The Super Bowl is the highlight of the NFL season, watched by millions in the United States and many more across the world. During a 2025 survey in the United States, around 78 percent of respondents stated that they planned to watch Super Bowl LIX between the Kansas City Chiefs and the San Francisco 49ers.

The Super Bowl is one of the highlights of the sporting calendar, but many viewers tune in for more than just the game itself. During a January 2025 survey in the United States, almost 35 percent of respondents stated that the famous Super Bowl commercials were one of the parts of the event they were looking forward to.

Super Bowl Ads (Year-by-Year)

Analyzing Funny, Patriotic, and Product Promotion Ads Year By Year

By data.world's Admin [source]

About this dataset

This dataset contains a comprehensive collection of Super Bowl Ads broadcasted. Our data comes from superbowl-ads.com, providing us with the URL's to watch each ad on YouTube. We have included seven defining characteristics of these advertisements - including funniness, patriotism, celebrity presence, animals featured, and use of sex to sell the product - that will offer unique insights into the cultural trends present in each year's advertising campaigns. Furthermore, this dataset implores us to ask questions about the relationship between popular culture and the kinds of ads companies have used in order to both promote their products as well as better relate with their audience through utilizing images and themes which reflect current society. With so much data available in an easily accessible format than ever before thanks to modern technology; exploring this content could give way to unprecedented opportunities for marketers who want gain an advantage in understanding their target demographic or can provide a fresh perspective for those looking consume something new

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How to use the dataset

There are a few different ways you can use this data to uncover America’s secrets through Super Bowl ads. Let’s explore some potential uses!

• Analyze changes in the types of themes across years: By looking at the data for each year separately and trying to identify trends or similarities across years in particular themes (like funny ads or dangerous ad), you can gain an understanding of any changes in how Americans view these aspects of their entertainment. For example, is there a trend towards more funny ads? Or more patriotic ones?

• Utilize Brand Analysis: pull up all of an individual brand’s data from all years and ask what types of messages this brand has been sending throughout its Super Bowl advertising over time– Do they like animals? Are their famous people in most ads? An understanding what type brands put out will allow insight into how Americans perceive them overall.

• Analyze correlations between themes: Find correlations between different aspects by performing analyses that compare two columns at a time over multiple years; some examples may include correlation between using sex vs using animals in advertising or correlation between having a celebrity spokesperson/actor/actress vs being patriotic with ad content could also be interesting to analyze.

Research Ideas

• Creating an interactive visualization that allows users to explore the different trends surrounding Super Bowl ads over the last two decades. This could include visuals such as bar graphs, line charts and scatter plots that show how often certain characteristics are used in ads, and how these characteristics have evolved over time.

• Running a classifier model to predict which characteristics will be used in an upcoming Super Bowl ad. This could use factors such as past data from similar brands or from the same company over multiple years.

• Using the data to create a machine learning algorithm that recommends which kinds of elements (i.e funny jokes, celebrity appearances, animals ect.) should be included in a new ad based on user input about their desired outcome for the ad (i.e increase brand awareness or position brand image)

Acknowledgements

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

License

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

Columns

File: superbowl-ads.csv | Column name | Description | |:------------------------------|:--------------------------------------------------------------| | year | The year the ad was broadcasted. (Integer) | | brand | The brand associated with the ad. (String) | | superbowl_ads_dot_com_url | The URL of the ad on Superbowl-ads.com. (String) | | youtube_url | The URL of the ad on YouTube. (String) ...

The Super Bowl is the highlight of the NFL season, watched by millions in the United States and many more across the world. During an early-2024 survey in the United States, over half of respondents stated that it was likely that they would watch Super Bowl LVIII in February 2024. The share of people who plan to watch the Super Bowl was the highest among 30 to 44-year-olds.

Overview

Mountain Dew is running a $1,000,000 counting contest. Computer Vision can help you win.

:fa-spacer:

Watch our video explaining how to use this dataset.

https://i.imgur.com/ED4jpM3.png" alt="Mountain Dew">

During Super Bowl LV, Mountain Dew sponsored an ad that encourages viewers to count all unique occurrences of Mountain Dew bottles. You can watch the full ad here. The first person to tweet the exactly correct count at Mountain Dew is eligible to win $1 million (see rules here).

Counting things is a perfect place for where computer vision can help.

We uploaded the Mountain Dew video to Roboflow, created three images per each second of the commercial (91 images from ~30 seconds of commercial), and annotated all bottles we could see. This dataset is the result.

We trained a model to recognize the Mountain Dew bottles, and then ran the original commercial back through this model. This helps identify Mountain Dew bottles that the human eye may have missed when completing counts.

https://i.imgur.com/rjZCS2a.png" alt="Image example">

Getting Started

Click "Fork" in the upper right hand corner or download the raw annotations in your desired format.

Note that while the images are property of PepsiCo, we are using them here as fair-use for educational purposes and have released the annotations under a Creative Commons license.

About Roboflow

Roboflow enables teams to use computer vision. :fa-spacer: Our end-to-end platform enables developers to collect, organize, annotate, train, deploy, and improve their computer vision models -- all without needing to hire a new ML engineering team. :fa-spacer:

https://raw.githubusercontent.com/Masterx-AI/Project_Retail_Analysis_with_Walmart/main/Wallmart1.jpg" alt="">

Description:

One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inappropriate machine learning algorithm. An ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc.

Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of all, which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. Historical sales data for 45 Walmart stores located in different regions are available.

Acknowledgements

The dataset is taken from Kaggle.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t single & multiple features.
• Also evaluate the models & compare their respective scores like R2, RMSE, etc.