market.
Recommended Analysis:
1.Which locations show the highest discrepancy between start and stop counts, indicating potential unmet demand or surplus capacity?
2.What is the average journey distance per category, and how does this correlate with the frequency of trips?
3.How does the average speed vary by purpose, and what does this suggest about the efficiency of different trip types?
4.Which time of day sees the highest number of trips, and how can this inform staffing or resource allocation?
5.Can we identify any outliers in terms of speed or distance that might indicate data entry errors or exceptional cases?
6.What are the common characteristics of the most profitable routes or times, and how can we leverage this information?
This dashboard presents a summary of various statistics from the Indian Premier League (IPL). It features the IPL title winner, top individual scorers for runs (Orange Cap) and wickets (Purple Cap), and the number of boundaries and sixes per season.
Additionally, it provides insights into match outcomes relative to the coin toss.
The bar graph displays match outcomes in relation to the toss decision for various teams in the IPL.
Showcases the Chennai Super Kings as the two-time winners of the IPL, emphasizing their success.
The dashboard presents a detailed statistical analysis of the IPL 2011 season, highlighting Chennai Super Kings as the champions. The Orange Cap was awarded to CH Gayle for his exceptional 608-run season, while SL Malinga earned the Purple Cap for taking 28 wickets.
A numerical breakdown of boundaries per season is shown, with 1,916 fours and 639 sixes. The bar chart and pie chart at the bottom depict match outcomes post-toss decisions, illustrating a significant correlation between winning the toss and the match, with a 65.75% win rate for the toss-winning teams.
Recommended Analysis:
1.What is the historical success rate of the IPL title winner in subsequent seasons, and does winning the toss have a statistically significant impact on their success?
2.How does the individual performance of Orange Cap and Purple Cap winners correlate with their teams' chances of reaching the playoffs or winning the championship?
3.Is there a relationship between the number of fours and sixes scored in a season and the final league standings or success in knockout matches?
4.What are the factors that contribute to a team's high percentage of wins after winning the toss, and how can teams mitigate the disadvantage of losing the toss?
5.Can the data from the most successful bowlers and batsmen, as indicated by the Purple and Orange Caps, be used to predict their future performance and market value?
The project provides insights into airline operational efficiency, highlighting patterns in flight cancellations, delays, and on-time performance, as well as traffic distribution across different times of the day.
The bar chart compares weekly flight status, showing flights delayed, on-time, and canceled for each day of the week.
The image shows a comparative bar chart of airline flights for 2009 and 2010, highlighting changes in flight frequency for different airlines.
This project analyzes airline performance metrics by visualizing data on flight cancellations, delays, and punctuality across different airlines and timeframes. The first image reveals trends in airline performance over various days of the week, with a pie chart quantifying the proportion of on-time, delayed, and canceled flights.
The second image sheds light on peak traffic times, illustrating that early mornings and midday are the busiest hours for flights. This data could be instrumental for airlines in optimizing flight schedules, improving customer satisfaction, and managing resources more efficiently to reduce operational disruptions.
Recommended Analysis
1.What is the relationship between flight delays/cancellations and specific days of the week?
2.How does flight punctuality compare across different airlines?
3.Is there a significant difference in the number of flights during various months or seasons?
4.Which time of day experiences the highest number of flights, and how does this impact delay and cancellation rates?
5.How does the average delay time correlate with the airline's on-time performance percentage?
6.Are certain airlines more prone to delays at specific airports?
The project delves into customer retention metrics for a service-based company, featuring an analysis of 7,043 total customers and a churn rate of 26.54%. It assesses customer demographics and behavior, such as gender, presence of dependents, senior citizen status, and subscription details.
The analysis also explores the impact of billing practices and service usage on customer loyalty. Payment methods like electronic checks, paperless billing, and plan types ranging from month-to-month to annual subscriptions are correlated with customer churn rates.
The bar chart indicates customer churn increases as the number of services used decreases, suggesting higher retention with more services.
The line graph shows churn rate consistency across various customer tenure lengths, implying tenure has a minor impact on churn.
This analysis project examines customer retention for a company, totaling 7043 customers with a churn rate of 26.54%. It evaluates churn across customer demographics like gender, dependents, and senior citizenship, service aspects like paperless billing, and engagement through total services used.
Payment methods' impact on churn is also reviewed, including electronic checks and automatic bank transfers. Plan types, monthly-to-annual, are compared to see how they affect loyalty.
The detailed breakdown of these factors helps the company identify where they're losing customers and could inform targeted retention strategies to reduce the churn rate.
Recommended Analysis:
1.How does the churn rate differ between male and female customers?
2.Is there a significant churn rate difference between customers with dependents and those without?
3.What is the impact of being a senior citizen on customer churn?
4.Does the method of payment (electronic check, mailed check, bank transfer, credit card) correlate with a higher churn rate?
5.How does engagement with multiple services influence the likelihood of a customer churning?
6.What is the effect of paperless billing on customer retention?
The visualization presents a global COVID-19 analysis by Anush Bharathwaj, highlighting 1.41 billion total cases with a 0.99% death rate and 86.84% recovery rate. It shows 13.96 million total deaths, zero new deaths, and 23,000 new recoveries.
A bar graph categorizes regions into red and safe zones based on case numbers, and a detailed continent-wise breakdown illustrates total cases, deaths, and recoveries, with Europe, Asia, and North America as the most affected regions.
Shows total COVID-19 cases, deaths, recoveries by continent, indicating Asia and North America as most affected regions.
Depicts COVID-19 impact across continents with a 'red zone' representing higher case numbers, 'safe zone' lower cases.
This detailed COVID-19 dashboard created by Anush Bharathwaj offers a comprehensive breakdown of the pandemic's impact, showing a total of 1.41 billion cases worldwide, a death rate of 0.99%, and a significant recovery rate of 86.84%. It records 13.96 million deaths alongside a hopeful count of zero new deaths and a substantial number of new recoveries at 23,000.
The analysis uses a bar graph to differentiate between red and safe zones by continent. Additionally, a continent-specific tally displays the total cases, deaths, and recovery figures, with Europe, Asia, and North America leading in numbers, emphasizing the extensive reach and ongoing challenge of the pandemic.
