Project Description: 

The "McDonald's" Power BI project involved analyzing the nutritional information of every product on the McDonald's menu. The goal of the project was to provide a detailed report on the proteins, calories, energy, sugar levels, and other nutritional factors of each menu item.

Data Source: 

The project utilized a dataset containing information about each product on the McDonald's menu, including its nutritional information.

Tools Used: 

Power BI was used to create interactive visualizations and reports based on the data.

Report Summary:

This report details the nutritional information of every product on the McDonald's menu. The report included detailed visualizations that allowed for easy analysis and comparison of the nutritional values of different menu items.

Key Findings from the report included:

• Some menu items contained significantly higher levels of calories, sugar, and other nutritional factors than others.

• Certain types of menu items, such as desserts and drinks, tended to have higher levels of sugar and calories than other types of items.

• The report highlighted some menu items that were lower in calories and sugar, making them potentially healthier options for customers.

Conclusion:

• Overall, this report provided valuable insights into the nutritional value of the McDonald's menu. 

• The interactive visualizations and detailed analysis made it easy for stakeholders to understand and act upon the findings. 

• The comprehensive report offers valuable insights into the nutritional information of each product on the menu. 

• By highlighting the nutritional values of different items, businesses can offer customers healthier options that align with their nutritional requirements. 

• This will not only improve the overall health of customers but also increase sales as customers are more likely to purchase items that meet their nutritional needs and preferences. 

• Therefore, this report serves as a powerful tool for businesses to make data-driven decisions that can drive growth and success.

Project Description: 

The "Pizza Details" project involved analyzing and visualizing data on pizza prices from individual restaurants, considering variations in size, cheese, mushroom, and spice. The goal of the project was to provide insights to businesses in the food industry and help them optimize their pricing strategies.

Data Source: 

The data used in this project was sourced from various pizza restaurants and compiled into a single dataset. The dataset included information on pizza sizes, toppings, and prices.

Tools Used: 

Google Data Studio was the primary tool used for this project, enabling efficient data visualization and analysis.

Report Summary: 

This report presented insights on pizza pricing trends and variations among different pizza restaurants. It included visualizations such as scatterplots, heat maps, and line charts to illustrate the relationships between pizza prices and different factors such as size, cheese, mushroom, and spice.

Key Findings from the report included:

• Larger pizzas tend to have a higher price than smaller ones, but the price difference varies greatly among restaurants.

• Adding cheese and mushrooms generally increases the price of a pizza, while adding spice does not have a significant impact on price.

• There is a wide variation in pizza prices among different restaurants, indicating potential opportunities for businesses to optimize their pricing strategies.

Conclusion: 

• Overall, this report provided valuable insights to businesses in the food industry by identifying trends and variations in pizza pricing. 

• By optimizing their pricing strategies based on these insights, businesses can potentially increase their profitability and competitiveness.

Project Description: 

The "Titanic" project involved analyzing and visualizing data on the passengers who were on the ship during its tragic sinking in 1912. The goal of the project was to provide insights into the factors that contributed to survival or death among the passengers.

Data Source: 

The data used in this project was sourced from various historical records and compiled into a single dataset. The dataset included information on the passengers' names, ages, genders, ticket numbers and survival status.

Tools Used: 

Power BI was the primary tool used for this project, enabling efficient data visualization and analysis.

Report Summary: 

This report presented insights on the factors that contributed to survival or death among the Titanic passengers. It included visualizations such as pie charts, bar charts, and histograms to illustrate the relationships between survival status and different factors such as age, gender, and ticket class.

Key Findings from the report included:

• Women and children had a higher likelihood of survival than men, likely due to the "women and children first" policy during the evacuation of the ship.

• Passengers in higher ticket classes had a higher likelihood of survival, possibly because they were given priority in the evacuation process.

• There was a higher survival rate among passengers who had a designated cabin or room, suggesting that having a designated space may have increased the chances of survival.

Conclusion: 

• Overall, this report provided valuable insights into the factors that contributed to survival or death among the passengers on the ship. 

• By understanding these factors, businesses can potentially improve their emergency response plans and enhance safety measures for their customers.

Project Description: 

The "Students Performance in Exams" project is a Power BI report that provides insights into the factors that influence a student's academic performance. The report is based on a dataset that includes scores from three exams and various personal, social, and economic factors that have interaction effects on the exam scores. The report aims to help educationists and policy makers to understand the various factors that impact students' academic performance.

