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.

BackgroundThe Department of Rehabilitation Medicine is key to improving patients’ quality of life. Driven by chronic diseases and an aging population, there is a need to enhance the efficiency and resource allocation of outpatient facilities. This study aims to analyze the treatment preferences of outpatient rehabilitation patients by using data and a grading tool to establish predictive models. The goal is to improve patient visit efficiency and optimize resource allocation through these predictive models.MethodsData were collected from 38 Chinese institutions, including 4,244 patients visiting outpatient rehabilitation clinics. Data processing was conducted using Python software. The pandas library was used for data cleaning and preprocessing, involving 68 categorical and 12 continuous variables. The steps included handling missing values, data normalization, and encoding conversion. The data were divided into 80% training and 20% test sets using the Scikit-learn library to ensure model independence and prevent overfitting. Performance comparisons among XGBoost, random forest, and logistic regression were conducted using metrics, including accuracy and receiver operating characteristic (ROC) curves. The imbalanced learning library’s SMOTE technique was used to address the sample imbalance during model training. The model was optimized using a confusion matrix and feature importance analysis, and partial dependence plots (PDP) were used to analyze the key influencing factors.ResultsXGBoost achieved the highest overall accuracy of 80.21% with high precision and recall in Category 1. random forest showed a similar overall accuracy. Logistic Regression had a significantly lower accuracy, indicating difficulties with nonlinear data. The key influencing factors identified include distance to medical institutions, arrival time, length of hospital stay, and specific diseases, such as cardiovascular, pulmonary, oncological, and orthopedic conditions. The tiered diagnosis and treatment tool effectively helped doctors assess patients’ conditions and recommend suitable medical institutions based on rehabilitation grading.ConclusionThis study confirmed that ensemble learning methods, particularly XGBoost, outperform single models in classification tasks involving complex datasets. Addressing class imbalance and enhancing feature engineering can further improve model performance. Understanding patient preferences and the factors influencing medical institution selection can guide healthcare policies to optimize resource allocation, improve service quality, and enhance patient satisfaction. Tiered diagnosis and treatment tools play a crucial role in helping doctors evaluate patient conditions and make informed recommendations for appropriate medical care.

TinyStories

A Diverse, Richly Annotated Corpus of Short-Form Stories

By Huggingface Hub [source]

About this dataset

This dataset contains the text of a remarkable collection of short stories known as the TinyStories Corpus. With over 2,000 annotated stories, it is populated with an array of diverse styles and genres from multiple sources. This corpus is enriched by intricate annotations across each narrative content, making it a valuable resource for narrative text classification. The text field in each row includes the entirety of each story that can be used to identify plots, characters and other features associated with story-telling techniques. Through this collection of stories, users will gain an extensive insight into a wide range of narratives which could be used to produce powerful machine learning models for Narrative Text Classification

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Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

In this dataset, each row contains a short story along with its associated labels for narrative text classification tasks. The data consists of the following columns: - text: The story text itself (string) - validation.csv: Contains a set of short stories for validation (dataframe) - train.csv: Contains the text of short stories used for narrative text classification (dataframe)

The data contained in both files can be used for various types of machine learning tasks related to narrative text classification. These include but are not limited to experiments such as determining story genres, predicting user reactions, sentiment analysis etc.

To get started with using this dataset, begin by downloading both validation and train csv files from Kaggle datasets page and saving them on your computer or local environment. Once downloaded, you may need to preprocess both datasets by cleaning up any unnecessary/wrongly formatted values or duplicate entries if any exists within it before proceeding further on to your research work or machine learning task experimentations as these have great impacts on your research results accuracy rate which you do not want compromised!

Next step is simply loading up these two datasets into Python pandas dataframes so that they can easily be manipulated and analyzed using common tools associated with Natural Language Processing(NLP). This would require you writing few simple lines using pandas API functions like read_csv(), .append(), .concat()etc depending upon what kind of analysis/experiment you intend conducting afterwards utilizing this dataset in Python Jupyter Notebook framework as well as other machine learning frameworks popular among data scientists like scikit-learn if it will be something more complex than simple NLP task operations!

