Dataset

This dataset was created by Kunal Khurana

Released under MIT

Contents

Description: Dive into the world of exceptional cinema with our meticulously curated dataset, "IMDb's Gems Unveiled." This dataset is a result of an extensive data collection effort based on two critical criteria: IMDb ratings exceeding 7 and a substantial number of votes, surpassing 10,000. The outcome? A treasure trove of 4070 movies meticulously selected from IMDb's vast repository.

What sets this dataset apart is its richness and diversity. With more than 20 data points meticulously gathered for each movie, this collection offers a comprehensive insight into each cinematic masterpiece. Our data collection process leveraged the power of Selenium and Pandas modules, ensuring accuracy and reliability.

Cleaning this vast dataset was a meticulous task, combining both Excel and Python for optimum precision. Analysis is powered by Pandas, Matplotlib, and NLTK, enabling to uncover hidden patterns, trends, and themes within the realm of cinema.

Note: The data is collected as of April 2023. Future versions of this analysis include Movie recommendation system Please do connect for any queries, All Love, No Hate.

Electronic health records (EHRs) have been widely adopted in recent years, but often include a high proportion of missing data, which can create difficulties in implementing machine learning and other tools of personalized medicine. Completed datasets are preferred for a number of analysis methods, and successful imputation of missing EHR data can improve interpretation and increase our power to predict health outcomes. However, use of the most popular imputation methods mainly require scripting skills, and are implemented using various packages and syntax. Thus, the implementation of a full suite of methods is generally out of reach to all except experienced data scientists. Moreover, imputation is often considered as a separate exercise from exploratory data analysis, but should be considered as art of the data exploration process. We have created a new graphical tool, ImputEHR, that is based on a Python base and allows implementation of a range of simple and sophisticated (e.g., gradient-boosted tree-based and neural network) data imputation approaches. In addition to imputation, the tool enables data exploration for informed decision-making, as well as implementing machine learning prediction tools for response data selected by the user. Although the approach works for any missing data problem, the tool is primarily motivated by problems encountered for EHR and other biomedical data. We illustrate the tool using multiple real datasets, providing performance measures of imputation and downstream predictive analysis.

This dataset has total population of dingapore basing on their ethnicity,gender . It is raw data which has mixed entities in columns . from year 1957 to 2018 population data is given . The main aim in uploading this data is to get skilled in python pandas for exploratory data analysis.

Data-Driven Analysis and Optimization of Machine Learning Systems Using MLPerf Benchmark Data

Repository Structure

• Data_Analysis.ipynb: A Jupyter Notebook containing the code for the Exploratory Data Analysis (EDA) presented in the thesis. Running this notebook reproduces the plots in the eda_plots/ directory.
• Dataset_Extension.ipynb : A Jupyter Notebook used for the data enrichment process. It takes the raw `Inference_data.csv` and produces the Inference_data_Extended.csv by adding detailed hardware specifications, cost estimates, and derived energy metrics.
• Optimization_Model.ipynb: The main Jupyter Notebook for the core contribution of this thesis. It contains the code to perform the 5-fold cross-validation, train the final predictive models, generate the Pareto-optimal recommendations, and create the final result figures.
• Inference_data.csv: The raw, unprocessed data collected from the official MLPerf Inference v4.0 results.
• Inference_data_Extended.csv: The final, enriched dataset used for all analysis and modeling. This is the output of the Dataset_Extension.ipynb notebook.
• eda_log.txt: A text log file containing summary statistics generated during the exploratory data analysis.
• requirements.txt: A list of all necessary Python libraries and their versions required to run the code in this repository.
• eda_plots/: A directory containing all plots (correlation matrices, scatter plots, box plots) generated by the EDA notebook.
• optimization_models_final/: A directory where the trained and saved final model files (.joblib) are stored after running the optimization notebook.
• pareto_validation_plot_fold_0.png: The validation plot comparing the true vs. predicted Pareto fronts, as presented in the thesis.
• shap_waterfall_final_model.png: The SHAP plot used for the model interpretability analysis, as presented in the thesis.

