In the measured time period, September 2025 saw the highest figures for online traffic to the C2C fashion marketplace depop.com. According to the data, desktop and mobile visits to depop.com reached **** million visits that month.

The dataset contains information about web requests to a single website. It's a time series dataset, which means it tracks data over time, making it great for machine learning analysis.

In the measured time period, November 2024 saw the highest figures for online traffic to the C2C fashion marketplace vestiairecollective.com. According to the data, desktop and mobile visits to vestiairecollective.com reached **** million visits that month.

In the measured time period, July 2025 saw the highest figures for the online classifieds site craigslist.org. According to the data, desktop and mobile visits to depop.com reached ***** million visits that month.

Daily utilization metrics for data.lacity.org and geohub.lacity.org. Updated monthly

Between December 2022 and January 2024, ******** was the online learning platform reporting the highest traffic, with a peak of *** million visits to its websites in December 2023. ******** ranked second, with the platform reaching a peak of ** million visits in the examined period. The website ******* (which stands for technology, entertainment, design) saw a peak of over ** million visits in March 2023.

In the measured time period, June 2025 saw the highest figures for online traffic to the fashion retail website hm.com. According to the data, desktop and mobile visits to hm.com reached over ** million visits that month.

This dataset originates from DataCamp. Many users have reposted copies of the CSV on Kaggle, but most of those uploads omit the original instructions, business context, and problem framing. In this upload, I’ve included that missing context in the About Dataset so the reader of my notebook or any other notebook can fully understand how the data was intended to be used and the intended problem framing.

Note: I have also uploaded a visualization of the workflow I personally took to tackle this problem, but it is not part of the dataset itself. Additionally, I created a PowerPoint presentation based on my work in the notebook, which you can download from here:
PPTX Presentation

Recipe Site Traffic

From: Head of Data Science
Received: Today
Subject: New project from the product team

Hey!

I have a new project for you from the product team. Should be an interesting challenge. You can see the background and request in the email below.

I would like you to perform the analysis and write a short report for me. I want to be able to review your code as well as read your thought process for each step. I also want you to prepare and deliver the presentation for the product team - you are ready for the challenge!

They want us to predict which recipes will be popular 80% of the time and minimize the chance of showing unpopular recipes. I don't think that is realistic in the time we have, but do your best and present whatever you find.

You can find more details about what I expect you to do here. And information on the data here.

I will be on vacation for the next couple of weeks, but I know you can do this without my support. If you need to make any decisions, include them in your work and I will review them when I am back.

Good Luck!

From: Product Manager - Recipe Discovery
To: Head of Data Science
Received: Yesterday
Subject: Can you help us predict popular recipes?

Hi,

We haven't met before but I am responsible for choosing which recipes to display on the homepage each day. I have heard about what the data science team is capable of and I was wondering if you can help me choose which recipes we should display on the home page?

At the moment, I choose my favorite recipe from a selection and display that on the home page. We have noticed that traffic to the rest of the website goes up by as much as 40% if I pick a popular recipe. But I don't know how to decide if a recipe will be popular. More traffic means more subscriptions so this is really important to the company.

Can your team: - Predict which recipes will lead to high traffic? - Correctly predict high traffic recipes 80% of the time?

We need to make a decision on this soon, so I need you to present your results to me by the end of the month. Whatever your results, what do you recommend we do next?

Look forward to seeing your presentation.

About Tasty Bytes

Tasty Bytes was founded in 2020 in the midst of the Covid Pandemic. The world wanted inspiration so we decided to provide it. We started life as a search engine for recipes, helping people to find ways to use up the limited supplies they had at home.

Now, over two years on, we are a fully fledged business. For a monthly subscription we will put together a full meal plan to ensure you and your family are getting a healthy, balanced diet whatever your budget. Subscribe to our premium plan and we will also deliver the ingredients to your door.

Example Recipe

This is an example of how a recipe may appear on the website, we haven't included all of the steps but you should get an idea of what visitors to the site see.

Tomato Soup

Servings: 4
Time to make: 2 hours
Category: Lunch/Snack
Cost per serving: $

Nutritional Information (per serving) - Calories 123 - Carbohydrate 13g - Sugar 1g - Protein 4g

Ingredients: - Tomatoes - Onion - Carrot - Vegetable Stock

Method: 1. Cut the tomatoes into quarters….

Data Information

The product manager has tried to make this easier for us and provided data for each recipe, as well as whether there was high traffic when the recipe was featured on the home page.

As you will see, they haven't given us all of the information they have about each recipe.

You can find the data here.

I will let you decide how to process it, just make sure you include all your decisions in your report.

Don't forget to double check the data really does match what they say - it might not.

The Traffic Volume Trends montly report is a natinal data report that provides quality controlled vehicle miles traveled data for each State for all roadways

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In the measured time period, May 2025 saw the highest figures for online traffic to the D2C sportswear retailer gymshark.com. According to the data, desktop and mobile visits to gymshark.com reached **** million visits that month.

We provide mobility data across the web with 30+ enriched attributes, including demographics, devices, web activity, intent and mobility insights. We help marketers, agencies, and platforms build precise mobility audience segments, optimize mobility targeting, attribute locations, and understand cross-device journeys. Our continuously updated mobility datasets deliver real-time mobility insights that power smarter mobility-based campaigns and future-ready strategies.

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With AdPreference, expect the following key benefits through our partnership: - Augment Mobility Data Attributes - Enrich CRM - Personalize Mobility Audiences - Fraud Prevention - Mobility Audience Curation

Access the largest and most customizable mobility data segments with AdPreference today. Supercharge your needs with unique and enriched mobility data not found anywhere else.

For more information, please visit https://www.adpreference.co/

The Traffic Volume Trends montly report is a natinal data report that provides quality controlled vehicle miles traveled data for each State for all roadways

The Traffic Volume Trends montly report is a natinal data report that provides quality controlled vehicle miles traveled data for each State for all roadways

The Traffic Volume Trends montly report is a natinal data report that provides quality controlled vehicle miles traveled data for each State for all roadways

The Traffic Volume Trends montly report is a natinal data report that provides quality controlled vehicle miles traveled data for each State for all roadways

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