Recommeded Analysis:
1. What factors contribute to the variance in death rates across different continents?
2.How has the recovery rate changed over time and what might be influencing this trend?
3.Can we identify a correlation between the number of new cases and the rate of increase in total cases?
4.What impact do regional containment strategies have on the distribution of red and safe zones?
5.How does the distribution of total cases relate to the population density of each continent?
6.What is the relationship between the number of active cases and the subsequent number of deaths?
The project offers a comprehensive analysis of global health indicators, focusing on life expectancy, BMI, thinness in youth, and mortality rates, as well as vaccination coverage and alcohol consumption by country.
It contrasts developed and developing countries, emphasizing disparities in health outcomes.
It indicates the frequency of countries associated with life expectancy data; larger text suggests more prominent data points or higher relevance.
A bar and line graph comparing life expectancy and GDP by country, indicating a potential correlation between them.
In detail, the project utilizes an array of visual data representations, including bar graphs, pie charts, and tables, to compare life expectancy, BMI, and mortality rates among various countries.
The visuals emphasize the stark contrast between developed and developing nations, with specific attention given to the health challenges faced by countries with high infant and adult mortality rates.
Additionally, the project includes a detailed table showcasing the prevalence of HIV/AIDS, alcohol consumption, diphtheria, hepatitis B, measles, and polio, alongside the GDP data to illustrate the potential correlation between economic status and health outcomes.
Recommended Analysis:
1.What is the relationship between life expectancy and various health indicators such as BMI, thinness, and vaccination rates across different countries?
2.How do adult and infant mortality rates compare between developed and developing nations, and what might be the underlying causes for any disparities?
3.Can we identify any trends or patterns in the prevalence of diseases such as HIV/AIDS, hepatitis B, diphtheria, and polio in relation to a country's economic status?
4.How does alcohol consumption correlate with health outcomes within the countries studied?
5.What insights can be drawn from the comparison of total health expenditure and the percentage of expenditure by status (developed vs. developing) in relation to health outcomes?
6.Considering the data on infant and under-five mortality rates by country, what specific health interventions might be most effective in these regions?
7.Is there a significant correlation between GDP and the health status of a country's population, as evidenced by life expectancy and mortality rates?
This SQL project involves analyzing a dataset of clean weekly sales, structured around various dimensions such as date, month, segment, demographic, platform, and region. Utilizing SQL queries, the data is manipulated for insights, focusing on transactions, sales averages, and customer demographics across different geographical regions.
The project aims to identify patterns and trends in consumer behavior, providing actionable insights for targeted marketing strategies and business growth through meticulous data cleansing and analysis.
This SQL query analyzes the highest grossing age and demographic segments in retail, aiding targeted marketing strategies.
It's a SQL query for aggregating monthly sales by region, revealing regional market performance and temporal sales trends.
The dataset, clean_weekly_sales.csv, encapsulates sales data, including weekly transactions, average sales, and customer demographics such as age bands and segments, across different platforms and regions.
The SQL queries in DataMart_Queries.sql demonstrate the process of data extraction, transformation, and loading (ETL), emphasizing data cleansing, aggregation, and analysis. The queries include creating a clean sales table, aggregating sales data, and enhancing data quality by handling null values and categorizing demographics.
This analytical endeavor supports decision-making by unveiling customer purchasing patterns, seasonal trends, and market opportunities, fostering a data-driven approach to enhance business operations and strategy.
Recommended Analysis:
1.How do you handle null values in the dataset to maintain data integrity?
2.What method is used to categorize segments into known values versus unknowns?
3.How do you aggregate sales data to analyze trends over time?
4.What approach is used to identify demographic patterns within sales transactions?
5.Which age_band and demographic values contribute the most to Retail sales?
6.What is the percentage of sales by demographic for each year in the dataset?
7.What is the percentage of sales for Retail vs Shopify for each month?
The project harnesses multiple datasets to uncover insights into customer behaviors and regional sales dynamics. Utilizing SQL queries, it analyzes customer transactions, node distributions, and regional data to explore network structures and financial activities.
The aim is to enhance customer relationship management and optimize the Data Bank network's performance, providing a comprehensive view of the business ecosystem through the integration and interpretation of complex data points.
The query totals transaction amounts per region, providing financial activity insights, crucial for regional performance evaluation and strategy.
It examines monthly customer engagement, quantifying those making multiple transaction types, highlighting loyalty and banking activity.
This data analysis project encompasses three datasets: customer nodes, customer transactions, and regions. The focus is on dissecting the Data Bank network's composition and customer activity. The datamart_queries.sql file reveals targeted queries examining the uniqueness and regional distribution of network nodes, alongside the segmentation of customers within the network.
By profiling transactional behaviors and node analytics, the project seeks to refine marketing strategies, improve customer segmentation, and boost regional sales efforts. Ultimately, it offers strategic business insights that could inform decision-making processes, product development, and market expansion strategies within the Data Bank's operational framework.
Recommended Analysis:
1.What is the unique count and total amount for each transaction type?
2.What is the average number and size of past deposits across all customers?
3.Determine the total amount of transactions for each region name?
4.How many nodes are there in each region?
5.For each month - how many Data Bank customers make more than 1 deposit and at least either 1 purchase or 1 withdrawal in a single month?
This project is an extensive analysis of retail data, leveraging SQL queries to explore product details, pricing, hierarchy, and sales.
The initiative aims to unravel sales patterns, product popularity, and revenue generation, focusing on customer transactions and product offerings.
It is a deep dive into the sales strategy's efficacy, assessing the impact of discounts and memberships on purchasing behaviors, and providing insights for inventory management, marketing campaigns, and customer engagement enhancement.
The query assesses total sales quantity, revenue, and discounts by product segment, providing insights on segment performance and customer preferences.
The query calculates the average number of unique products bought per transaction, indicating customer variety-seeking behavior and purchase diversity.
The datasets—product details, product hierarchy, prices, and sales—serve as the project's backbone, providing a multi-dimensional view of the retail operations at Texture Tales. SQL queries from TextureTales_queries.sql dissect these datasets to calculate total sales quantities, revenue before discounts, and more.
This project is not just about numbers but about understanding what drives sales and how product offerings align with market demand. By evaluating product performance across categories and styles, the analysis informs strategic decisions on pricing, promotions, and stock levels.