Requirements and Installation

Step-by-Step Reproduction Workflow

Step 1: Data Enrichment (Optional)

Step 2: Exploratory Data Analysis (Optional)

Step 3: Main Model Training, Validation, and Recommendation

1. It will perform the 5-fold group-aware cross-validation to validate the performance of the predictive models.
2. It will train the final production models on the entire dataset and save them to the optimization_models_final/ directory.
3. It will generate the final Pareto front recommendations and single-best recommendations for the Computer Vision task.
4. It will generate the final figures used in the results section, including pareto_validation_plot_fold_0.png and shap_waterfall_final_model.png.

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.

The success of scientific projects increasingly depends on using data analysis tools and data in distributed IT infrastructures. Scientists need to use appropriate data analysis tools and data, extract patterns from data using appropriate computational resources, and interpret the extracted patterns. Data analysis tools and data reside on different machines because the volume of the data often demands specific resources for their storage and processing, and data analysis tools usually require specific computational resources and run-time environments. The data analytics software framework DASF, developed at the GFZ German Research Centre for Geosciences (https://www.gfz-potsdam.de) and funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (https://www.digitalearth-hgf.de/), provides a framework for scientists to conduct data analysis in distributed environments. The data analytics software framework DASF supports scientists to conduct data analysis in distributed IT infrastructures by sharing data analysis tools and data. For this purpose, DASF defines a remote procedure call (RPC) messaging protocol that uses a central message broker instance. Scientists can augment their tools and data with this protocol to share them with others. DASF supports many programming languages and platforms since the implementation of the protocol uses WebSockets. It provides two ready-to-use language bindings for the messaging protocol, one for Python and one for the Typescript programming language. In order to share a python method or class, users add an annotation in front of it. In addition, users need to specify the connection parameters of the message broker. The central message broker approach allows the method and the client calling the method to actively establish a connection, which enables using methods deployed behind firewalls. DASF uses Apache Pulsar (https://pulsar.apache.org/) as its underlying message broker. The Typescript bindings are primarily used in conjunction with web frontend components, which are also included in the DASF-Web library. They are designed to attach directly to the data returned by the exposed RPC methods. This supports the development of highly exploratory data analysis tools. DASF also provides a progress reporting API that enables users to monitor long-running remote procedure calls. One application using the framework is the Digital Earth Flood Event Explorer (https://git.geomar.de/digital-earth/flood-event-explorer). The Digital Earth Flood Event Explorer integrates several exploratory data analysis tools and remote procedures deployed at various Helmholtz centers across Germany.

Why this Dataset

This dataset brings to you Iris Dataset in several data formats (see more details in the next sections).

You can use it to test the ingestion of data in all these formats using Python or R libraries. We also prepared Python Jupyter Notebook and R Markdown report that input all these formats:

• Test Data Formats in Python
• Test Data Formats in R

Iris Dataset

Iris Dataset was created by R. A. Fisher and donated by Michael Marshall.

Repository on UCI site: https://archive.ics.uci.edu/ml/datasets/iris

Data Source: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/

The file downloaded is iris.data and is formatted as a comma delimited file.

This small data collection was created to help you test your skills with ingesting various data formats.

Content

This file was processed to convert the data in the following formats: * csv - comma separated values format * tsv - tab separated values format * parquet - parquet format
* feather - feather format * parquet.gzip - compressed parquet format * h5 - hdf5 format * pickle - Python binary object file - pickle format * xslx - Excel format
* npy - Numpy (Python library) binary format * npz - Numpy (Python library) binary compressed format * rds - Rds (R specific data format) binary format

Acknowledgements

I would like to acknowledge the work of the creator of the dataset - R. A. Fisher and of the donor - Michael Marshall.

Inspiration

Use these data formats to test your skills in ingesting data in various formats.