By now if done everything mentioned correctly here then we are ready now to finally get into actually working out our desired applications from exploring potential connections between different narratives or character traits via supervised Machine Learning models such as Naive Bayes Classifier among many others that could ultimately provide us useful insights revealing patterns existing underneath all those texts! With all necessary datas loaded up in supporting python platforms correctly so feel free to make interesting discoveries/predictions from extensive analyses provided by this richly annotated TinyStories Narrative Dataset!

Research Ideas

• Creating a text classification algorithm to automatically categorize short stories by genre.
• Developing an AI-based summarization tool to quickly summarize the main points in a story.
• Developing an AI-based story generator that can generate new stories based on existing ones in the dataset

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: validation.csv | Column name | Description | |:--------------|:--------------------------------| | text | The text of the story. (String) |

File: train.csv | Column name | Description | |:--------------|:----------------------------...

Analyzing customers’ characteristics and giving the early warning of customer churn based on machine learning algorithms, can help enterprises provide targeted marketing strategies and personalized services, and save a lot of operating costs. Data cleaning, oversampling, data standardization and other preprocessing operations are done on 900,000 telecom customer personal characteristics and historical behavior data set based on Python language. Appropriate model parameters were selected to build BPNN (Back Propagation Neural Network). Random Forest (RF) and Adaboost, the two classic ensemble learning models were introduced, and the Adaboost dual-ensemble learning model with RF as the base learner was put forward. The four models and the other four classical machine learning models-decision tree, naive Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) were utilized respectively to analyze the customer churn data. The results show that the four models have better performance in terms of recall rate, precision rate, F1 score and other indicators, and the RF-Adaboost dual-ensemble model has the best performance. Among them, the recall rates of BPNN, RF, Adaboost and RF-Adaboost dual-ensemble model on positive samples are respectively 79%, 90%, 89%,93%, the precision rates are 97%, 99%, 98%, 99%, and the F1 scores are 87%, 95%, 94%, 96%. The RF-Adaboost dual-ensemble model has the best performance, and the three indicators are 10%, 1%, and 6% higher than the reference. The prediction results of customer churn provide strong data support for telecom companies to adopt appropriate retention strategies for pre-churn customers and reduce customer churn.

Demographic Analysis of Shopping Behavior: Insights and Recommendations

Dataset Information: The Shopping Mall Customer Segmentation Dataset comprises 15,079 unique entries, featuring Customer ID, age, gender, annual income, and spending score. This dataset assists in understanding customer behavior for strategic marketing planning.

Cleaned Data Details: Data cleaned and standardized, 15,079 unique entries with attributes including - Customer ID, age, gender, annual income, and spending score. Can be used by marketing analysts to produce a better strategy for mall specific marketing.

Challenges Faced: 1. Data Cleaning: Overcoming inconsistencies and missing values required meticulous attention. 2. Statistical Analysis: Interpreting demographic data accurately demanded collaborative effort. 3. Visualization: Crafting informative visuals to convey insights effectively posed design challenges.

Research Topics: 1. Consumer Behavior Analysis: Exploring psychological factors driving purchasing decisions. 2. Market Segmentation Strategies: Investigating effective targeting based on demographic characteristics.

Suggestions for Project Expansion: 1. Incorporate External Data: Integrate social media analytics or geographic data to enrich customer insights. 2. Advanced Analytics Techniques: Explore advanced statistical methods and machine learning algorithms for deeper analysis. 3. Real-Time Monitoring: Develop tools for agile decision-making through continuous customer behavior tracking. This summary outlines the demographic analysis of shopping behavior, highlighting key insights, dataset characteristics, team contributions, challenges, research topics, and suggestions for project expansion. Leveraging these insights can enhance marketing strategies and drive business growth in the retail sector.