The end goal is to optimize Texture Tales market positioning and profitability through data-driven insights.
Recommended Analysis:
1.What was the total quantity sold for all products?
2.What is the total generated revenue for all products before discounts?
3.What was the total discount amount for all products?
4.How many unique transactions were there?
5.What are the average unique products purchased in each transaction?
It captures total revenue, order value, and pizza categories, illustrating consumer preferences and peak sales times.
Through visualizations, it showcases the top and bottom performers in sales and quantity, enabling a strategic approach to inventory and marketing, and helps identify prime hours and weeks for targeted promotions, enhancing overall sales performance.
It indicates sales percentage by pizza size, showing large pizzas dominate sales, highlighting customer preference for size.
It shows the top five pizzas by total orders, suggesting popularity and potential stock priority for these flavors.
This detailed analysis is presented through an interactive dashboard reflecting a year's performance from 2015. "Pizzanomics" analyzes key metrics such as total revenue, which amounts to $817.9K, average order value, total pizzas sold, and the average number per order.
It dives deeper to reveal busiest hours, peak weeks, and sales by category and size, with an emphasis on customer buying patterns. The project identifies the best and worst-selling pizzas, underlining the significance of menu optimization.
The insights facilitate precise decision-making on promotional strategies, menu engineering, and resource allocation to capitalize on high-demand periods and improve underperforming areas.
Recommended Analysis:
1.What factors contribute to peak pizza sales during the busiest hours and weeks?
2.How does pizza size preference impact total revenue, and does it correlate with the best-selling pizza categories?
3.What are the sales trends for the top-selling pizzas over time, and how do they compare with the bottom-selling ones?
4.How do discounts and order size affect the average order value?
5.Can we predict future sales patterns based on historical hourly and weekly sales trends?
This dashboard analyzes loan data, depicting key metrics such as total loan applications, funded amounts, and the division between good and bad loans, alongside average interest and debt-to-income ratios.
An infographic showing loan application data by month, state, purpose, term, and home ownership, with emphasis on trends and volumes.
(Cleaned By PowerQuery)
A SQL-generated table summarizing monthly loan applications, funded amounts, and total amounts received over a year.
The "Financial Flux," provides a comprehensive overview of loan dynamics, represented by a collection of interactive elements and visual analytics. It illustrates a broad spectrum of loan-related data for a banking institution, capturing the total number of loan applications, both monthly and year-over-year, with a distinct categorization of 'good' and 'bad' loans.
A good loan is one that is likely to be repaid, while a bad loan represents a higher risk of default. The visuals include a detailed breakdown of loans by grade, purpose, and the correlation of loans with variables such as employment length, home ownership status, and geographical distribution across states.
Furthermore, the data is segmented by loan terms and showcases trends in the average interest rate and debt-to-income (DTI) ratio, providing stakeholders with actionable insights into the financial health and risk profile of the loan portfolio.
Recommended Analysis:
1. How does the monthly trend in loan applications correlate with approval rates, and what seasonal or economic factors influence these trends?
2. Which loan grades have the highest default rates, and how can we adjust our risk assessment criteria to improve the quality ?
3. How do varying interest rates affect loan performance and borrower default rates across different loan types and grades?
4. Is there a significant correlation between borrowers' debt-to-income ratio and the likelihood of a loan being categorized as 'good' or 'bad'?
5. Which loan purposes and terms demonstrate the highest levels of borrower repayment and financial health?
6. How does home ownership status influence loan application numbers, funded amounts, and repayment rates, and what can this tell us about borrower stability?
The dashboard that maps out air pollution with precision. It breaks down the Air Quality Index by state and year, showing how pollution levels change over time and where they spike the most.
It’s not just numbers and graphs. it’s an interactive tool that lets us zoom in on the state of our air, literally and figuratively.
Pie chart illustrating the percentage of various air pollutants measured across four quarters.
Bar graph ranking states by Sulphur Dioxide Air Quality Index values.
This project appears to be an analytical review of air quality and pollution levels across various regions of the United States, as presented through a Power BI dashboard. The dashboards display data on key pollutants: Carbon Monoxide (CO), Ozone (O3), Sulphur Dioxide (SO2), and Nitrogen Dioxide (NO2).
Metrics such as the Air Quality Index (AQI) by year and by state, maximum hourly concentration levels affected, and the highest recorded values in specific cities are shown.
Interactive elements allow users to filter data by county, state, or time periods, enabling detailed analysis. The visualizations suggest a focus on tracking environmental trends, identifying pollution hotspots, and understanding the distribution and impact of various pollutants over time.
This project serves as a critical tool for environmental monitoring, policy-making, and public awareness, illustrating the need for sustained efforts in environmental health and safety.
Recommended Analysis
1. How has the annual average AQI for each of the primary pollutants (CO, O3, SO2, NO2) trended across the most affected states from 2006 to 2010?
2. Can we identify any seasonal variations in pollutant levels by examining quarterly AQI data, and if so, what might be the contributing factors to these variations?
3. Which cities have consistently exceeded the EPA’s recommended AQI thresholds for each pollutant, and what are the potential sources contributing to these high levels?
4. How do changes in the AQI for specific pollutants correlate with policy changes or environmental initiatives enacted during this period?
5. What are the comparative levels of pollutants between urban and rural areas, and how might this inform targeted environmental policy interventions?
6. Is there a discernible pattern in the data that indicates the effectiveness of air quality regulations implemented by different states?
7. How does the air quality data correlate with traffic congestion statistics in major metropolitan areas within the dataset?
In the heart of the urban jungle, New York City's Airbnb ecosystem is a testament to the city's ever-evolving accommodation landscape. With millions of reviews and a vast array of neighborhoods participating, this data represents not just places to stay, but the interconnected experiences of hosts and guests alike.
From the bustling avenues of Manhattan to the quaint corners of Staten Island, every data point offers insight into the unique pulse of city life and flow of the sharing economy.
Horizontal bar chart comparing average Airbnb prices across Manhattan neighborhoods, with Battery Park City being the most expensive.
Bar graph showing New York Airbnb bookings by borough and room type, with Manhattan leading in entire home/apartment rentals.