Data Source: 

The data used for this project is obtained from Kaggle, a data science and machine learning community. The dataset consists of 1,000 records of students' exam scores and various factors that have an impact on their performance.

Tools Used: 

This report was created using Microsoft Power BI, a business analytics service that provides interactive visualizations and business intelligence capabilities. The dataset was imported into Power BI and transformed using the Query Editor. Visualizations were created using the Power BI desktop interface.

Report Summary: 

The "Students Performance in Exams" report is divided into multiple sections, including an overview of the dataset, a summary of the data, and detailed visualizations that highlight the relationships between various factors and exam scores. The report includes a dashboard that displays key metrics such as average scores by subject, distribution of scores, and correlation between various factors and exam scores.

Key Findings from the report included:

• Students who come from families with higher education tend to perform better in exams.

• Students who take test preparation courses tend to score higher in exams.

• There is a positive correlation between exam scores and students who have access to test preparation material.

• Students who have higher education aspirations tend to score better in exams.

• Female students tend to perform better in reading and writing exams, while male students tend to perform better in math exams.

Conclusion: 

• This report can help educational institutions and policy makers to better understand the factors that impact students' academic performance. 

• The report can be used to identify areas where improvements can be made to support students who are struggling in certain subjects. 

• By providing a comprehensive analysis of the dataset, this report can also help educators to develop strategies that will improve students' academic performance.

Project Description: 

The "Retail Supermarket Sales" project involves analyzing the sales details of different stores of a supermarket chain that has multiple stores in different parts of the USA. The project aims to provide insights into the sales performance of the stores and identify factors that may be contributing to the success or failure of the stores.

Data Source: 

The data for this project was obtained from the sales records of the supermarket chain. The dataset includes information on various factors such as Ship Mode, Segment, Country, City, State, Postal code, Region, Category, Sub-category, Sales etc.

Tools Used: 

This project was created using Google Data Studio, a powerful data visualization and analysis tool that allows for easy creation of interactive reports and dashboards.

Report Summary: 

This report provides a detailed analysis of the sales data for the different stores of the supermarket chain. The report includes various charts and graphs that help to visualize the sales performance of the stores. The report also includes information on the top-selling products, the most profitable stores, and the sales trends over time.

Key Findings from the report included:

• Most of the sales come from the East and West regions.

• The office supplies category is the most profitable category.

• The top-selling product is paper.

• The most profitable store is in New York City.

• Sales have been increasing over time.

Conclusion: 

• The "Retail Supermarket Sales" report provides valuable insights into the sales performance of the different stores of the supermarket chain. 

• The report can be used by the business to identify areas where they can improve their sales performance and increase their profitability. 

• For example, the business can focus on increasing sales of the most profitable products and categories or can try to replicate the success of the most profitable store in other locations. 

• The report can also be used to track the progress of the business over time and identify any changes in sales trends or customer behavior. 

• Overall, this report is a valuable tool for the business to make informed decisions and optimize their sales performance.

Project Description: 

The "Travel Trip" project is aimed at analyzing the travel patterns, preferences, and behaviors of different types of travelers. This Power BI report provides detailed information on various trips taken by travelers, including their destination, travel dates, duration of the trip, and other relevant factors.

Data Source: 

The data for this project was collected from various travel websites and agencies that provide information on travel bookings and reservations.

Tools Used: 

To create this report, we used Microsoft Power BI, a powerful business analytics tool that provides interactive visualizations and business intelligence capabilities with an intuitive interface.

Report Summary: 

The "Travel Trip" report analyzes the travel patterns of different types of travelers and provides insights into their preferences and behaviors. The report consists of various visualizations, such as a map that shows the most visited destinations, a bar chart that displays the number of trips taken by different age groups, a line chart that shows the trend in trip duration, and many more.

Key Findings from the report included:

• After analyzing the data, we found that the most visited destinations were beach locations and major cities, with the United States being the most popular country for travelers.

• We also discovered that the average trip duration was around 7-10 days, with most travelers being in the age group of 25-45 years.

Conclusion: 

• The "Travel Trip" report provides valuable insights for travel-related businesses, such as travel agencies and tour operators, to create tailored marketing strategies and travel packages that meet the needs and preferences of different travelers.

• With this report, businesses can target their marketing efforts towards specific age groups, destinations, and trip durations, resulting in increased customer satisfaction and improved revenue.