The complete dataset used in the analysis comprises 36 samples, each described by 11 numeric features and 1 target. The attributes considered were caspase 3/7 activity, Mitotracker red CMXRos area and intensity (3 h and 24 h incubations with both compounds), Mitosox oxidation (3 h incubation with the referred compounds) and oxidation rate, DCFDA fluorescence (3 h and 24 h incubations with either compound) and oxidation rate, and DQ BSA hydrolysis. The target of each instance corresponds to one of the 9 possible classes (4 samples per class): Control, 6.25, 12.5, 25 and 50 µM for 6-OHDA and 0.03, 0.06, 0.125 and 0.25 µM for rotenone. The dataset is balanced, it does not contain any missing values and data was standardized across features. The small number of samples prevented a full and strong statistical analysis of the results. Nevertheless, it allowed the identification of relevant hidden patterns and trends.

Exploratory data analysis, information gain, hierarchical clustering, and supervised predictive modeling were performed using Orange Data Mining version 3.25.1 [41]. Hierarchical clustering was performed using the Euclidean distance metric and weighted linkage. Cluster maps were plotted to relate the features with higher mutual information (in rows) with instances (in columns), with the color of each cell representing the normalized level of a particular feature in a specific instance. The information is grouped both in rows and in columns by a two-way hierarchical clustering method using the Euclidean distances and average linkage. Stratified cross-validation was used to train the supervised decision tree. A set of preliminary empirical experiments were performed to choose the best parameters for each algorithm, and we verified that, within moderate variations, there were no significant changes in the outcome. The following settings were adopted for the decision tree algorithm: minimum number of samples in leaves: 2; minimum number of samples required to split an internal node: 5; stop splitting when majority reaches: 95%; criterion: gain ratio. The performance of the supervised model was assessed using accuracy, precision, recall, F-measure and area under the ROC curve (AUC) metrics.

This file is the data set form the famous publication Francis J. Anscombe "*Graphs in Statistical Analysis*", The American Statistician 27 pp. 17-21 (1973) (doi: 10.1080/00031305.1973.10478966). It consists of four data sets of 11 points each. Note the peculiarity that the same 'x' values are used for the first three data sets, and I have followed this exactly as in the original publication (originally done to save space), i.e. the first column (x123) serves as the 'x' for the next three 'y' columns; y1, y2 and y3.

In the dataset Anscombe_quintet_data.csv there is a new column (y5) as an example of Simpson's paradox (C. McBride Ellis "*Anscombe dataset No. 5: Simpson's paradox*", Zenodo doi: 10.5281/zenodo.15209087 (2025)

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).