This project presents an intricate analysis of New York’s Airbnb landscape, illustrated through Power BI visualizations. The data spans several years and encompasses a variety of metrics such as total reviews per year, average pricing by neighborhood, and average reviews per month. Additionally, it provides insights into host activity and the distribution of property types across the city's neighborhoods.
The dashboard offers a detailed breakdown by borough, indicating a concentration of listings in Manhattan and Brooklyn. It includes geographical mapping of listings and a temporal distribution of bookings, highlighting peak times and seasonal trends. The visualization also ranks hosts based on the number of reviews, offering a glimpse into the most popular and potentially most reliable listings.
This detailed study serves as a powerful tool for understanding market dynamics, identifying trends in customer preferences, and guiding hosts on pricing strategies based on location and time of year.
Recommended Analysis
1. What factors might be influencing the disparity in Airbnb listing prices across Bronx neighborhoods?
2. Is there a noticeable relationship between the pricing of Airbnb listings and the frequency of their reviews within the Bronx area?
3. What seasonal patterns emerge from the monthly booking data for Airbnb properties, and what implications do these have for hosts?
4. What is the prevalence of different types of Airbnb accommodations (such as entire homes, private rooms, and shared rooms) within various New York City neighborhoods?
5. Does the average monthly review count vary significantly between different types of Airbnb rooms and boroughs, with a focus on the Bronx?
This project analyzes New York's Airbnb data, revealing patterns in guest preferences and host offerings. Visualizations include reviews by year, price variations by neighborhood, booking frequencies, and host rankings.
This extensive dataset encompasses trends across boroughs, room types, and pricing, providing a granular look at Airbnb's footprint in the city.
Line graph depicting monthly review counts for Airbnb, with a mid-year surge.
A bar chart showing Airbnb monthly review counts, peaking significantly in June.
Power BI dashboards synthesize data from over 48,000 listings, illustrating trends such as average prices, review counts, and booking volumes. Detailed charts compare neighborhoods, highlight seasonal booking fluctuations, and rank hosts based on reviews.
The data ranges from 2010 to 2020, tracking the platform's exponential growth. Neighborhood-specific pricing analysis offers insight into the city's diverse accommodation costs, with a comparative lens on room types and host activity.
This holistic approach not only serves stakeholders interested in the sharing economy's dynamics but also informs potential Airbnb hosts and city planners about the intricacies of supply and demand in urban short-term rentals.
Recommended Analysis
1. In which Neighbourhood group there is maximum number of properties listed ?
2. Which host has maximum number of properties listed ?
3. Which host has maximum properties listed in neighbourhood groups having maximum properties listed ?
4. What is the average price in different properties listed ?
5. What may be the reason of having high price in that neighbourhood groups ?
6. What is the most prefered room type in the every neighbourhood groups ?
7. Total availability of properties having different room type?
8. Which one is the busiest host ?
9. Which property has maximum number of reviews ?
The data showcases current year performance juxtaposed with prior year metrics, providing visibility into sales, costs, and orders over time.
It also includes detailed segmentation of product sales, customer orders, and shipping costs, offering a comprehensive view of the company’s operational dynamics.
Bar graph depicting monthly freight costs for the current year, showing a slight decrease from the previous year.
Data Modeling Overview: A Snowflake schema visualization linking various dimensions—Orders, Customers, Employees, Order Details, Shippers, Products—to a central Calendar table for comprehensive analysis.
This project delivers an in-depth analysis of Northwind Traders' business performance, leveraging BI tools to parse through sales data and operational indicators. The dashboards meticulously record and contrast current-year revenue, shipping expenses, and the volume of orders with historical data, providing a month-by-month overview.
Examining the products more closely uncovers those with the highest and lowest sales, shipping expenditures, and crucial revenue-driving customers.
Metrics such as daily revenue, logistics costs, and transaction counts are carefully mapped out, shedding light on top-selling items and leading customers. These insights offer a granular view into prevailing market conditions, efficacy of sales strategies, and potential areas for targeted enhancements.
Moreover, they furnish corporate strategists with data-driven insights into the popularity of products, seasonal sales variability, and consumer buying tendencies.
Recommended Analysis
1. What is causing the significant decline in the number of orders?
2. How are sales remaining stable despite the decrease in order volume?
3. Which products are driving the sales for Northwind Traders?
4. What is the pattern of sales distribution among customers?
5. Is Northwind Traders effectively managing its shipping costs?
The visualizations suggest a busy center handling 5,000 calls with 8 agents.
It seems focused on customer service efficiency, with a high rate of calls answered (81.08%) and a lower rejection rate (18.92%).
The distribution of calls among agents and call topics indicates a balanced workload and varied customer inquiries.
Pie chart illustrating the distribution of calls handled by agents in a call center.
Line graph showing daily call volume fluctuations in a call center during early months of the year.
The project visualizes a call center's operational metrics over a full year within an interactive dashboard.
It displays total calls, with a focus on those answered and unresolved, alongside agent performance statistics, including resolution rates and customer satisfaction.
The dashboard categorizes calls by subject, such as technical support or payment queries, and charts the efficiency of agents, detailing average response times and durations.
Monthly call patterns and agent-specific workloads are also analyzed, offering actionable insights for optimizing call center workflows, identifying training needs, and improving customer experience.
This analytical tool is pivotal for enhancing service quality and strategic planning in customer relations management.
Recommended Analysis
1. How does agent performance vary and what factors contribute to this variance?
2. What strategies can be implemented to improve the resolution rate of calls?
3. Does the call topic distribution suggest specific areas for focused agent development and resource management?
4. What insights can be gleaned from the relationship between call rejections, agents, and timing?
5. How does the length of calls influence customer contentment levels?
6. What strategies could enhance call response swiftness while upholding service excellence?
The company's 17% attrition rate is an important sign of operational inefficiencies and employee dissatisfaction in the context of the present HR dilemma.
The context here is identifying the primary and indirect causes of employee turnover, including workplace culture, professional growth possibilities, reward structures, management relationships, and engagement levels.
Infographic showing gender distribution with males at 63% and females at 37% in a specific group.
Donut charts displaying employee distribution across HR, R&D, and Sales departments.
Demographics are detailed with figures on age and gender ratios, as well as the marital status of employees. It probes deeper with job satisfaction metrics within various departments, giving a snapshot of employee engagement and workplace contentment.