The invoice dataset provided is a mock dataset generated using the Python Faker library. It has been designed to mimic the format of data collected from an online store. The dataset contains various fields, including first name, last name, email, product ID, quantity, amount, invoice date, address, city, and stock code. All of the data in the dataset is randomly generated and does not represent actual individuals or products. The dataset can be used for various purposes, including testing algorithms or models related to invoice management, e-commerce, or customer behavior analysis. The data in this dataset can be used to identify trends, patterns, or anomalies in online shopping behavior, which can help businesses to optimize their online sales strategies.

The "Digital Payments 2025 Dataset" is a synthetic dataset representing digital payment transactions across various payment applications in India for the year 2025. It captures monthly transaction data for multiple payment apps, including banks, UPI platforms, and mobile payment services, reflecting the growing adoption of digital payments in India. The dataset was created as part of a college project to simulate realistic transaction patterns for research, education, and analysis in data science, economics, and fintech studies. It includes metrics such as customer transaction counts and values, total transaction counts and values, and temporal data (month and year). The data is synthetic, generated using Python libraries to mimic real-world digital payment trends, and is suitable for academic research, teaching, and exploratory data analysis.

This dataset comprises over 20 years of geotechnical laboratory testing data collected primarily from Vienna, Lower Austria, and Burgenland. It includes 24 features documenting critical soil properties derived from particle size distributions, Atterberg limits, Proctor tests, permeability tests, and direct shear tests. Locations for a subset of samples are provided, enabling spatial analysis.

The dataset is a valuable resource for geotechnical research and education, allowing users to explore correlations among soil parameters and develop predictive models. Examples of such correlations include liquidity index with undrained shear strength, particle size distribution with friction angle, and liquid limit and plasticity index with residual friction angle.

Python-based exploratory data analysis and machine learning applications have demonstrated the dataset's potential for predictive modeling, achieving moderate accuracy for parameters such as cohesion and friction angle. Its temporal and spatial breadth, combined with repeated testing, enhances its reliability and applicability for benchmarking and validating analytical and computational geotechnical methods.

This dataset is intended for researchers, educators, and practitioners in geotechnical engineering. Potential use cases include refining empirical correlations, training machine learning models, and advancing soil mechanics understanding. Users should note that preprocessing steps, such as imputation for missing values and outlier detection, may be necessary for specific applications.

Key Features:

• Temporal Coverage: Over 20 years of data.
• Geographical Coverage: Vienna, Lower Austria, and Burgenland.
• Tests Included:
  • Particle Size Distribution
  • Atterberg Limits
  • Proctor Tests
  • Permeability Tests
  • Direct Shear Tests
• Number of Variables: 24
• Potential Applications: Correlation analysis, predictive modeling, and geotechnical design.

Technical Details:

• Missing values have been addressed using K-Nearest Neighbors (KNN) imputation, and anomalies identified using Local Outlier Factor (LOF) methods in previous studies.
• Data normalization and standardization steps are recommended for specific analyses.

Acknowledgments:
The dataset was compiled with support from the European Union's MSCA Staff Exchanges project 101182689 Geotechnical Resilience through Intelligent Design (GRID).

This is the first public release of the RICardo dataset under the licence odbl v1.0. This dataset is precisely described un der the data package format.

This release includes 368,871 bilateral or total trade flows from 1787 to 1938 for 373 reporting entities. It also contains python scripts used to compile and filter the flows to fuel our exploratory data analysis online tool.

During the first half of 2020, the COVID-19 pandemic changed the social gathering lifestyle to online business and social interaction. The worldwide imposed travel bans and national lockdown prevented social gatherings, making learning institutions and businesses to adopt an online platform for learning and business transactions. This development led to the incorporation of video conferencing into daily activities. This data article presents broadband data usage measurement collected using Glasswire software on various conference calls made between July and August. The services considered in this work are Google Meet, Zoom, Mixir, and Hangout. The data were recorded in Microsoft Excel 2016, running on a personal computer. The data was cleaned and processed using google colaboratory, which runs Python scripts on the browser. Exploratory data analysis is conducted on the data set using linear regression to model a predictive model to assess the best performance model that offers the best quality of service for online video and voice conferencing. The data is necessary to learning institutions using online programs and to learners accessing online programs in a smart city and developing countries. The data is presented in tables and graphs.

