In an era where convenience reigns supreme, online food ordering has emerged as a ubiquitous solution, offering the ease of dining from the comfort of one's home or office. While its convenience is undeniable, the realm of online food ordering presents a dynamic landscape of opportunities and challenges. Amidst this backdrop, I embarked on a project to delve deep into the world of online food reviews. With a keen eye for insights and a passion for data-driven decision-making, I crafted an interactive dashboard that serves as a window into the intricacies of customer behavior and preferences. This dashboard is more than just a collection of charts and graphs; it's a gateway to understanding the nuances of customer demographics, their culinary inclinations, and the factors that influence their dining choices. From dissecting income distributions by marital status and age group to unraveling gender dynamics across various occupations, every visualization tells a story. Through meticulous data analysis and visualization techniques, I've unveiled trends, patterns, and correlations that empower stakeholders to make informed decisions. Whether it's optimizing marketing strategies, refining product offerings, or enhancing customer satisfaction, this dashboard equips businesses with the insights they need to thrive in the competitive landscape of online food ordering.

Objective:

Analyzing and visualizing demographic and behavioral insights from online food reviews data.

Data Source:

Online food dataset from the Kaggle , containing information about customers' demographics, preferences, and feedback. Dataset was in the ".csv" file format and can easily be load and transform in the Power Query editor of Power BI.

Key Metrics and Visualizations:

• Sum of Monthly Income by Marital Status and Age Group
• Count of Gender by Marital Status and Gender
• Count of Educational Qualifications by Occupation
• Gender by their Occupation
• Reviews by Age Group
• Potential customers Food Status.

This figure shows the visualizations of some key metrices as mentioned above.

ETL(Extract, Transform & Load) of the dataset:

The given dataset was almost cleared and well documented but still there were various changes which should have to be embraces and for that purpose I used Power Query for the data engineering of the given dataset. There were many columns which have no contribution in the insight generation so to make data more readable and accurate , the extras columns were removed , values were replaced and the data types were changed in the following orders:

• An unnamed column with just one value "yes" was removed since it have no heading and it have nothing to contribute in insight generation
• the values of the field/column "Monthly Income" were replaced from "Below Rs.10000" to "Rs.9500," Rs10001 to Rs.25000" to "Rs.18500, " from "Rs.25001 to Rs.50000" into "Rs.37950", and lastly "More than Rs.50000" to "Rs.70000".
• Then the values of the field "Output" is replaced from "Yes" to "delivered" and from "No" to "Pending"
• And in the very last step , data type of the fields "Longitude " &" Latitude" from Fixed Decimal point to Decimal Number and of "Monthly Income" changed from Number to Currency.Key Filters/Slicers:

Key Slicers used in my dashboard are Age, Gender and Educational Qualifications on some of the visuals.The above figure shows the slicers have been used in the dashboard and they are converted from Vertical List to Dropdown. Now looking upon their affects on the visualizations, we can easily generate valuable insights or information from the resulting changings like,The above figure shows that if we apply the filters like Age to 24 , Gender to female and Educational Qualifications to Graduate then we can clearly see that how it make changes into the chart groups of "Count of Educational Qualifications by Occupation", showing that around 33.33% of the females are Employee, 33.33% are Student, and again 33.33% are Self-employed. Adding to it , we can see the changes made in "Gender by their Occupation" chart shows that we have an equal distribution of number of females as of 2, as Employee, Self- Employed and Students respectively. The other chart of " Sum of Monthly Income by Marital Status and Age Group "that the females ranges in the Age Group from 20 to 25 have Marital Status of Married and Single with Sum of Monthly Income of 89K and 85K respectively.