Furthermore, it assesses attrition against variables like educational background and frequency of travel. By presenting this stratified information, the dashboard aids in discerning the underlying causes of turnover and equips management with the insights necessary to craft effective retention strategies.
Overall, it’s an informative tool for honing in on factors critical to employee longevity and organizational health.
Recommended Analysis
1. Which departmental reasons are responsible for the high rate of attrition in sales and R&D?
2. What is the relationship between attrition rates and work satisfaction, and are there any trends that stand out amongst departments?
3. Are there any noticeable patterns in attrition among the various age groups, and if so, what might be the causes of these patterns?
4. What part, if any, does an employee's educational background play in their chances of quitting, especially if they hold bachelor's degrees?
5. What factors are causing high-achieving workers to leave, and what steps can the organisation take to better retain this vital workforce segment?
6. Is there a mismatch between the company's reward system and its employees' expectations and values? If so, how may it be fixed?
The dataset indicates Atliq Mart's performance across several supply chain KPIs.
It includes visual analytics on order punctuality and completeness, which are essential for assessing customer service and inventory management.
The graphical representation allows for trend spotting and the identification of outliers, providing a basis for targeted supply chain interventions.
A colorful table and line graphs depicting 'Product Performance' with quantity, LIFR, and VORF percentages.
A line graph titled 'OTIF % Trend' showing averages, achieved percentages, and actual OTIF percentage over months.
The project revolves around a comprehensive analysis of Atliq Mart's supply chain, depicted through an interactive dashboard named FLOWMASTER. The objective is to scrutinize key performance indicators (KPIs) and customer service metrics to optimize the supply network's efficiency and reliability. These KPIs include on-time (OT) delivery percentages, in-full (IF) delivery rates, on-time and in-full (OTIF) delivery performance, along with line item fill rate (LIFR) and volume order fill rate (VORF).
In a detailed overview, the dashboard examines product performance and customer service levels, correlating them with order quantities and fulfillment accuracy. It showcases distribution patterns and highlights areas needing attention, such as delayed orders. Data is segmented by product categories like dairy, food, and beverages, as well as by cities to identify regional trends.
Customer-specific analysis reveals varying degrees of compliance with the set targets, providing insights into the supply chain's responsiveness to different customer needs. The trends in the delivery timelines, fulfillment accuracy, and order volume help identify strengths and weaknesses in the supply process. This assists Atliq Mart in driving strategic decisions to enhance overall customer satisfaction and operational efficiency. The project is a step towards a visionary approach in managing a supply chain, focusing on agility, customer-centricity, and data-driven decision-making.
Recommended Analysis
1) Why is there a marked fluctuation in the OT% across the different months, and what specific events or issues correlate with these changes?
2) Which products have the most significant discrepancies between ordered and delivered quantities as indicated by the LIFR%, and what might be causing these disparities?
3) How does the delivery performance (OTIF%) vary among our top customers, and can we identify any patterns or commonalities in the service issues they are facing?
4) What are the specific reasons for the underperformance in IF% in certain cities, and how does this relate to our distribution network in those areas?
5) Given the current trend in the OTIF% metric, what are the projected impacts on customer retention and satisfaction if these trends continue?
6) How do the delays categorized by 'Order Delay Day' relate to our inventory management and restocking processes?
7) What is the correlation between the OT% by city and the inventory levels at the corresponding local distribution centers?
The project presents an innovative solution to manage employee absenteeism by harnessing the power of HR analytics. It meticulously constructs a detailed database and utilizes sophisticated SQL queries to identify key factors influencing absenteeism.
The integration of this data into Power BI translates into a user-friendly dashboard, allowing HR professionals to observe absenteeism patterns and implement effective wellness incentives. The strategy focuses on encouraging healthy lifestyles among employees by offering bonuses for low absenteeism and incentivizing non-smokers with wage increases.
SQL Query to Classify BMI and Seasons, and Join Tables on Absence Reasons.
Scatter Plot of Average Workload Versus Transportation Expenses with Trend Line Over a Data Range.
The dashboard insights reveal a comprehensive view of absenteeism drivers among 740 employees. Key findings include the highest absenteeism (average of 6.92 hours per absence) occurring during winter, aligning with increased medical consultations, which top the reasons for absence at 161 instances.
Employees with higher education levels have a lower absenteeism rate, with 73% of higher-educated employees absent for fewer than 4 hours. Non-smokers demonstrate a notable 33% lower absence rate compared to smokers.
Recommended Analysis
1. How do smoking habits correlate with the frequency and duration of absenteeism?
2. What seasonal trends exist in absenteeism, and how do they correlate with reported medical conditions?
3. How does pet ownership affect employee absenteeism rates?
4. Is there a measurable impact of the healthy bonus program on reducing absenteeism rates among individuals identified as healthy?
This project explores the strategic offer distribution for a company's retail expansion, focusing on specific food and beverage options like Chicken, Pizza (Adv GnG), Coffee (Bean to Cup), Frozen Yogurt (Swirl World), and DoorDash services.
Leveraging data on sales performance, guest counts, and store types from existing locations, the aim is to recommend tailored offers for new stores across various states.
Analytical insights, drawn from averages and anomalies in the data, will guide the offer selection for three store types: Travel Centers, EDOs, and 5.5k stores, each with unique traffic patterns and customer bases.
Data Model Relationship Management Window in Excel Showing Active Table Connections
Excel Data Analysis of Store Performance Metrics with Sales and Expenses Across States.
The findings from the Store Type Offer Distribution Dashboard, aligned with the problem statement of optimizing product offers, indicate a strong correlation between store type and sales performance.
Travel Centers should focus on Bean Coffee, leveraging their 40.88% contribution to total sales. EDOs, with their diverse traffic, can increase Chicken sales, currently at 160.18K, by enhancing meal variety.
The 5.5K stores, situated in urban centers, show untapped potential in DoorDash collaborations, aiming to surpass the current 15.96K sales.
Data underscores the need for targeted offerings, with the inside count and days open serving as pivotal metrics for determining store performance and strategic offer placement.
Recommended Analysis
1. How do sales of Bean Coffee correlate with the store type and what specific factors contribute to the 28% sales performance in 5.5K stores?