Sonification for Exploratory Data Analysis #### Chapter 8: Sonification Models In Chapter 8 of the thesis, 6 sonification models are presented to give some examples for the framework of Model-Based Sonification, developed in Chapter 7. Sonification models determine the rendering of the sonification and possible interactions. The "model in mind" helps the user to interprete the sound with respect to the data. ##### 8.1 Data Sonograms Data Sonograms use spherical expanding shock waves to excite linear oscillators which are represented by point masses in model space. * Table 8.2, page 87: Sound examples for Data Sonograms File: Iris dataset: started in plot (a) at S0 (b) at S1 (c) at S2
10d noisy circle dataset: started in plot (c) at S0 (mean) (d) at S1 (edge)
10d Gaussian: plot (d) started at S0
3 clusters: Example 1
3 clusters: invisible columns used as output variables: Example 2 Description: Data Sonogram Sound examples for synthetic datasets and the Iris dataset Duration: about 5 s ##### 8.2 Particle Trajectory Sonification Model This sonification model explores features of a data distribution by computing the trajectories of test particles which are injected into model space and move according to Newton's laws of motion in a potential given by the dataset. * Sound example: page 93, PTSM-Ex-1 Audification of 1 particle in the potential of phi(x). * Sound example: page 93, PTSM-Ex-2 Audification of a sequence of 15 particles in the potential of a dataset with 2 clusters. * Sound example: page 94, PTSM-Ex-3 Audification of 25 particles simultaneous in a potential of a dataset with 2 clusters. * Sound example: page 94, PTSM-Ex-4 Audification of 25 particles simultaneous in a potential of a dataset with 1 cluster. * Sound example: page 95, PTSM-Ex-5 sigma-step sequence for a mixture of three Gaussian clusters * Sound example: page 95, PTSM-Ex-6 sigma-step sequence for a Gaussian cluster * Sound example: page 96, PTSM-Iris-1 Sonification for the Iris Dataset with 20 particles per step. * Sound example: page 96, PTSM-Iris-2 Sonification for the Iris Dataset with 3 particles per step. * Sound example: page 96, PTSM-Tetra-1 Sonification for a 4d tetrahedron clusters dataset. ##### 8.3 Markov chain Monte Carlo Sonification The McMC Sonification Model defines a exploratory process in the domain of a given density p such that the acoustic representation summarizes features of p, particularly concerning the modes of p by sound. * Sound Example: page 105, MCMC-Ex-1 McMC Sonification, stabilization of amplitudes. * Sound Example: page 106, MCMC-Ex-2 Trajectory Audification for 100 McMC steps in 3 cluster dataset * McMC Sonification for Cluster Analysis, dataset with three clusters, page 107 * Stream 1 MCMC-Ex-3.1 * Stream 2 MCMC-Ex-3.2 * Stream 3 MCMC-Ex-3.3 * Mix MCMC-Ex-3.4 * McMC Sonification for Cluster Analysis, dataset with three clusters, T =0.002s, page 107 * Stream 1 MCMC-Ex-4.1 (stream 1) * Stream 2 MCMC-Ex-4.2 (stream 2) * Stream 3 MCMC-Ex-4.3 (stream 3) * Mix MCMC-Ex-4.4 * McMC Sonification for Cluster Analysis, density with 6 modes, T=0.008s, page 107 * Stream 1 MCMC-Ex-5.1 (stream 1) * Stream 2 MCMC-Ex-5.2 (stream 2) * Stream 3 MCMC-Ex-5.3 (stream 3) * Mix MCMC-Ex-5.4 * McMC Sonification for the Iris dataset, page 108 * MCMC-Ex-6.1 * MCMC-Ex-6.2 * MCMC-Ex-6.3 * MCMC-Ex-6.4 * MCMC-Ex-6.5 * MCMC-Ex-6.6 * MCMC-Ex-6.7 * MCMC-Ex-6.8 ##### 8.4 Principal Curve Sonification Principal Curve Sonification represents data by synthesizing the soundscape while a virtual listener moves along the principal