References OpenAI. (2022). ChatGPT [Computer software]. Retrieved from https://openai.com/chatgpt. Mustafa, Z. (2022). Shopping Mall Customer Segmentation Data [Data set]. Kaggle. Retrieved from https://www.kaggle.com/datasets/zubairmustafa/shopping-mall-customer-segmentation-data Donkeys. (n.d.). Kaggle Python API [Jupyter Notebook]. Kaggle. Retrieved from https://www.kaggle.com/code/donkeys/kaggle-python-api/notebook Pandas-Datareader. (n.d.). Retrieved from https://pypi.org/project/pandas-datareader/

🌿 Overview

Dive into the Indoor Plant Health & Growth Dataset, a comprehensive collection of environmental and care-related observations across 1,000 samples of common houseplants. Whether you're building a plant health prediction model, exploring smart gardening applications, or practicing data preprocessing, this dataset offers a rich and realistic foundation.

🧠 Context

Indoor plants are highly sensitive to their environment, and factors like sunlight, watering, humidity, and soil quality play crucial roles in their growth and health. This dataset brings together diverse parameters — from leaf count and fertilizer use to pest detection and soil moisture — to support a range of machine learning, horticultural analysis, and environmental modeling tasks.

Use this dataset to explore the complex interplay between plant care and outcomes, or to develop applications that support sustainable and data-driven plant maintenance.

📊 Dataset Details

Size: 1,000 rows × 17 columns

Data Format: CSV file, fully compatible with Python, R, Excel, etc.

🔑 Features:

Plant_ID: Scientific plant name (e.g., Ficus lyrata, Aloe vera)

Height_cm: Height of the plant in centimeters

Leaf_Count: Total number of leaves

New_Growth_Count: Number of new buds or leaves observed

Health_Notes: Qualitative notes on plant appearance (e.g., “yellowing leaves”)

Watering_Amount_ml:Amount of water given in milliliters

Watering_Frequency_days: Days between watering sessions

Sunlight_Exposure: Descriptive light exposure (e.g., “3 hrs morning sun”)

Room_Temperature_C: Room temperature in Celsius

Humidity_%:Room humidity percentage

Fertilizer_Type:Type of fertilizer used

Fertilizer_Amount_ml: Amount of fertilizer applied

Pest_Presence: Detected pest type (if any)

Pest_Severity: Level of pest infestation

Soil_Moisture_%: Soil moisture percentage

Soil_Type: Soil classification (e.g., loamy, clay, sandy)

Health_Score: Rating from 1 (dying) to 5 (thriving)

📈 Data Quality Notes

Clean and ready for modeling, with realistic variation across samples

Features a mix of numerical, categorical, and textual data

Ideal for data cleaning, feature engineering, and exploratory analysis

Great practice for encoding categorical variables and building regression/classification models

💡 Potential Use Cases

Train machine learning models to predict plant health

Build smart gardening or IoT-based plant monitoring applications

Analyze the impact of environmental and care parameters on plant growth

Visualize trends in plant care routines and outcomes

Practice data preprocessing, transformation, and modeling workflows

📌 Please consider upvoting if you find this dataset helpful. Happy growing — and modeling! 🌱

Cooperation involves the challenge of jointly selecting one from multiple goals while maintaining the team’s joint commitment to it. We test joint commitment in a multi-player hunting game, combining psychophysics and computational modeling. Joint commitment is modeled through an "Imagined We" (IW) approach, where each agent uses Bayesian inference to infer the intention of “We”, an imagined supraindividual agent controlling all agents as its body parts. This is compared against a Reward Sharing (RS) model, which frames cooperation through reward sharing via multi-agent reinforcement learning (MARL). Both humans and IW, but not RS, maintained high performance by jointly committing to a single prey, regardless of prey quantity or speed. Human observers rated all hunters in both human and IW teams as making high contributions to the catch, regardless of their proximity to the prey, suggesting that high-quality hunting stemmed from sophisticated cooperation rather than individual strategie...