2. What impact does DoorDash partnership have on sales across different store types and could expanding this service further enhance sales metrics?
3. With EDO stores averaging 160.18K in Chicken sales, how can we replicate this success in other store types, and what offers or promotions might contribute to similar performance?
4. Considering the variance in Pizza sales, particularly the 316.10K in 5.5K stores, what drives these differences, and how can we leverage this to optimize offers in other store types?
This project aims to create a powerful database and visualization tool for hotel booking data analysis, empowering stakeholders to gain in-depth insights into their operations.
By developing an SQL-based database and integrating it with Power BI, we enable the exploration of key metrics such as revenue growth, occupancy rates, and parking space utilization.
It explores questions like revenue trends, the necessity of parking expansion, and pattern identification in booking data, such as peak seasons and customer preferences.
SQL Query Combining Yearly Hotel Data, Joining Market Segments, and Calculating Meal Costs.
SQL Revenue Analysis Query Aggregating Yearly Hotel Data for Growth Comparison by Year and Hotel Type.
The Power BI dashboard has revealed critical insights for the hotel's performance metrics. Total revenue saw a 12% compound annual growth rate from 2018 to 2020, with a notable revenue spike of 18% in Q3 each year, aligning with peak tourism seasons.
City hotels showed a stronger performance, contributing 65% to the overall revenue, and displayed a 75% occupancy rate versus the resort hotels' 60%.
The dashboard also highlighted a steady 5% year-over-year increase in Average Daily Rate (ADR), with the highest ADR observed in December, likely due to holiday travel.
Interestingly, the analysis showed a 10% increase in guest retention rates, suggesting effective customer loyalty programs.
Parking space utilization peaked at 95% capacity during summer months, indicating a potential need for expansion.
Recommended Analysis
1. How does revenue growth correlate with seasonal trends across different hotel types?
2. What is the impact of Average Daily Rate (ADR) on the overall revenue, and how does it vary between city and resort hotels?
3. Is the current parking capacity meeting the demand, and how does this affect guest satisfaction?
4. What trends can we observe in guest retention rates, and how do these influence revenue projections for future quarters?
The electric vehicle (EV) market is undergoing rapid transformation, with a marked shift towards more sustainable transportation options. The dashboard presents a comprehensive analysis of the EV landscape, utilizing multiple Key Performance Indicators (KPIs) to assess market size, growth, and consumer trends.
By analyzing total vehicle count, average electric range, and the proportion of battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs), we gain critical insights into the current state and trajectory of EV adoption.
Additionally, the dashboard explores the distribution of EVs by model year, state, make, CAFV eligibility, and model, highlighting consumer preferences and the impact of policy incentives.
Distribution of Total Vehicle Registrations Across the United States by State
Exponential Growth in Total Vehicle Registrations by Model Year (2010-2024)
The dashboard vividly demonstrates the EV market's expansion, revealing a total of 150.42K vehicles with an average electric range of 67.83 miles.
BEVs represent a substantial 78% of the total, indicating a strong consumer shift towards fully electric models. PHEVs, making up 22%, suggest that hybrid technologies remain relevant.
The rise in EVs from 2010 to the present illustrates a steep growth trajectory, particularly noticeable after 2015. Regionally, certain states stand out as leaders in EV adoption, which may correlate with policy support and infrastructure availability.
Manufacturer and model data reflect consumer loyalty and the impact of brand recognition in the EV space. The CAFV eligibility information implies a strategic advantage for qualifying vehicles, potentially accelerating their market penetration.
Recommended Analysis
1. What is the trend in electric vehicle adoption from 2010 to the present?
2. Which states have the highest adoption of electric vehicles, and what might be contributing to this?
3. Which manufacturers are leading in electric vehicle production, and how does this reflect market competition?
4. How significant is the role of Clean Alternative Fuel Vehicle (CAFV) incentives in EV adoption?
As organizations seek to minimize employee turnover and enhance productivity, it is essential to understand the factors that contribute to employee dissatisfaction and churn.
Leveraging data from a pilot program, this study aims to identify key drivers of employee turnover using a churn model developed through advanced analytics.
The model will help determine how job satisfaction, time spent at the company, workload, and salary levels impact the likelihood of employees leaving.
Feature Importance Plot Showing Key Variables in Employee Retention
Query Results from Google Cloud BigQuery for Employee Data Analysis
The dynamic dashboard reveals several critical findings about employee turnover within the organization. The overall churn rate stands at 7%, with an average employee satisfaction level of 50.16%. Employees with longer tenure and higher satisfaction levels show a notably lower propensity to leave, underscoring the importance of job satisfaction in retention.
The dashboard indicates that the most significant churn occurs in the sales and technical departments, with dissatisfaction and excessive work hours being the primary drivers, as depicted by a bar chart.
Analyzing the stacked bar chart, it's evident that the support and IT departments have a higher prediction of employees staying, suggesting better conditions or management practices in these areas. These insights are crucial for targeted retention strategies
Recommended Analysis
1. What is Causing Employees to Leave?
2. Are Employees Satisfied?
3. What Departments Have the Most Churn?
4. What Does Failure Look Like?
5. What Trends are Important?
6. What Actions Affect the Trend?
The healthcare industry faces challenges in efficiently managing patient data and financial performance across different categories and hospitals. This project aims to analyze revenue trends, profit margins, and priority records to provide insights into hospital operations and patient care efficiency.
By examining patient records, admit dates, and hospital performance metrics, this project seeks to identify key areas for improvement and potential strategies to enhance profitability and service quality in the healthcare sector.
Visualizing profit and discount trends across hospital categories and admission dates from 2017-2018.
Mapping patient distribution across various regions and states for hospital performance analysis.
The dashboard reveals a 7.64% decrease in profit despite an 8.26% increase in revenue and a 3.01% rise in discounts. The total revenue is $18,633,364.29. Surgical and Medical categories show the highest revenues at $1.29M and $1.12M respectively. However, Labor & Delivery in 2018 reported a loss of $8,564.59.
Patient records are distributed by priority, with 8.39K records categorized across hospitals. North Hospital accounts for the highest revenue share. Key insights include revenue distribution by admit date and profit and discount analysis by category and year.
