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/

These are the data belonging to the project 'Measuring (online) word segmentation in adults'. Offline and online (click detection) data is saved seperately. Offline data is saved seperately per experiment and combined in one datafile ('WS_Offline_Exp123"). These datafiles can be used to run the R Markdown files (place in folder "Data").

Improved animal tracking technologies provide opportunities for novel segmentation of movement tracks/paths into behavioral activity modes (BAMs) critical to understanding the ecology of individuals and the functioning of ecosystems. Current BAM segmentation includes biological change point analyses and hidden Markov models. Here we use an elemental approach to segmenting tracks into µ‐step-long "base segments" and m-base-segment-long "words". These are respectively clustered into n statistical movement elements (StaMEs) and k "raw" canonical activity modes (CAMs). Once the words are coded using m extracted StaME symbols, those encoded by the same string of symbols, after a rectification processes has been implemented to minimize misassigned words, are identified with particular "rectified" CAM types. The percent of reassignment errors, along with information theory measures, are used to compare the efficiencies of coding both simulated and empirical barn owl data for a selection of parameter values and approaches to clustering. Methods The methods developed in this manuscript have been demonstrated on both empirical and simulated relocation data. The former corresponds to an adult female barn owl (Tyto alba) individual, which is part of a population tagged at our study site in the Harod Valley in northeast Israel. The simulated data has been generated using a two‐mode step‐selection kernel simulator called Numerus ANIMOVER_1 (Getz et al. (2023)). Data processing has been carried out using a series of several machine learning and other algorithms presented in Varun Sethi's GitHub repository Hierarchical-path-segmentation-II.

Accurate colony counting is crucial for assessing microbial growth in high-throughput workflows. However, existing automated counting solutions struggle with the issue of merged colonies, a common occurrence in high-throughput plating. To overcome this limitation, we propose MCount, the only known solution that incorporates both contour information and regional algorithms for colony counting. By optimizing the pairing of contours with regional candidate circles, MCount can accurately infer the number of merged colonies. We evaluate MCount on a precisely labeled Escherichia coli dataset of 960 images (15,847 segments) and achieve an average error rate of 3.99%, significantly outperforming existing published solutions such as NICE (16.54%), AutoCellSeg (33.54%), and OpenCFU (50.31%). MCount is user friendly as it only requires two hyperparameters. To further facilitate deployment in scenarios with limited labeled data, we propose statistical methods for selecting the hyperparameters using...,

Folder "96 well colonies" contains 10 microplate images with the original resolution. Â Folder "results" contains a segmentation and quantification results from the microplate images. Folder "Codes" includes Python source codes.

, , # MCount: An automated colony counting tool for high-throughput microbiology

https://doi.org/10.5061/dryad.2280gb62f

File: Codes.zip

Description:Â Includes .ipynb Python source code and example images / results.

File: 96-well plate.zip

Description:Â Contains 10 microplate images of the original resolution. Each filename is self-explanatory (an image was independently taken at a different condition or timepoint)

File: results.zip

Description:Â Contains a segmentation and quantified data from the microplate images necessary for replicating the study.

• Each subfolder "image_1", "image 2"... "image 10" corresponds to data analyzed from the original image contained in folder "96-well plate.zip"
• In each subfolder (e.g., "results/image_1"), there are 96 JPG images with filenames ending with from "A1.jpg" to "H12.jpg". They are partitions of the original image and refer to the well number of the microplate.
• In e...

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The machine learning market segmentation report helps the market vendors with information about the geographical segmentation of this market.

With 38%, North America has the highest market share of the machine learning market. One of the key factors that will drive the market growth is increasing adoption of cloud-based offerings. Growth is organized retail distribution channel will drive the growth of this market in the North America region. To gather more information regarding the geographical landscape distribution, click here for a free sample report.

Data files for analysis reported in M&C manuscript

The goal of this work is to provide an empirical basis for research on image segmentation and boundary detection. To this end, 12,000 hand-labeled segmentations of 1,000 Corel dataset images from 30 human subjects have been collected. Half of the segmentations were obtained from presenting the subject with a color image; the other half from presenting a grayscale image. The public benchmark based on this data consists of all of the grayscale and color segmentations for 300 images. The images are divided into a training set of 200 images, and a test set of 100 images.

