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.

A dataset comparing features, pricing, and ratings of the top sites to buy website traffic in 2025: Google Ads, Facebook Ads, PropellerAds, and SparkTraffic.

Code:

Packet_Features_Generator.py & Features.py

To run this code:

pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j

-h, --help show this help message and exit -i TXTFILE input text file -x X Add first X number of total packets as features. -y Y Add first Y number of negative packets as features. -z Z Add first Z number of positive packets as features. -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,... -s S Generate samples using size s. -j

Purpose:

Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.

Uses Features.py to calcualte the features.

startMachineLearning.sh & machineLearning.py

To run this code:

bash startMachineLearning.sh

This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags

Options (to be edited within this file):

--evaluate-only to test 5 fold cross validation accuracy

--test-scaling-normalization to test 6 different combinations of scalers and normalizers

Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use

--grid-search to test the best grid search hyperparameters - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:' - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'

Purpose:

Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.

Data

Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.

Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:

First number is a classification number to denote what website, query, or vr action is taking place.

The remaining numbers in each line denote:

The size of a packet,

and the direction it is traveling.

negative numbers denote incoming packets

positive numbers denote outgoing packets

Figure 4 Data

This data uses specific lines from the Virtual Reality.txt file.

The action 'LongText Search' refers to a user searching for "Saint Basils Cathedral" with text in the Wander app.

The action 'ShortText Search' refers to a user searching for "Mexico" with text in the Wander app.

The .xlsx and .csv file are identical

Each file includes (from right to left):

The origional packet data,

each line of data organized from smallest to largest packet size in order to calculate the mean and standard deviation of each packet capture,

and the final Cumulative Distrubution Function (CDF) caluclation that generated the Figure 4 Graph.

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.

packs.best is ranked #2460 in MX with 942.46K Traffic. Categories: . Learn more about website traffic, market share, and more!

jaya9.best is ranked #1192 in BD with 182.3K Traffic. Categories: . Learn more about website traffic, market share, and more!

The Japanese review site my-best.com had the highest bounce rate among the most visited retail websites in Japan in July 2024. Operated by mybest, Inc. and part of LY Corporation, the website had a bounce of nearly ** percent, while ranking as the ****** most visited retail website in the same month.

Traffic analytics, rankings, and competitive metrics for my-best.com as of September 2025

This dataset contains network traffic logs captured by Burp-Suite, aimed at classifying web requests as either good or bad based on their characteristics. The dataset is designed for the task of predicting whether incoming requests are legitimate (good) or malicious (bad), aiding in the detection and prevention of web-based attacks.

List of bad words to check in the URL path

badwords = ['sleep', 'uid', 'select', 'waitfor', 'delay', 'system', 'union', 'order by', 'group by', 'admin', 'drop', 'script']

vegamoviese.best is ranked #424233 in IN with 3.47K Traffic. Categories: . Learn more about website traffic, market share, and more!

In 2024, most of the global website traffic was still generated by humans, but bot traffic is constantly growing. Fraudulent traffic through bad bot actors accounted for 37 percent of global web traffic in the most recently measured period, representing an increase of 12 percent from the previous year. Sophistication of Bad Bots on the rise The complexity of malicious bot activity has dramatically increased in recent years. Advanced bad bots have doubled in prevalence over the past 2 years, indicating a surge in the sophistication of cyber threats. Simultaneously, the share of simple bad bots drastically increased over the last years, suggesting a shift in the landscape of automated threats. Meanwhile, areas like food and groceries, sports, gambling, and entertainment faced the highest amount of advanced bad bots, with more than 70 percent of their bot traffic affected by evasive applications. Good and bad bots across industries The impact of bot traffic varies across different sectors. Bad bots accounted for over 50 percent of the telecom and ISPs, community and society, and computing and IT segments web traffic. However, not all bot traffic is considered bad. Some of these applications help index websites for search engines or monitor website performance, assisting users throughout their online search. Therefore, areas like entertainment, food and groceries, and even areas targeted by bad bots themselves experienced notable levels of good bot traffic, demonstrating the diverse applications of benign automated systems across different sectors.

best-smarter.me is ranked #948008 in US with 5.08K Traffic. Categories: . Learn more about website traffic, market share, and more!

Traffic analytics, rankings, and competitive metrics for best-hashtags.com as of October 2025

gmtcloud.best is ranked #0 in GR with 2.63M Traffic. Categories: . Learn more about website traffic, market share, and more!

egi.best is ranked #17857 in SA with 32.11K Traffic. Categories: . Learn more about website traffic, market share, and more!

yabalovo.best is ranked #2054 in RU with 918.64K Traffic. Categories: . Learn more about website traffic, market share, and more!

Traffic analytics, rankings, and competitive metrics for good-win-racing.com as of October 2025

After the outbreak of the coronavirus (COVID-19) online general retailers and grocery shopping had the most positive development as compared to the luxury products category or online cosmetics industry, also analyzed by the source. In general, the ***** big winners of the crisis in terms of traffic, conversion rate and time spent per session were the online grocery sector, high-tech products and the home furnishing sector.

More information about

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