This dataset provides insights into user behaviour, content consumption patterns, and the overall performance metrics for a streaming service. It captures key interactions and demographic information to understand how users engage with video content.

Columns

• User_ID: A unique identifier for each individual user.
• Session_ID: A unique identifier for a user's specific viewing session.
• Device_ID: An identifier for the device used by the user.
• Video_ID: An identifier for the video content being viewed.
• Duration_Watched (minutes): The length of time (in minutes) a user spent watching a video.
• Genre: The specific genre of the video content, such as Action, Comedy, or Drama.
• Country: The country where the user's interaction event took place.
• Age: The age of the user.
• Gender: The user's gender, e.g., Male or Female.
• Subscription_Status: Indicates the user's subscription level, such as Free or Premium.
• Ratings: The user's rating or feedback for the content, typically on a scale from 1 to 5.
• Languages: The language of the content being viewed.
• Device_Type: The type of device used by the user (e.g., Smartphone, Tablet).
• Location: The location or city where the interaction event occurred.
• Playback_Quality: The quality of video playback, such as HD, SD, or 4K.
• Interaction_Events: The count of interaction events during a user's session (e.g., clicks, likes, shares).

Distribution

The data file is typically provided in CSV format. Specific numbers for the total rows or records are not explicitly available. However, unique values for User_ID, Session_ID, Device_ID, and Video_ID are noted as 6214. The Duration_Watched ranges from 0.06 to 120.00 minutes. The dataset includes 243 unique genres, with Documentary and Thriller each making up 17% of the content. User ages range from 10.00 to 70.00. Gender distribution shows approximately 51% female and 49% male users. Subscription status is evenly split with 50% free and 50% premium users.

Usage

This dataset is ideal for analysing user engagement, optimising content recommendations, and assessing the performance of streaming services. It can be used for developing predictive models for user churn, personalising content experiences, and understanding global consumption trends.

Coverage

The dataset covers interactions globally, with detailed geographic insights available through the 'Country' and 'Location' columns. Demographic scope includes user age (ranging from 10 to 70), gender (split almost evenly), and subscription status. A specific time range for the data collection is not provided.

License

CC0

Who Can Use It

This dataset is suitable for data scientists, data analysts, researchers, and machine learning engineers. It can be utilised for building recommendation systems, conducting behavioural analytics, developing user segmentation models, and performing general data science and analytics tasks related to streaming platforms.

Dataset Name Suggestions

• Streaming Service User Behaviour
• Video Content Consumption Data
• Global Streaming Analytics
• User Engagement Streaming Dataset
• Streaming Platform Performance Data

Attributes

Original Data Source: video streaming application

Reddit is a social news, content rating and discussion website. It's one of the most popular sites on the internet. Reddit has 52 million daily active users and approximately 430 million users who use it once a month. Reddit has different subreddits and here We'll use the r/AskScience Subreddit.

The dataset is extracted from the subreddit /r/AskScience from Reddit. The data was collected between 01-01-2016 and 20-05-2022. It contains 612,668 Datapoints and 25 Columns. The database contains a number of information about the questions asked on the subreddit, the description of the submission, the flair of the question, NSFW or SFW status, the year of the submission, and more. The data is extracted using python and Pushshift's API. A little bit of cleaning is done using NumPy and pandas as well. (see the descriptions of individual columns below).

The dataset contains the following columns and descriptions: author - Redditor Name author_fullname - Redditor Full name contest_mode - Contest mode [implement obscured scores and randomized sorting]. created_utc - Time the submission was created, represented in Unix Time. domain - Domain of submission. edited - If the post is edited or not. full_link - Link of the post on the subreddit. id - ID of the submission. is_self - Whether or not the submission is a self post (text-only). link_flair_css_class - CSS Class used to identify the flair. link_flair_text - Flair on the post or The link flair’s text content. locked - Whether or not the submission has been locked. num_comments - The number of comments on the submission. over_18 - Whether or not the submission has been marked as NSFW. permalink - A permalink for the submission. retrieved_on - time ingested. score - The number of upvotes for the submission. description - Description of the Submission. spoiler - Whether or not the submission has been marked as a spoiler. stickied - Whether or not the submission is stickied. thumbnail - Thumbnail of Submission. question - Question Asked in the Submission. url - The URL the submission links to, or the permalink if a self post. year - Year of the Submission. banned - Banned by the moderator or not.