This dataset is part of the study “Representations of Sound and Music in the Middle Ages: Analysis and Visualization of the Musiconis Database”, authored by Edmundo Camacho, Xavier Fresquet, and Frédéric Billiet.

It contains structured descriptions of musical performances, performers, and instruments extracted from the Musiconis database (December 2024 version). This dataset does not include organological descriptions, which are available in a separate dataset.

The Musiconis database provides a structured and interoperable framework for studying medieval music iconography. It enables investigations into:

• The evolution and spread of musical instruments across Europe and the Mediterranean.

• Performer typologies and their representation in medieval art.

• The relationships between musical practices and social or religious contexts.

Contents:

• Musiconis Dataset (JSON format, December 2024 version):

• Musical scenes and their descriptions

• Performer metadata (roles, social status, gender, interactions)

• Instrument classifications (without detailed organological descriptions)

• Colab Notebook (Python):

• Data processing and structuring

• Visualization of performer distributions and instrument usage

• Exploratory statistics and mapping

Tools Used:

• Python (Pandas, Seaborn, Matplotlib, Plotly)

• Statistical and exploratory data analysis

• Visualization of instrument distributions, performer interactions, and musical context

This package contains data and code to replicate the findings presented in our paper titled "GUI Testing of Android Applications: Investigating the Impact of the Number of Testers on Different Exploratory Testing Strategies".

Abstract

Graphical User Interface (GUI) testing plays a pivotal role in ensuring the quality and functionality of mobile apps. In this context, Exploratory Testing (ET), a distinctive methodology in which individual testers pursue a creative, and experience-based approach to test design, is often used as an alternative or in addition to traditional scripted testing. Managing the exploratory testing process is a challenging task, that can easily result either in wasteful spending or in inadequate software quality, due to the relative unpredictability of exploratory testing activities, which depend on the skills and abilities of individual testers. A number of works have investigated the diversity of testers’ performance when using ET strategies, often in a crowdtesting setting. These works, however, investigated ET effectiveness in detecting bugs, and not in scenarios in which the goal is to generate a re-executable test suite, as well. Moreover, less work has been conducted on evaluating the impact of adopting different exploratory testing strategies. As a first step towards filling this gap in the literature, in this work we conduct an empirical evaluation involving four open-source Android apps and twenty masters students, that we believe can be representative of practitioners partaking in exploratory testing activities. The students were asked to generate test suites for the apps using a Capture and Replay tool and different exploratory testing strategies. We then compare the effectiveness, in terms of aggregate code coverage, that different-sized groups of students using different exploratory testing strategies may achieve. Results provide deeper insights into code coverage dynamics to project managers interested in using exploratory approaches to test simple Android apps, on which they can make more informed decisions.

Contents and Instructions

This package contains:

apps-under-test.zip A zip archive containing the source code of the four Android applications we considered in our study, namely MunchLife, TippyTipper, Trolly, and SimplyDo.

apps-under-test-instrumented.zip A zip archive containing the instrumented source code of the four Android applications we used to compute branch coverage.

students-test-suites.zip A zip archive containing the test suites developed by the students using Uninformed Exploratory Testing (referred to as "Black Box" in the subdirectories) and Informed Exploratory Testing (referred to as "White Box" in the subdirectories). This also includes coverage reports.

compute-coverage-unions.zip A zip archive containing Python scripts we developed to compute the aggregate LOC coverage of all possible subsets of students. The scripts have been tested on MS Windows. To compute the LOC coverage achieved by any possible subsets of testers using IET and UET strategies, run the analysisAndReport.py script. To compute the LOC coverage achieved by mixed crowds in which some testers use a U+IET approach and others use a UET approach, run the analysisAndReport_UET_IET_combinations_emma.py script.