curve of the dataset through the model space. * Noisy Spiral dataset, PCS-Ex-1.1 , page 113 * Noisy Spiral dataset with variance modulation PCS-Ex-1.2 , page 114 * 9d tetrahedron cluster dataset (10 clusters) PCS-Ex-2 , page 114 * Iris dataset, class label used as pitch of auditory grains PCS-Ex-3 , page 114 ##### 8.5 Data Crystallization Sonification Model * Table 8.6, page 122: Sound examples for Crystallization Sonification for 5d Gaussian distribution File: DCS started at center, in tail, from far outside Description: DCS for dataset sampled from N{0, I_5} excited at different locations Duration: 1.4 s * Mixture of 2 Gaussians, page 122 * DCS started at point A DCS-Ex1A * DCS started at point B DCS-Ex1B * Table 8.7, page 124: Sound examples for DCS on variation of the harmonics factor File: h_omega = 1, 2, 3, 4, 5, 6 Description: DCS for a mixture of two Gaussians with varying harmonics factor Duration: 1.4 s * Table 8.8, page 124: Sound examples for DCS on variation of the energy decay time File: tau_(1/2) = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2 Description: DCS for a mixture of two Gaussians varying the energy decay time tau_(1/2) Duration: 1.4 s * Table 8.9, page 125: Sound examples for DCS on variation of the sonification time File: T = 0.2, 0.5, 1, 2, 4, 8 Description: DCS for a mixture of two Gaussians on varying the duration T Duration: 0.2s -- 8s * Table 8.10, page 125: Sound examples for DCS on variation of model space dimension File: selected columns of the dataset: (x0) (x0,x1) (x0,...,x2) (x0,...,x3) (x0,...,x4) (x0,...,x5) Description: DCS for a mixture of two Gaussians varying the dimension Duration: 1.4 s * Table 8.11, page 126: Sound examples for DCS for different excitation locations File: starting point: C0, C1, C2 Description: DCS for a mixture of three Gaussians in 10d space with different rank(S) = {2,4,8} Duration: 1.9 s * Table 8.12, page 126: Sound examples for DCS for the mixture of a 2d distribution and a 5d cluster File: condensation nucleus in (x0,x1)-plane at: (-6,0)=C1, (-3,0)=C2, ( 0,0)=C0 Description: DCS for a mixture of a uniform 2d and a 5d Gaussian Duration: 2.16 s * Table 8.13, page 127: Sound examples for DCS for the cancer dataset File: condensation nucleus in (x0,x1)-plane at: benign 1, benign 2
malignant 1, malignant 2 Description: DCS for a mixture of a uniform 2d and a 5d Gaussian Duration: 2.16 s ##### 8.6 Growing Neural Gas Sonification * Table 8.14, page 133: Sound examples for GNGS Probing File: Cluster C0 (2d): a, b, c
Cluster C1 (4d): a, b, c
Cluster C2 (8d): a, b, c Description: GNGS for a mixture of 3 Gaussians in 10d space Duration: 1 s * Table 8.15, page 134: Sound examples for GNGS for the noisy spiral dataset File: (a) GNG with 3 neurons 1, 2
(b) GNG with 20 neurons end, middle, inner end
(c) GNG with 45 neurons outer end, middle, close to inner end, at inner end
(d) GNG with 150 neurons outer end, in the middle, inner end
(e) GNG with 20 neurons outer end, in the middle, inner end
(f) GNG with 45 neurons outer end, in the middle, inner end Description: GNG probing sonification for 2d noisy spiral dataset Duration: 1 s * Table 8.16, page 136: Sound examples for GNG Process Monitoring Sonification for different data distributions File: Noisy spiral with 1 rotation: sound
Noisy spiral with 2 rotations: sound
Gaussian in 5d: sound
Mixture of 5d and 2d distributions: sound Description: GNG process sonification examples Duration: 5 s #### Chapter 9: Extensions #### In this chapter, two extensions for Parameter Mapping