This dataset can be used for Flair Prediction, NSFW Classification, and different Text Mining/NLP tasks. Exploratory Data Analysis can also be done to get the insights and see the trend and patterns over the years.

This project focuses on data mapping, integration, and analysis to support the development and enhancement of six UNCDF operational applications: OrgTraveler, Comms Central, Internal Support Hub, Partnership 360, SmartHR, and TimeTrack. These apps streamline workflows for travel claims, internal support, partnership management, and time tracking within UNCDF.Key Features and Tools:Data Mapping for Salesforce CRM Migration: Structured and mapped data flows to ensure compatibility and seamless migration to Salesforce CRM.Python for Data Cleaning and Transformation: Utilized pandas, numpy, and APIs to clean, preprocess, and transform raw datasets into standardized formats.Power BI Dashboards: Designed interactive dashboards to visualize workflows and monitor performance metrics for decision-making.Collaboration Across Platforms: Integrated Google Collab for code collaboration and Microsoft Excel for data validation and analysis.

** Feature Engineering for Churn Prediction**

🚀**# BCG Data Science Job Simulation | Forage** This notebook focuses on feature engineering techniques to enhance a dataset for churn prediction modeling. As part of the BCG Data Science Job Simulation, I transformed raw customer data into valuable features to improve predictive performance.

📊 What’s Inside? ✅ Data Cleaning: Removing irrelevant columns to reduce noise ✅ Date-Based Feature Extraction: Converting raw dates into useful insights like activation year, contract length, and renewal month ✅ New Predictive Features:

consumption_trend → Measures if a customer’s last-month usage is increasing or decreasing total_gas_and_elec → Aggregates total energy consumption ✅ Final Processed Dataset: Ready for churn prediction modeling

📂Dataset Used: 📌 clean_data_after_eda.csv → Original dataset after Exploratory Data Analysis (EDA) 📌 clean_data_with_new_features.csv → Final dataset after feature engineering

🛠 Technologies Used: 🔹 Python (Pandas, NumPy) 🔹 Data Preprocessing & Feature Engineering

🌟 Why Feature Engineering? Feature engineering is one of the most critical steps in machine learning. Well-engineered features improve model accuracy and uncover deeper insights into customer behavior.

🚀 This notebook is a great reference for anyone learning data preprocessing, feature selection, and predictive modeling in Data Science!

📩 Connect with Me: 🔗 GitHub Repo: https://github.com/Pavitr-Swain/BCG-Data-Science-Job-Simulation 💼 LinkedIn: https://www.linkedin.com/in/pavitr-kumar-swain-ab708b227/

🔍 Let’s explore churn prediction insights together! 🎯

This dataset contains GPS tracking data and performance metrics for motorcycle taxis (boda bodas) in Nairobi, Kenya, comparing traditional internal combustion engine (ICE) motorcycles with electric motorcycles. The study was conducted in two phases:Baseline Phase: 118 ICE motorcycles tracked over 14 days (2023-11-13 to 2023-11-26)Transition Phase: 108 ICE motorcycles (control) and 9 electric motorcycles (treatment) tracked over 12 days (2023-12-10 to 2023-12-21)The dataset is organised into two main categories:Trip Data: Individual trip-level records containing timing, distance, duration, location, and speed metricsDaily Data: Daily aggregated summaries containing usage metrics, economic data, and energy consumptionThis dataset enables comparative analysis of electric vs. ICE motorcycle performance, economic modelling of transportation costs, environmental impact assessment, urban mobility pattern analysis, and energy efficiency studies in emerging markets.Institutions:EED AdvisoryClean Air TaskforceStellenbosch UniversitySteps to reproduce:Raw Data CollectionGPS tracking devices installed on motorcycles, collecting location data at 10-second intervalsRider-reported information on revenue, maintenance costs, and fuel/electricity usageProcessing StepsGPS data cleaning: Filtered invalid coordinates, removed duplicates, interpolated missing pointsTrip identification: Defined by >1 minute stationary periods or ignition cyclesTrip metrics calculation: Distance, duration, idle time, average/max speedsDaily data aggregation: Summed by user_id and date with self-reported economic dataValidation: Cross-checked with rider logs and known routesAnonymisation: Removed start and end coordinates for first and last trips of each day to protect rider privacy and home locationsTechnical InformationGeographic coverage: Nairobi, KenyaTime period: November-December 2023Time zone: UTC+3 (East Africa Time)Currency: Kenyan Shillings (KES)Data format: CSV filesSoftware used: Python 3.8 (pandas, numpy, geopy)Notes: Some location data points are intentionally missing to protect rider privacy. Self-reported economic and energy consumption data has some missing values where riders did not report.CategoriesMotorcycle, Transportation in Africa, Electric Vehicles