Recommended Analysis
1. What is the overall trend in revenue from Q1 2015 to Q4 2018?
A: The overall trend in revenue shows fluctuations with a general increase, peaking in Q1 2017, and maintaining a stable range between $0.5M and $1.5M per quarter.
2. Which hospital generated the highest revenue, and what is its share compared to others?
A: North Hospital generated the highest revenue, capturing the largest share of total hospital revenue compared to Downtown and South hospitals.
3. What category and year combination had the highest profit, and what was the amount?
A: Critical Care in 2017 had the highest profit amounting to $36,470.87.
4. Which category in 2018 reported a loss, and what was the amount of loss?
A: Labor & Delivery reported a loss of $8,564.59 in 2018
5. How does the count of records distributed by priority reflect on hospital service urgency?
A: The count of records by priority indicates a significant proportion of high and urgent priority cases, demonstrating the hospitals' need to efficiently manage urgent patient care.
6. What is the percentage change in profit, revenue, and discount from the previous period?
A: Profit decreased by 7.64%, revenue increased by 8.26%, and discounts increased by 3.01%, indicating a need to manage profitability despite increased revenue and discount offerings.
In this project, we aim to leverage detailed IPL datasets to analyze player performance, team dynamics, and match outcomes over several seasons of the Indian Premier League. The project seeks to understand the factors that contribute most significantly to winning matches, match outcomes, and identify standout players based on statistical metrics.
Using a combination of data exploration, visualization, and predictive modeling techniques, this analysis will provide insights into effective strategies and player utilization, helping teams make informed decisions to enhance their performance in the league
Top 10 Most Economical Bowlers in IPL Powerplay Overs
IPL Team Performance Based on Wins After Winning the Toss
The Notebook highlights that the team with the highest scoring innings consistently won more games, demonstrating the critical impact of high-scoring performances. The strike rate analysis shows that teams with players having a strike rate over 140 have a 60% higher chance of winning matches compared to others.
Additionally, teams that restricted their opponents to less than 160 runs in the first innings won approximately 75% of those matches, underlining the effectiveness of strong bowling strategies.
There's also a significant correlation (0.72) between top-order batsmen's average scores and the team's overall success rate, emphasizing the importance of solid starts.
Recommended Analysis
1. Which players have the most consistent performance across seasons in terms of runs scored and strike rate?
2. How does the win/loss ratio vary for teams at different home venues?
3. What is the correlation between the toss decision (batting or bowling first) and match outcomes for each team?
4. Which bowlers have the best economy rates in matches that their teams win?
5. How do batting partnerships affect the final score in matches?
6. What is the impact of player dismissals (e.g., bowled, caught) on the team's scoring rates?
7. Are there any trends in player performance based on match locations and conditions?
In the fast-paced ride-sharing market, understanding user behavior is vital for improving service and customer satisfaction. Uber, a key player, generates vast data revealing consumer preferences and patterns.
This project aims to harness this data through an analytics dashboard, addressing key questions: What are the preferred payment methods and how do they vary by region? How do trip distances relate to fares, indicating user habits and pricing strategies? We will also analyze ride types (solo, group) and the frequency of service disputes to identify areas needing customer service enhancements.
MageAI ETL Pipeline for Uber Data Export to BigQuery
Google Compute Engine Terminal Running Uber Data Engineering Project
The analysis of the Uber dataset through our dashboard has provided crucial insights into user behavior and transactional patterns. We observed a total revenue of $1.6 million from 100,000 recorded trips, with an average trip distance of 3 miles and an average fare amount of $13.3 per trip.
Most transactions were conducted via credit card, accounting for 66% of all payments. The JFK area registered the highest fare rates, indicative of its significant contribution to overall earnings.
SQL queries further highlighted operational efficiencies and areas needing improvement, particularly in regions with lower transaction volumes.
Recommended Analysis
1. What is the distribution of payment types across different geographic locations?
2. How does the average fare amount vary between different rate codes?
3. What is the correlation between trip distance and total fare amount?
4. Which areas have the highest frequency of rides and how can this information be used to optimize fleet distribution?
5. What are the common characteristics of trips that end in payment disputes?
Despite the increasing prevalence of thyroid disorders worldwide, access to thyroid care remains uneven across different regions. This disparity is evident from the distribution and frequency of thyroid-related healthcare services, which varies significantly from one area to another.
The problem lies in identifying which regions are underserved and understanding the trends in thyroid case management over time. A deeper insight into the geographic concentration of thyroid care requirements is crucial to address the gaps in service delivery effectively.
Trends in Thyroid Care Cases Over Given Time Period
Regions Receiving Most Delivery Services for Thyroid Care
The dashboard highlights that Bihar is a hotspot for thyroid care inquiries, suggesting a high demand for services in this region. It also shows a varied distribution of thyroid case records across different states, with Delhi leading followed by Haryana and Assam.
Over the analyzed period, the trend line indicates a significant fluctuation in case numbers, peaking in early July. Furthermore, data analysis reveals that a substantial portion (54.6%) of inquiries are general questions, indicating a potential lack of basic knowledge about thyroid health among the populace.
The geographic distribution suggests a concentration of thyroid care needs not only in India but also highlights significant requirements in neighboring countries.
Recommended Analysis
1. Why does Bihar have a significantly higher number of thyroid care inquiries compared to other regions?
2. What causes the fluctuations in thyroid care cases observed in the trend line over the analyzed period?
3. How effective are the current thyroid health communication strategies given the high percentage of general inquiries?
4. What can be done to improve thyroid health services in areas identified as underserved in the dataset?
5. How does the distribution of thyroid cases in neighboring countries compare to that within India, and what implications does this have for regional health policies?
The primary objective of this project is to conduct a comprehensive analysis of commercial transactions and personal financial behavior over a four-year period, from 2014 to 2017. This analysis aims to identify key trends and patterns in sales and profits across different product categories and geographical locations.
The project will explore the correlation between delivery times, sales volumes, and profitability to optimize operational efficiencies. This strategic insight is crucial for stakeholders to make informed decisions, enhance financial performance, and adapt to changing market conditions.
Sales and Profits Trends Over Time for Various Products
Spending Category Distribution Showing Percentage of Total Expenditures
Technology products lead sales at 38.8%, followed closely by office supplies and furniture, suggesting strong market demand in these categories.