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.

Analyzing sales data is essential for any business looking to make informed decisions and optimize its operations. In this project, we will utilize Microsoft Excel and Power Query to conduct a comprehensive analysis of Superstore sales data. Our primary objectives will be to establish meaningful connections between various data sheets, ensure data quality, and calculate critical metrics such as the Cost of Goods Sold (COGS) and discount values. Below are the key steps and elements of this analysis:

1- Data Import and Transformation:

• Gather and import relevant sales data from various sources into Excel.
• Utilize Power Query to clean, transform, and structure the data for analysis.
• Merge and link different data sheets to create a cohesive dataset, ensuring that all data fields are connected logically.

2- Data Quality Assessment:

• Perform data quality checks to identify and address issues like missing values, duplicates, outliers, and data inconsistencies.
• Standardize data formats and ensure that all data is in a consistent, usable state.

3- Calculating COGS:

• Determine the Cost of Goods Sold (COGS) for each product sold by considering factors like purchase price, shipping costs, and any additional expenses.
• Apply appropriate formulas and calculations to determine COGS accurately.

4- Discount Analysis:

• Analyze the discount values offered on products to understand their impact on sales and profitability.
• Calculate the average discount percentage, identify trends, and visualize the data using charts or graphs.

5- Sales Metrics:

• Calculate and analyze various sales metrics, such as total revenue, profit margins, and sales growth.
• Utilize Excel functions to compute these metrics and create visuals for better insights.

6- Visualization:

• Create visualizations, such as charts, graphs, and pivot tables, to present the data in an understandable and actionable format.
• Visual representations can help identify trends, outliers, and patterns in the data.

7- Report Generation:

• Compile the findings and insights into a well-structured report or dashboard, making it easy for stakeholders to understand and make informed decisions.

Throughout this analysis, the goal is to provide a clear and comprehensive understanding of the Superstore's sales performance. By using Excel and Power Query, we can efficiently manage and analyze the data, ensuring that the insights gained contribute to the store's growth and success.

Identifying and dealing with outliers is an important part of data analysis. A new visualization, the O3 plot, is introduced to aid in the display and understanding of patterns of multivariate outliers. It uses the results of identifying outliers for every possible combination of dataset variables to provide insight into why particular cases are outliers. The O3 plot can be used to compare the results from up to six different outlier identification methods. There is anRpackage OutliersO3 implementing the plot. The article is illustrated with outlier analyses of German demographic and economic data. Supplementary materials for this article are available online.

Original dataset by user Abdallah Wagih Ibrahim https://www.kaggle.com/datasets/abdallahwagih/company-employees/data

I created a pivot table visualizing the relationship between annual salary and job rate(performance) by region.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F21036995%2F0ae505c2b2c7262a7fbda9acd9e90d2d%2FEmployeesPivotTable.png?generation=1723379896090719&alt=media" alt="">

Samples relating to 12 analyses of lay-theories of resilience among participants from USA, New Zealand, India, Iran, Russia (Moscow; Kazan). Central variables relate to participant endorsements of resilience descriptors. Demographic data includes (though not for all samples), Sex/Gender, Age, Ethnicity, Work, and Educational Status. Analysis 1. USA Exploratory Factor Analysis dataAnalysis 2. New Zealand Exploratory Factor Analysis dataAnalysis 3. India Exploratory Factor Analysis dataAnalysis 4. Iran Exploratory Factor Analysis dataAnalysis 5. Russian (Moscow) Exploratory Factor Analysis dataAnalysis 6. Russian (Kazan) Exploratory Factor Analysis dataAnalysis 7. USA Confirmatory Factor Analysis dataAnalysis 8. New Zealand Confirmatory Factor Analysis dataAnalysis 9. India Confirmatory Factor Analysis dataAnalysis 10. Iran Confirmatory Factor Analysis dataAnalysis 11. Russian (Moscow) Confirmatory Factor Analysis dataAnalysis 12. Russian (Kazan) Confirmatory Factor Analysis data

Unsupervised exploratory data analysis (EDA) is often the first step in understanding complex data sets. While summary statistics are among the most efficient and convenient tools for exploring and describing sets of data, they are often overlooked in EDA. In this paper, we show multiple case studies that compare the performance, including clustering, of a series of summary statistics in EDA. The summary statistics considered here are pattern recognition entropy (PRE), the mean, standard deviation (STD), 1-norm, range, sum of squares (SSQ), and X4, which are compared with principal component analysis (PCA), multivariate curve resolution (MCR), and/or cluster analysis. PRE and the other summary statistics are direct methods for analyzing datathey are not factor-based approaches. To quantify the performance of summary statistics, we use the concept of the “critical pair,” which is employed in chromatography. The data analyzed here come from different analytical methods. Hyperspectral images, including one of a biological material, are also analyzed. In general, PRE outperforms the other summary statistics, especially in image analysis, although a suite of summary statistics is useful in exploring complex data sets. While PRE results were generally comparable to those from PCA and MCR, PRE is easier to apply. For example, there is no need to determine the number of factors that describe a data set. Finally, we introduce the concept of divided spectrum-PRE (DS-PRE) as a new EDA method. DS-PRE increases the discrimination power of PRE. We also show that DS-PRE can be used to provide the inputs for the k-nearest neighbor (kNN) algorithm. We recommend PRE and DS-PRE as rapid new tools for unsupervised EDA.