ObjectiveThe primary objective of this study was to analyze CpG dinucleotide dynamics in coronaviruses by comparing Wuhan-Hu-1 with its closest and most distant relatives. Heatmaps were generated to visualize CpG counts and O/E ratios across intergenic regions, providing a clear depiction of conserved and divergent CpG patterns.Methods1. Data CollectionSource : The dataset includes CpG counts and O/E ratios for various coronaviruses, extracted from publicly available genomic sequences.Format : Data was compiled into a CSV file containing columns for intergenic regions, CpG counts, and O/E ratios for each virus.2. PreprocessingData Cleaning :Missing values (NaN), infinite values (inf, -inf), and blank entries were handled using Python's pandas library.Missing values were replaced with column means, and infinite values were capped at a large finite value (1e9).Reshaping :The data was reshaped into matrices for CpG counts and O/E ratios using meltpandas[] and pivot[] functions.3. Distance CalculationEuclidean Distance :Pairwise Euclidean distances were calculated between Wuhan-Hu-1 and other viruses using the scipy.spatial.distance.euclidean function.Distances were computed separately for CpG counts and O/E ratios, and the total distance was derived as the sum of both metrics.4. Identification of Closest and Distant RelativesThe virus with the smallest total distance was identified as the closest relative .The virus with the largest total distance was identified as the most distant relative .5. Heatmap GenerationTools :Heatmaps were generated using Python's seaborn library (sns.heatmap) and matplotlib for visualization.Parameters :Heatmaps were annotated with numerical values for clarity.A color gradient (coolwarm) was used to represent varying CpG counts and O/E ratios.Titles and axis labels were added to describe the comparison between Wuhan-Hu-1 and its relatives.ResultsClosest Relative :The closest relative to Wuhan-Hu-1 was identified based on the smallest Euclidean distance.Heatmaps for CpG counts and O/E ratios show high similarity in specific intergenic regions.Most Distant Relative :The most distant relative was identified based on the largest Euclidean distance.Heatmaps reveal significant differences in CpG dynamics compared to Wuhan-Hu-1 .Tools and LibrariesThe following tools and libraries were used in this analysis:Programming Language :Python 3.13Libraries :pandas: For data manipulation and cleaning.numpy: For numerical operations and handling missing/infinite values.scipy.spatial.distance: For calculating Euclidean distances.seaborn: For generating heatmaps.matplotlib: For additional visualization enhancements.File Formats :Input: CSV files containing CpG counts and O/E ratios.Output: PNG images of heatmaps.Files IncludedCSV File :Contains the raw data of CpG counts and O/E ratios for all viruses.Heatmap Images :Heatmaps for CpG counts and O/E ratios comparing Wuhan-Hu-1 with its closest and most distant relatives.Python Script :Full Python code used for data processing, distance calculation, and heatmap generation.Usage NotesResearchers can use this dataset to further explore the evolutionary dynamics of CpG dinucleotides in coronaviruses.The Python script can be adapted to analyze other viral genomes or datasets.Heatmaps provide a visual summary of CpG dynamics, aiding in hypothesis generation and experimental design.AcknowledgmentsSpecial thanks to the open-source community for developing tools like pandas, numpy, seaborn, and matplotlib.This work was conducted as part of an independent research project in molecular biology and bioinformatics.LicenseThis dataset is shared under the CC BY 4.0 License , allowing others to share and adapt the material as long as proper attribution is given.DOI: 10.6084/m9.figshare.28736501