Analyzing customers’ characteristics and giving the early warning of customer churn based on machine learning algorithms, can help enterprises provide targeted marketing strategies and personalized services, and save a lot of operating costs. Data cleaning, oversampling, data standardization and other preprocessing operations are done on 900,000 telecom customer personal characteristics and historical behavior data set based on Python language. Appropriate model parameters were selected to build BPNN (Back Propagation Neural Network). Random Forest (RF) and Adaboost, the two classic ensemble learning models were introduced, and the Adaboost dual-ensemble learning model with RF as the base learner was put forward. The four models and the other four classical machine learning models-decision tree, naive Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) were utilized respectively to analyze the customer churn data. The results show that the four models have better performance in terms of recall rate, precision rate, F1 score and other indicators, and the RF-Adaboost dual-ensemble model has the best performance. Among them, the recall rates of BPNN, RF, Adaboost and RF-Adaboost dual-ensemble model on positive samples are respectively 79%, 90%, 89%,93%, the precision rates are 97%, 99%, 98%, 99%, and the F1 scores are 87%, 95%, 94%, 96%. The RF-Adaboost dual-ensemble model has the best performance, and the three indicators are 10%, 1%, and 6% higher than the reference. The prediction results of customer churn provide strong data support for telecom companies to adopt appropriate retention strategies for pre-churn customers and reduce customer churn.

** 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! 🎯

Context

Objective is to remove/suppress indoor or outdoor noises present during conversation for other person on call. Improve recording system or get rid of the noises present in the environment.

Real time noise reduction using DNN : - https://devblogs.nvidia.com/nvidia-real-time-noise-suppression-deep-learning/ - http://staff.ustc.edu.cn/~jundu/Publications/publications/Trans2015_Xu.pdf

Design a network which works for stationary and non-stationary noises

Content

Prepared Data : 1 sec speech mixed with noises present in indoor and outdoor environment - For preparing longer speech duration with noise present in it - Use audio and marcas python library
- For filtering noise for longer speech - make 1 sec model for cleaning the noise and repeat for process in chunks of 1 sec for longer wav files.

• Data preparation technique (a) ProcessWav.ipynb (b) Processdata.ipynb

  • https://github.com/vijay033/Noise-Suppression-Auto-Encoder

• Additional reference repository for preparing datasets

  • https://github.com/vijay033/Audio-Noising-DeNoising/blob/master/prepareData.ipynb

• Model architecture and summary attached along with dataset to reduce noise :

  • Modeling train(noisy input) vs y_train (clean output) similar to noising-denoising technique for images

• Train and test code is available for reference ( https://github.com/vijay033/Noise-Suppression-Auto-Encoder ) (a) trainnoise.ipynb ( train ) - generate model.h5 (b) testprediction ( using model.h5 ) - chunking of audio for 1 sec - passed through model - rejoin all chunked files

Sampling Rate = 16K File format : WAV NPY dimension : 257 X 62 X 3

Acknowledgements

• Since data is not balanced, prepare more data in balanced way
• Uploaded result gives,
  1. proof of work on dataset
  2. functioning of network design (autoencoder)
  3. inspires to improve existing work
  4. SNR for test samples ( 13 files - NoiseTest_SNR.pptx )
• System limitations to play with epoc and batch size
• Other Reference Links :
• Wav2ImageSpectrogram - ImageSpectogram2Wav https://www.youtube.com/watch?v=QrBpex7ZCMY https://github.com/sikora507/elgen/blob/master/src/audio%20analysis.ipynb
• AutoEncoder Model For Image Denoising https://github.com/nsarang/ImageDenoisingAutoencdoer
• Mix Noise With Speech https://pypi.org/project/maracas/

• Chunking wav files in small segments https://www.programcreek.com/python/example/89506/pydub.AudioSegment.from_file https://readthedocs.org/projects/audiosegment/downloads/pdf/latest/

• Free Sound Noise https://www.freesoundeffects.com/free-sounds/

• Speech Dataset http://www.openslr.org/12 ( Not all sample used : train-clean-100.tar.gz 6.3G) https://openslr.org/83/

Inspiration

Model which can remove or suppress the noises from the original speech. CNN or Auto encoder model to remove or suppress noise from the original speech.