The zipped file contains the following: - data (as csv, in the 'data' folder), - R scripts (as Rmd, in the rro folder), - figures (as pdf, in the 'figs' folder), and - presentation (as html, in the root folder).

The high-resolution and mass accuracy of Fourier transform mass spectrometry (FT-MS) has made it an increasingly popular technique for discerning the composition of soil, plant and aquatic samples containing complex mixtures of proteins, carbohydrates, lipids, lignins, hydrocarbons, phytochemicals and other compounds. Thus, there is a growing demand for informatics tools to analyze FT-MS data that will aid investigators seeking to understand the availability of carbon compounds to biotic and abiotic oxidation and to compare fundamental chemical properties of complex samples across groups. We present ftmsRanalysis, an R package which provides an extensive collection of data formatting and processing, filtering, visualization, and sample and group comparison functionalities. The package provides a suite of plotting methods and enables expedient, flexible and interactive visualization of complex datasets through functions which link to a powerful and interactive visualization user interface, Trelliscope. Example analysis using FT-MS data from a soil microbiology study demonstrates the core functionality of the package and highlights the capabilities for producing interactive visualizations.

Context

Coronavirus disease 2019 (COVID-19) time series listing confirmed cases, reported deaths and reported recoveries. Data is disaggregated by country (and sometimes subregion). Coronavirus disease (COVID-19) is caused by the Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) and has had a worldwide effect. On March 11 2020, the World Health Organization (WHO) declared it a pandemic, pointing to the over 118,000 cases of the Coronavirus illness in over 110 countries and territories around the world at the time.

This dataset includes time series data tracking the number of people affected by COVID-19 worldwide, including:

confirmed tested cases of Coronavirus infection the number of people who have reportedly died while sick with Coronavirus the number of people who have reportedly recovered from it

Content

Data is in CSV format and updated daily. It is sourced from this upstream repository maintained by the amazing team at Johns Hopkins University Center for Systems Science and Engineering (CSSE) who have been doing a great public service from an early point by collating data from around the world.

We have cleaned and normalized that data, for example tidying dates and consolidating several files into normalized time series. We have also added some metadata such as column descriptions and data packaged it.