branch-coverage-computation.zip A zip archive containing Python scripts we developed to compute the aggregate branch coverage of all considered subsets of students. The scripts have been tested on MS Windows. To compute the branch coverage achieved by any possible subsets of testers using UET and I+UET strategies, run the branch_coverage_analysis.py script. To compute the code coverage achieved by mixed crowds in which some testers use a U+IET approach and others use a UET approach, run the mixed_branch_coverage_analysis.py script.

data-analysis-scripts.zip A zip archive containing R scripts to merge and manipulate coverage data, to carry out statistical analysis and draw plots. All data concerning RQ1 and RQ2 is available as a ready-to-use R data frame in the ./data/all_coverage_data.rds file. All data concerning RQ3 is available in the ./data/all_mixed_coverage_data.rds file.

Data Preparation:

This data was scraped using python scripts from https://www.fesliyanstudios.com which provides you with royalty free musics in all trends that could be used in any of your projects or videos.

Dataset Description:

The original dataset contains 7 classes but this is just a subset of the original dataset which contains 4 classes. The audio files in the dataset were already preprocessed by converting them into smaller chunks of equal size. All music were divided into 10 seconds smaller chunks using FFMPEG.

The chunks of the same song have the equally spaced names like : 1001-1010(Same song) The four classes are: 1.) Funny, 2.) Happy, 3.) Motivation and 4.) Suspense

This project uses Python to load BlocPower's data for 121 million buildings in the US, summarize it to the spatial unit of interest (state, county or zipcode) and plot key statistics. It also compares and validates the zipcode-level statistics with other independent data sources - Microsoft (for building counts) and Goldstein et al (2022) for energy use.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F23516597%2F11309e6c4df1437ed2aa6a8fb121daa5%2FScreenshot%202025-04-10%20at%2004.17.42.png?generation=1744233480336962&alt=media" alt="">

Notebooks

1. Exploratory_Data_Analysis

https://www.kaggle.com/code/idmitri/exploratory-data-analysis

2. RUL_Prediction_Modeling

https://www.kaggle.com/code/idmitri/rul-prediction-modeling

О проекте

Силовые трансформаторы на АЭС могут эксплуатироваться дольше расчетного срока службы (25 лет), что требует усиленного мониторинга их состояния для обеспечения надежности и безопасности эксплуатации.

Для оценки состояния трансформаторов применяется хроматографический анализ растворенных газов, который позволяет выявлять дефекты по концентрациям газов в масле и прогнозировать остаточный срок службы трансформатора (RUL). Традиционные системы мониторинга ограничиваются фиксированными пороговыми значениями концентраций, снижая точность диагностики и автоматизацию. Методы машинного обучения позволяют выявлять скрытые зависимости и повышать точность прогнозирования. Подробнее: https://habr.com/ru/articles/743682/

Результаты

В данном проекте проводится глубокий анализ данных (EDA) с созданием 12 групп признаков:
- gases (концентрации газов)
- trend (трендовые компоненты)
- seasonal (сезонные компоненты)
- resid (остаточные компоненты)
- quantiles (квантили распределений)
- volatility (волатильность концентраций)
- range (размах значений)
- coefficient of variation (коэффициент вариации)
- standard deviation (стандартное отклонение)
- skewness (асимметрия распределения)
- kurtosis (эксцесс распределения)
- category (категориальные признаки неисправностей)

Использование статистических и декомпозиционных признаков позволило достичь совпадения точности силуэта распределения RUL с автоматической обработкой выбросов, что ранее требовало ручной корректировки.

Для моделирования использованы алгоритмы машинного обучения (LightGBM, CatBoost, Extra Trees) и их ансамбль. Лучшая точность достигнута моделью LightGBM с оптимизацией гиперпараметров с помощью Optuna: MAE = 61.85, RMSE = 88.21, R2 = 0.8634.