Despite overall stable sales, the profit graph shows variability, with categories like appliances and copiers exhibiting lower margins, indicating potential issues in pricing or cost efficiency.
The geographic sales distribution emphasizes a heavy concentration in certain states, pinpointing regional market strengths and areas for expansion.
A consistent delivery time of approximately 5.4 days correlates with maintaining customer satisfaction, hinting at the importance of efficient logistics management.
Recommended Analysis
1. How does the profitability of technology products compare to other categories like furniture and office supplies in terms of percentage margin?
2. What are the trends in delivery times over the four years and how do they correlate with customer satisfaction and repeat purchases?
3. Which geographic regions show the most significant growth in sales, and what factors contribute to this trend?
4. What is the year-on-year growth rate for each product category and how does it affect strategic inventory decisions?
5. How do sales and profit trends compare between high-cost items like appliances and copiers versus low-cost items like office supplies?
6. What impact do seasonal variations have on sales figures across different product categories?
7. How do operational costs (reflected in cost prices) impact overall profitability for different product categories?
The Google Ads dashboard presents a comprehensive overview of ad campaign performance over a two-year period, but several challenges arise that could hinder optimal decision-making. The dashboard must clearly identify trends and anomalies in click-through rates, impressions, and conversion rates.
The high variability in cost per conversion across different campaigns suggests a need for a refined strategy to ensure budget efficiency..
Conversion Rates by Device Type: Mobile, Computer, Tablet
Cost and Average CPC Trends Over Time for Advertisements
The total clicks amounted to 483.8K with an overall click-through rate (CTR) of 1.2%, indicating moderate engagement. Impressions totaled 41.8M, demonstrating extensive reach.
The conversion rate stood at 63.4%, translating to 595.5K conversions, signifying a high effectiveness in turning viewers into action-takers.
The average cost per click (CPC) of $0.6 and a total cost of $282.86K highlight substantial investment, with the cost per conversion relatively low at $0.5, showing efficient use of the budget in generating conversions
Recommended Analysis
1. What is the trend in Click-Through Rate (CTR) over the analyzed period?
2. How do the conversions correlate with the impressions received?
3. Which campaigns have the highest Cost Per Conversion, and what might be driving those costs?
4. How does device usage impact campaign performance in terms of clicks and conversions?
5. What are the implications of the changes in average Cost Per Click (CPC) over time?
6. How effective are the ad expenditures in relation to the overall conversions achieved?
Hotel management seeks to enhance revenue and optimize customer satisfaction by understanding booking trends and customer behavior. The current data lacks detailed insights on seasonal booking patterns, customer preferences, and the impact of cancellations on overall revenue.
By analyzing historical booking data, this project aims to identify key factors influencing booking trends and customer behavior. The insights will help the management develop targeted strategies for increasing occupancy rates, reducing cancellations, and maximizing revenue throughout the year.
Hotel Bookings by Parking Space Requirements: City vs. Resort Hotels
Monthly Booking Trends by Customer Type: September to August
The analysis of hotel booking data from 2015-2017 revealed that City Hotels had higher bookings compared to Resort Hotels, with peak bookings in August and December. Transient customers accounted for the majority of bookings. Most bookings came from online travel agents, while the highest revenue per booking was observed in City Hotels.
Cancellations were more frequent in Resort Hotels. Customers requiring parking spaces were primarily associated with City Hotels. The average daily rate (ADR) varied significantly by month, with City Hotels consistently maintaining higher ADR than Resort Hotels.
Recommended Analysis
1. Which type of hotel had the highest number of bookings?
A: City Hotels had the highest number of bookings, significantly surpassing Resort Hotels, especially in August and December.
2. What is the primary customer type for hotel bookings?
A: The primary customer type for hotel bookings is Transient customers, accounting for the majority of bookings.
3. Which market segment contributes the most to hotel bookings?
A: The Online Travel Agent (OTA) segment contributes the most to hotel bookings, indicating a strong reliance on online platforms.
4. How do cancellation rates compare between City Hotels and Resort Hotels?
A: Cancellation rates are higher in Resort Hotels compared to City Hotels, suggesting a need for improved booking policies or customer engagement strategies for Resort Hotels.
5.What is the trend in average daily rate (ADR) across different months?
A: The ADR varies significantly by month, with City Hotels consistently achieving higher ADRs than Resort Hotels. Peak ADR months include December and August.
6. How does the requirement for car parking spaces correlate with hotel bookings?
A: Customers requiring car parking spaces are primarily associated with City Hotels, highlighting the importance of parking facilities in urban locations.
Toman Bike Share seeks the creation of a dashboard to visualize key performance metrics. This includes hourly revenue analysis, profit and revenue trends, seasonal revenue, and rider demographics to facilitate informed decision-making.
A preliminary version of the dashboard is required as soon as possible. Additionally, an estimated timeline for completion and recommendations regarding potential price adjustments for the next year are requested.
SQL query combining bike share data to calculate revenue and profit.
A bar and line chart comparing riders, average profit, and average revenue by month.
The dashboard reveals a total revenue of $15.19 million and a profit of $10.45 million. The profit margin is 0.45, indicating a healthy profitability rate for Toman Bike Share.
In 2021, there were 1.24 million riders contributing to a revenue of $4.96 million and a profit of $3.42 million. In 2022, the number of riders increased to 2.05 million, with revenue rising to $10.23 million and profit to $7.03 million.
Revenue distribution by season shows the highest earnings in the third season at $4.9 million, followed by the second season at $4.2 million, the fourth season at $3.9 million, and the first season at $2.2 million, highlighting significant seasonal variations in revenue.
Implement the new prices but be ready to adjust based on immediate customer feedback and sales data. Monitoring closely will allow you to fine-tune your pricing strategy without committing fully to a price that might turn out to be too high.
If the price in 2022 was $4.99, a 10% increase would make the new price about $5.49.
A 15% increase would set the price at approximately $5.74.
Recommended Analysis
1) What are the peak revenue hours for Toman Bike Share?
2) How does revenue and profit trend over the months?
3) What is the seasonal impact on revenue?
4) What is the distribution of riders by rider types?
5) What is the year-over-year growth in revenue and profit?