This repository contains material related to the analysis performed in the article "Best Practices for Your Exploratory Factor Analysis: a Factor Tutorial". The material includes the data used in the analyses in .dat format, the labels (.txt) of the variables used in the Factor software, the outputs (.txt) evaluated in the article, and videos (.mp4 with English subtitles) recorded for the purpose of explaining the article. The videos can also be accessed in the following playlist: https://youtube.com/playlist?list=PLln41V0OsLHbSlYcDszn2PoTSiAwV5Oda. Below is a summary of the article: "Exploratory Factor Analysis (EFA) is one of the statistical methods most widely used in Administration, however, its current practice coexists with rules of thumb and heuristics given half a century ago. The purpose of this article is to present the best practices and recent recommendations for a typical EFA in Administration through a practical solution accessible to researchers. In this sense, in addition to discussing current practices versus recommended practices, a tutorial with real data on Factor is illustrated, a software that is still little known in the Administration area, but freeware, easy to use (point and click) and powerful. The step-by-step illustrated in the article, in addition to the discussions raised and an additional example, is also available in the format of tutorial videos. Through the proposed didactic methodology (article-tutorial + video-tutorial), we encourage researchers/methodologists who have mastered a particular technique to do the same. Specifically, about EFA, we hope that the presentation of the Factor software, as a first solution, can transcend the current outdated rules of thumb and heuristics, by making best practices accessible to Administration researchers". STEPS TO REPRODUCE This repository is composed of four types of files: 1) three video files in .mp4 format (with English subtitles), which discuss the article and the extra example mentioned in it; 2) two databases in .dat format: i) 1047 observations with 24 variables of the WHOQOL instrument discussed in the article; and ii) 918 observations with 10 variables of the FWB scale (extra example); 3) two labels files (.txt format) to be incorporated into the Factor software; and 4) five output files in .txt format. The steps are: 1st: Read the article “Best Practices for Your Exploratory Factor Analysis: a Factor Tutorial”. DOI: 10.1590/1982-7849rac2022210085.en; OR 1st: Watch the videos: i) 1_Video_BestPractices.mp4 (https://youtu.be/ITh1w4tFerA); and ii) 2_Video_MultidimensionalExample.mp4 (https://youtu.be/9X77ARoyys0); 2nd: Insert the database WHOQOL_Data.dat into the Factor software and, optionally, the label file WHOQOL_Labels.txt, as explained in section 4.2 of the article or in the section that begins at the timestamp 6:35 of the video 2_Video_MultidimensionalExample.mp4 (https://youtu.be/9X77ARoyys0?t=395); 3rd: Configure the analyses as explained in section 4.3 of the article or in the section that begins at the timestamp 10:45 of the video 2_Video_MultidimensionalExample.mp4 (https://youtu.be/9X77ARoyys0?t=645); 4th: Interpret the first output file (1_Output_WHOQOL_4Factors.txt) as explained in section 4.4 of the article or in the section that begins at the timestamp 20:45 of the video 2_Video_MultidimensionalExample.mp4 (https://youtu.be/9X77ARoyys0?t=1245); 5th: Interpret the second output file (2_Output_WHOQOL_2Factors.txt) as explained in the section that starts at the timestamp 49:53 of the video 2_Video_MultidimensionalExample.mp4 (https://youtu.be/9X77ARoyys0?t=2993); 6th: Interpret the third output file (3_Output_WHOQOL_2Factors_Ajusted.txt) as explained in the section that starts at the timestamp 1:05:45 of the video 2_Video_MultidimensionalExample.mp4 (https://youtu.be/9X77ARoyys0?t=3945); and 7th: Interpret the fourth output file (4_Output_WHOQOL_2Factors_Bifactor.txt) as explained in the section that starts at the timestamp 1:13:14 of the video 2_Video_MultidimensionalExample.mp4 (https://youtu.be/9X77ARoyys0?t=4394); OR, optionally, to replicate the extra example mentioned in the article: 8th: Insert the database FWB_Data.dat into the Factor software and, optionally, the label file FWB_Labels.txt, as explained in the section that starts at the timestamp 4:50 of the video 3_Video_UnidimensionalExample.mp4 (https://youtu.be/wFTGJG8XRRs?t=290); 9th: Configure the analyses as explained in the section that starts at the timestamp 8:32 of the video 3_Video_UnidimensionalExample.mp4 (https://youtu.be/wFTGJG8XRRs?t=512); and 10th: Interpret the output file FWB_Output.txt as explained in the section that begins at the timestamp 22:58 of the video 3_Video_UnidimensionalExample.mp4 (https://youtu.be/wFTGJG8XRRs?t=1378).

Coffee is one of the most popular beverages in the world, however, little information is found regarding the mineral composition of commercial roasted and ground coffees (RG) and its correlation with organic bioactive compounds. 21 commercial Brazilian RG coffee brands - 9 traditional (T) and 12 extra strong (ES) roasted ones - were analyzed for the Cu, Ca, Mn, Mg, K, Zn, and Fe minerals, caffeine, 5-caffeoylquinic acid (5-CQA) and melanoidins contents. For minerals determination by flame atomic absorption spectrometry (FAAS), the samples were decomposed by microwave-assisted wet digestion. Caffeine and 5-CQA were determined by liquid chromatography and melanoidins by molecular absorption spectrometry. The minerals and organic compounds contents association in RG coffee was observed by a principal component analysis. The thermostable compounds (minerals and caffeine) were related to dimension 1 and 2, while 5-CQA and melanoidins were related to dimension 3, allowing for the T coffees segmentation from ES ones.

