This dataset is a sample from the TalkingData AdTracking competition. I kept all the positive examples (where is_attributed == 1), while discarding 99% of the negative samples. The sample has roughly 20% positive examples.

For this competition, your objective was to predict whether a user will download an app after clicking a mobile app advertisement.

File descriptions

train_sample.csv - Sampled data

Data fields

Each row of the training data contains a click record, with the following features.

• ip: ip address of click.
• app: app id for marketing.
• device: device type id of user mobile phone (e.g., iphone 6 plus, iphone 7, huawei mate 7, etc.)
• os: os version id of user mobile phone
• channel: channel id of mobile ad publisher
• click_time: timestamp of click (UTC)
• attributed_time: if user download the app for after clicking an ad, this is the time of the app download
• is_attributed: the target that is to be predicted, indicating the app was downloaded

Note that ip, app, device, os, and channel are encoded.

I'm also including Parquet files with various features for use within the course.

Dataset

This dataset was created by Chokaew Phonhan

Contents

• sensor_geometory_add_feature.csv
  Added basic statistics and a flag for whether or not the sensor is DeepCore to 5160 sensors.
• train_meta_add_feature.parquet
  Added basic statistics for each event.

Context

There's a data from the 9th chapter of the Feature Engineering for Machine Learning book by Alice Zheng and Amanda Casari. The data is suitable for the last project os this book: Academic Papers Recommendation System.

Dataset

This dataset was created by furu-nag

Contents

how to use library load data and preprocess data at the same time

use below code:

train,test = loadAndPreprocess(train_path,test_path)

Dataset

This dataset was created by Aleksandr Razin

Contents

Dataset

This dataset was created by blue7red

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by ItaiHarpaz

Contents

The datasets are derived from eye fundus images provided in Kaggle's 'APTOS 2019 Blindness Detection' competition. The competition involves classification of eye fundus images into 5 levels of severity in diabetic retinopathy.

Unlike most participants who used deep learning approach to this classification problem, here we tried using Fractal Dimensions and Persistent Homology (one of the major tools in Topological Data Analysis, TDA) in extracting features from images, as inputs to simpler ML algorithms like SVM. It shows some promising results with this approach.

There are three files in this dataset:

1. Process_Images.html - R scripts for extracting Fractal Dimensions and Persistent Homology features from images.

2. train_features.RDS and test_features.RDS - the output RDS (R dataset files) for training and testing images for the above Kaggle competition.

Columns in train_features.RDS & test_features.RDS:

1. id_code - image id

2. diagnosis - severity of diabetic retinopathy on a scale of 0 to 4: 0=No DR; 1=Mild; 2=Moderate; 3=Severe; 4=Proliferative DR; Artificially set to be 0 for test_features.RDS

3. n - number of persistent homology components detected from the image

4. fd1 to fd21 - proportion of sliding windows having a specific fractal dimensions: fd1 = proportion of windows having FD=2; fd2=proportion of windows having FD in (2, 2.05];... fd21=proportion of windows having FD in (2.95,3.00]

5. l1_2 to l1_499 - silhouette (p=0.1, dim=1) at various time steps.

Dataset

This dataset was created by Hongyi Shao

Contents

Dataset

This dataset was created by Mukesh Manral

Contents

Dataset

This dataset was created by Bhuwanesh Tripathi

Released under Other (specified in description)

Contents

Dataset

This dataset was created by Zhou Hong

Contents

Dataset

This dataset was created by Anagha Joshi

Contents

This is just a list of SMILES strings for 9 molecules.

The attached code is an example for how to generate new molecular features from these (or other) smiles strings.

Dataset

This dataset was created by Mathurin Aché

Contents

This dataset comprises user comments collected from YouTube videos discussing Prabowo Subianto’s speech in relation to former U.S. President Donald Trump’s tariff policies. The data is organized into three separate Excel files, each representing a different sentiment distribution:

1. Balanced Dataset: Contains an equal number of comments across all three sentiment classes — positive, negative, and neutral — to support unbiased model training and evaluation.

2. Unbalanced Dataset: Reflects the natural distribution of sentiments as observed in the raw data, providing a realistic scenario for real-world sentiment analysis.

3. Neutral-Inclusive Dataset: A version of the dataset that includes comments labeled as neutral, in addition to positive and negative sentiments, offering a more comprehensive view of public opinion.

This dataset is suitable for sentiment classification tasks, public opinion mining, and research in political discourse analysis, particularly in the context of sentiment analysis

Dataset

This dataset was created by Proloy Pal

Contents

Dataset

This dataset was created by Shrishti Tayde

Released under MIT

Contents