Cite: @InProceedings{ MartinFTM01, author = {D. Martin and C. Fowlkes and D. Tal and J. Malik}, title = {A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics}, booktitle = {Proc. 8th Int'l Conf. Computer Vision}, year = {2001}, month = {July}, volume = {2}, pages = {416--423} }

The capability to detect boulders on the surface of small bodies is beneficial for vision-based applications such as hazard detection during critical operations and navigation. This task is challenging due to the wide assortment of irregular shapes, the characteristics of the boulders population, and the rapid variability in the illumination conditions. Moreover, the lack of publicly available labeled datasets for these applications damps the research about data-driven algorithms. To tackle these challenges, the Dataset fOr bOuldeRs Segmentation (DOORS) has been designed. The dataset is thought to be useful for (but not limited to) boulders recognition, centroid regression, segmentation, and navigation applications. The dataset is divided into two sets: Regression: Contains images, masks, and labels for 4 splits of single boulders positioned on the surface of a spherical mesh. It can be used to perform navigation, boulder recognition, segmentation, and centroid regression. Segmentation: Contain images, masks, and labels of 2 datasets: DS1 and DS2. DS1 is made of the same images of the Regression dataset but is specifically designed for segmentation. DS2 is made of images with multiple instances of boulders appearing on the surface of the Didymos asteroid model A detailed characterization of the statistical properties of the DOORS dataset and the description of the Blender setup used to generate it is visible in "DOORS: Dataset fOr bOuldeRs Segmentation. Statistical properties and Blender setup", by Mattia Pugliatti and Francesco Topputo, arXiv pre-print, Oct 2022.

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The apple market segmentation analysis identifies Distribution Channel (offline and online) and Geographic Landscape (APAC, Europe, MEA, North America, and South America). The subsegments explored in the apple market research are as follows:

Apple Market Segmentation Analysis by Distribution ChannelofflineonlineApple Market Segmentation Analysis by Geographic LandscapeAPACEuropeMEANorth AmericaSouth America

Context

The goal of this work is to provide an empirical basis for research on image segmentation and boundary detection.

Content

The dataset consists of 500 natural images, ground-truth human annotations and benchmarking code. The data is explicitly separated into disjoint train, validation and test subsets. The dataset is an extension of the BSDS300, where the original 300 images are used for training / validation and 200 fresh images, together with human annotations, are added for testing. Each image was segmented by five different subjects on average.

Acknowledgements

This dataset was obtained and modified from The Berkeley Segmentation Dataset and Benchmark from Computer Vision Group (University of California Berkeley). For more details on the dataset refer dataset's home page and related publication. Work based on the dataset should cite:

@InProceedings{MartinFTM01,
 author = {D. Martin and C. Fowlkes and D. Tal and J. Malik},
 title = {A Database of Human Segmented Natural Images and its
      Application to Evaluating Segmentation Algorithms and
      Measuring Ecological Statistics},
 booktitle = {Proc. 8th Int'l Conf. Computer Vision},
 year = {2001},
 month = {July},
 volume = {2},
 pages = {416--423}
}

A prerequisite for spoken language learning is segmenting continuous speech into words. Amongst many possible cues to identify word boundaries, listeners can use both transitional probabilities between syllables and various prosodic cues. However, the relative importance of these cues remains unclear, and previous experiments have not directly compared the effects of contrasting multiple prosodic cues. We used artificial language learning experiments, where native German speaking participants extracted meaningless trisyllabic “words” from a continuous speech stream, to evaluate these factors. We compared a baseline condition (statistical cues only) to five test conditions, in which word-final syllables were either (a) followed by a pause, (b) lengthened, (c) shortened, (d) changed to a lower pitch, or (e) changed to a higher pitch. To evaluate robustness and generality we used three tasks varying in difficulty. Overall, pauses and final lengthening were perceived as converging with the statistical cues and facilitated speech segmentation, with pauses helping most. Final-syllable shortening hindered baseline speech segmentation, indicating that when cues conflict, prosodic cues can override statistical cues. Surprisingly, pitch cues had little effect, suggesting that duration may be more relevant for speech segmentation than pitch in our study context. We discuss our findings with regard to the contribution to speech segmentation of language-universal boundary cues vs. language-specific stress patterns.