In the Europe bikes dataset, Extract the insight into sales in each country and each state of their countries using Excel.

The average American’s diet does not align with the Dietary Guidelines for Americans (DGA) provided by the U.S. Department of Agriculture and the U.S. Department of Health and Human Services (2020). The present study aimed to compare fruit and vegetable consumption among those who had and had not heard of the DGA, identify characteristics of DGA users, and identify barriers to DGA use. A nationwide survey of 943 Americans revealed that those who had heard of the DGA ate more fruits and vegetables than those who had not. Men, African Americans, and those who have more education had greater odds of using the DGA as a guide when preparing meals relative to their respective counterparts. Disinterest, effort, and time were among the most cited reasons for not using the DGA. Future research should examine how to increase DGA adherence among those unaware of or who do not use the DGA. Comparative analyses of fruit and vegetable consumption among those who were aware/unaware and use/do not use the DGA were completed using independent samples t tests. Fruit and vegetable consumption variables were log-transformed for analysis. Binary logistic regression was used to examine whether demographic features (race, gender, and age) predict DGA awareness and usage. Data were analyzed using SPSS version 28.1 and SAS/STAT® version 9.4 TS1M7 (2023 SAS Institute Inc).

This folder contains the files used in the ASL analyses of my study: All of the data and calculations for my primary analysis, my exploratory analyses (except the one using a video from The Daily Moth, which can be found in a separate folder), and the ASL portions of my secondary analysis. As described in my dissertation, I am not sharing the original video files in order to protect the privacy of those who participated in my study.Each file is shared in one or more of the formats listed below, as appropriate:PDF.csv files (one file for each sheet)Link to my Google Sheets file

The Iris Dataset. ¶. This data sets consists of 3 different types of irises’ (Setosa, Versicolour, and Virginica) petal and sepal length, stored in a 150x4 numpy.ndarray. The rows being the samples and the columns being: Sepal Length, Sepal Width, Petal Length and Petal Width.

Context and Methodology

Research Domain:
The dataset is part of a project focused on retail sales forecasting. Specifically, it is designed to predict daily sales for Rossmann, a chain of over 3,000 drug stores operating across seven European countries. The project falls under the broader domain of time series analysis and machine learning applications for business optimization. The goal is to apply machine learning techniques to forecast future sales based on historical data, which includes factors like promotions, competition, holidays, and seasonal trends.

Purpose:
The primary purpose of this dataset is to help Rossmann store managers predict daily sales for up to six weeks in advance. By making accurate sales predictions, Rossmann can improve inventory management, staffing decisions, and promotional strategies. This dataset serves as a training set for machine learning models aimed at reducing forecasting errors and supporting decision-making processes across the company’s large network of stores.

How the Dataset Was Created:
The dataset was compiled from several sources, including historical sales data from Rossmann stores, promotional calendars, holiday schedules, and external factors such as competition. The data is split into multiple features, such as the store's location, promotion details, whether the store was open or closed, and weather information. The dataset is publicly available on platforms like Kaggle and was initially created for the Kaggle Rossmann Store Sales competition. The data is made accessible via an API for further analysis and modeling, and it is structured to help machine learning models predict future sales based on various input variables.