Комментарий

Код для проведения разведочного анализа данных (EDA) был разработан и протестирован локально в VSC Jupyter Notebook с использованием окружения Python 3.10.16. И на платформе Kaggle большинство графиков отображается корректно. Но некоторые сложные и комплексные визуализации (например, многомерные графики с цветовой шкалой) не адаптированы из-за ограничений среды. Несмотря на попытки оптимизировать код без существенных изменений, добиться полной совместимости не удалось. Основная проблема заключалась в конфликте версий библиотек и значительном снижении производительности — расчет занимал примерно в 10 раз больше времени по сравнению с локальной машиной MacBook M3 Pro. На Kaggle либо корректно выполнялись операции с использованием PyCaret, либо работали модели машинного обучения, но не обе части одновременно.

Предлагается гибридный вариант работы:
- Публикация и вывод метрик на Kaggle для визуализации результатов. - Локальный расчет и обучение моделей с использованием предварительно настроенного окружения Python 3.10.16. Для воспроизведения экспериментов подготовлена папка Codes с кодами VSC EDA, RUL и файлом libraries_for_modeling, содержащим список версий всех используемых библиотек.

Готов ответить в комментариях на все вопросы по настройке и запуску кода. И буду признателен за советы по предотвращению подобных проблем.

The Data Visualization Corpus

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1430847%2F29f7950c3b7daf11175aab404725542c%2FGettyImages-1187621904-600x360.jpg?generation=1601115151722854&alt=media" alt="">

Data Visualization

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions

The Data Visualizaion Copus

The Data Visualization corpus consists:

• 32 cheat sheets: This includes A-Z about the techniques and tricks that can be used for visualization, Python and R visualization cheat sheets, Types of charts, and their significance, Storytelling with data, etc..

• 32 Charts: The corpus also consists of a significant amount of data visualization charts information along with their python code, d3.js codes, and presentations relation to the respective charts explaining in a clear manner!

• Some recommended books for data visualization every data scientist's should read:

  1. Beautiful Visualization by Julie Steele and Noah Iliinsky
  2. Information Dashboard Design by Stephen Few
  3. Knowledge is beautiful by David McCandless (Short abstract)
  4. The Functional Art: An Introduction to Information Graphics and Visualization by Alberto Cairo
  5. The Visual Display of Quantitative Information by Edward R. Tufte
  6. storytelling with data: a data visualization guide for business professionals by cole Nussbaumer knaflic
  7. Research paper - Cheat Sheets for Data Visualization Techniques by Zezhong Wang, Lovisa Sundin, Dave Murray-Rust, Benjamin Bach

Suggestions:

In case, if you find any books, cheat sheets, or charts missing and if you would like to suggest some new documents please let me know in the discussion sections!

Resources:

• Charts: I personally recommend data viz catalogue, it's easy to understand with their explanation!
• Python codes: Plotly for python and Python graph gallery
• R codes for charts:Plotly for R
• d3 codes: Visualization codes using d3

Request to kaggle users:

• A kind request to kaggle users to create notebooks on different visualization charts as per their interest by choosing a dataset of their own as many beginners and other experts could find it useful!

• To create interactive EDA using animation with a combination of data visualization charts to give an idea about how to tackle data and extract the insights from the data

Suggestion and queries:

Feel free to use the discussion platform of this data set to ask questions or any queries related to the data visualization corpus and data visualization techniques

Kindly upvote the dataset if you find it useful or if you wish to appreciate the effort taken to gather this corpus! Thank you and have a great day!

Description -

Curated Datasets of Tuberculosis and respective comorbidity conditions against Diabetes and HIV. These Datasets are processed and ready for implementation into Machine Learning algorithms as well as extensive Exploratory Data Analysis workflows to classify disease phenotypes against specific gene expression signatures.

Dataset Labels -

1. GSE114192 - TB-Diabetes
2. GSE193978 - TB-Diabetes
3. GSE249102 - TB-Diabetes
4. GSE165708 - TB-HIV
5. GSE248986 - TB-HIV

Python Script - Python Script mentioning the overall dataset preparation has been added