Statistical analysis for semantic segmentation on various algorithms for dataset 1.

Egyptian Hieroglyphic Signs Segmentation with Orientation

Datasets for Ancient Egyptian Hieroglyphic Research

Egyptian Hieroglyphic Signs Segmentation with Orientation (SS) Dataset

Overview:

The Signs Segmentation (SS) Dataset comprises 300 images, each containing a single line of ordered Ancient Egyptian hieroglyphic signs. These images were automatically cropped from segmented lines within the HLA Dataset using our trained layout analysis models. The SS Dataset is specifically designed for the task of segmenting individual hieroglyphic signs within a line of text.

Data Generation and Annotation:

The lines of hieroglyphs in the HLA Dataset were processed using our trained models to automatically extract individual lines. These cropped line images form the basis of the SS Dataset. Each image in this dataset was then manually annotated using the CVAT platform with polygonal segmentation masks for three distinct classes: “Left Sign”, “Right Sign”, and “Dual Sign” (representing ligatures or signs that visually merge) that present the orientation of signs.

Key Statistics:

• Total Images: 300
• Content: Single lines of ordered hieroglyphic signs
• Annotation Classes: 3 (“Left Sign”, “Right Sign”, “Dual Sign”)
• Annotation Type: Polygon Segmentation Masks

Potential Uses: This dataset is well-suited for training and evaluating models for:

1. Individual hieroglyphic sign segmentation within a line of text.
2. Distinguishing between individual and joined signs.
3. Classify the orientation of signs.

Json annotation files "in coco format":

• Train: 272 images.
• Validation: 28 images.
• Test: 0 images.

Dataset Citation :

El-Taher, Ahmed; Azab, Shahera; Mohamed, Ammar, 2025, "Egyptian Hieroglyphic Signs Segmentation with Orientation", https://doi.org/10.7910/DVN/HIFG2P,Harvard Dataverse, V1

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Chocolate Market research indicates that the global market can be primarily differentiated into Product (Milk chocolate, Dark chocolate, and White chocolate) and Geographic Landscape (APAC, Europe, MEA, North America, and South America). The chocolate market has been analyzed on various dimensions, and the market segments have been reviewed both qualitatively and quantitatively.

Chocolate Market Segmentation Analysis by ProductMilk chocolateDark chocolateWhite chocolateChocolate Market Segmentation Analysis by Geographic LandscapeAPACEuropeMEANorth AmericaSouth America

Match Rate Improvement: Higher with enriched data Segmentation Accuracy: Improved with enriched attributes CTR/CVR: Measurable lift with enriched targeting CAC: Potentially reduced with better targeting ROAS: Measurable improvement with enriched audiences Fill-Rate: Quality metric for enrichment evaluation Precision/Recall: Statistical quality metrics for enrichment

The Public Safety market is a vital sector that encompasses various technologies and services aimed at ensuring the security and well-being of communities and individuals. With the rise in urbanization, population growth, and the complexity of safety threats, the demand for robust public safety solutions has surged.

Statistical Analysis for semantic segmentation on various classifiers for dataset 2.

The Community Engagement Software market has emerged as a critical tool for organizations looking to foster meaningful interactions and build lasting relationships with their audiences. With the digital landscape continually evolving, businesses, non-profits, and governmental agencies increasingly rely on community

The Virtual Experiment System (VES) market is experiencing significant growth due to its transformative impact on industries like education, healthcare, and research and development. These systems allow users to conduct experiments in a virtual setting, eliminating the need for physical materials and environments. T

IAM: Enhancing RGB-D Instance Segmentation with New Benchmarks

For detailed statistics about our datasets, please refer to the following paper:Preprint: https://arxiv.org/abs/2501.01685 Github pages:https://github.com/AIM-SKKU/NYUDv2-IS https://github.com/AIM-SKKU/SUN-RGBD-IS https://github.com/AIM-SKKU/Box-IS