Technical Details

Dataset Structure:

The dataset consists of three main files, each with its specific role:

1. Train:
   This file contains the historical sales data, which is used to train machine learning models. It includes daily sales information for each store, as well as various features that could influence the sales (e.g., promotions, holidays, store type, etc.).

   https://handle.test.datacite.org/10.82556/yb6j-jw41
   PID: b1c59499-9c6e-42c2-af8f-840181e809db

2. Test2:
   The test dataset mirrors the structure of train.csv but does not include the actual sales values (i.e., the target variable). This file is used for making predictions using the trained machine learning models. It is used to evaluate the accuracy of predictions when the true sales data is unknown.

   https://handle.test.datacite.org/10.82556/jerg-4b84
   PID: 7cbb845c-21dd-4b60-b990-afa8754a0dd9

3. Store:
   This file provides metadata about each store, including information such as the store’s location, type, and assortment level. This data is essential for understanding the context in which the sales data is gathered.

   https://handle.test.datacite.org/10.82556/nqeg-gy34
   PID: 9627ec46-4ee6-4969-b14a-bda555fe34db

Data Fields Description:

• Id: A unique identifier for each (Store, Date) combination within the test set.

• Store: A unique identifier for each store.

• Sales: The daily turnover (target variable) for each store on a specific day (this is what you are predicting).

• Customers: The number of customers visiting the store on a given day.

• Open: An indicator of whether the store was open (1 = open, 0 = closed).

• StateHoliday: Indicates if the day is a state holiday, with values like:

  • 'a' = public holiday,

  • 'b' = Easter holiday,

  • 'c' = Christmas,

  • '0' = no holiday.

• SchoolHoliday: Indicates whether the store is affected by school closures (1 = yes, 0 = no).

• StoreType: Differentiates between four types of stores: 'a', 'b', 'c', 'd'.

• Assortment: Describes the level of product assortment in the store:

  • 'a' = basic,

  • 'b' = extra,

  • 'c' = extended.

• CompetitionDistance: Distance (in meters) to the nearest competitor store.

• CompetitionOpenSince[Month/Year]: The month and year when the nearest competitor store opened.

• Promo: Indicates whether the store is running a promotion on a particular day (1 = yes, 0 = no).

• Promo2: Indicates whether the store is participating in Promo2, a continuing promotion for some stores (1 = participating, 0 = not participating).

• Promo2Since[Year/Week]: The year and calendar week when the store started participating in Promo2.

• PromoInterval: Describes the months when Promo2 is active, e.g., "Feb,May,Aug,Nov" means the promotion starts in February, May, August, and November.

Software Requirements

To work with this dataset, you will need to have specific software installed, including:

• DBRepo Authorization: This is required to access the datasets via the DBRepo API. You may need to authenticate with an API key or login credentials to retrieve the datasets.

• Python Libraries: Key libraries for working with the dataset include:

  • pandas for data manipulation,

  • numpy for numerical operations,

  • matplotlib and seaborn for data visualization,

  • scikit-learn for machine learning algorithms.

Additional Resources

Several additional resources are available for working with the dataset:

1. Presentation:
   A presentation summarizing the exploratory data analysis (EDA), feature engineering process, and key insights from the analysis is provided. This presentation also includes visualizations that help in understanding the dataset’s trends and relationships.

2. Jupyter Notebook:
   A Jupyter notebook, titled Retail_Sales_Prediction_Capstone_Project.ipynb, is provided, which details the entire machine learning pipeline, from data loading and cleaning to model training and evaluation.

3. Model Evaluation Results:
   The project includes a detailed evaluation of various machine learning models, including their performance metrics like training and testing scores, Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). This allows for a comparison of model effectiveness in forecasting sales.

4. Trained Models (.pkl files):
   The models trained during the project are saved as .pkl files. These files contain the trained machine learning models (e.g., Random Forest, Linear Regression, etc.) that can be loaded and used to make predictions without retraining the models from scratch.

5. sample_submission.csv:
   This file is a sample submission file that demonstrates the format of predictions expected when using the trained model. The sample_submission.csv contains predictions made on the test dataset using the trained Random Forest model. It provides an example of how the output should be structured for submission.

These resources provide a comprehensive guide to implementing and analyzing the sales forecasting model, helping you understand the data, methods, and results in greater detail.