Looking for a free Walmart product dataset? The Walmart Products Free Dataset delivers a ready-to-use ecommerce product data CSV containing ~2,100 verified product records from Walmart.com. It includes vital details like product titles, prices, categories, brand info, availability, and descriptions — perfect for data analysis, price comparison, market research, or building machine-learning models.

Key Features

Complete Product Metadata: Each entry includes URL, title, brand, SKU, price, currency, description, availability, delivery method, average rating, total ratings, image links, unique ID, and timestamp.

CSV Format, Ready to Use: Download instantly - no need for scraping, cleaning or formatting.

Good for E-commerce Research & ML: Ideal for product cataloging, price tracking, demand forecasting, recommendation systems, or data-driven projects.

Free & Easy Access: Priced at USD $0.0, making it a great starting point for developers, data analysts or students.

Who Benefits?

• Data analysts & researchers exploring e-commerce trends or product catalog data.
• Developers & data scientists building price-comparison tools, recommendation engines or ML models.
• E-commerce strategists/marketers need product metadata for competitive analysis or market research.
• Students/hobbyists needing a free dataset for learning or demo projects.

Why Use This Dataset Instead of Manual Scraping?

• Time-saving: No need to write scrapers or deal with rate limits.
• Clean, structured data: All records are verified and already formatted in CSV, saving hours of cleaning.
• Risk-free: Avoid Terms-of-Service issues or IP blocks that come with manual scraping.
  Instant access: Free and immediately downloadable.

To collect feedback on their learning environment from families, students and teachers. Aids in facilitating the understanding of families perceptions, students, and teachers regarding their school. School leaders use feedback from the survey to reflect and make improvements to schools and programs. Each year all parents, teachers and students in grades 6-12 take the NYC School Survey. The survey is aligned to the DOE's Framework for Great Schools. It is designed to collect important information about each school's ability to support student success.

A little paragraph from one real dataset, with a few little changes to protect students' private information. Permissions are given.

Goals

You are going to help teachers with only the data: 1. Prediction: To tell what makes a brilliant student who can apply for a graduate school, whether abroad or not. 2. Application: To help those who fails to apply for a graduate school with advice in job searching.

Tips

1. Educational data may have subtle structures, hierarchies and heterogeneity are probably involved. Simple regressions can hardly make any difference. Also, you should keep an eye on the collinearity in some indicators collected by teachers who have already forgot statistics.
2. Not all students are free to choose to apply for a graduate school, but some were born with privileges.
3. Some of the students are trying (or planning to try) to apply for a graduate school for years, you should be responsible to give advice accurately under their circumstances

About the Data

Some of the original structure are deleted or censored. For those are left: Basic data like: - ID - class: categorical, initially students were divided into 2 classes, yet teachers suspect that of different classes students may performance significant differently. - gender - race: categorical and censored - GPA: real numbers, float

Some teachers assume that scores of math curriculums can represent one's likelihood perfectly: - Algebra: real numbers, Advanced Algebra - ......

Some assume that background of students can affect their choices and likelihood significantly, which are all censored as: - from1: students' home locations - from2: a probably bad indicator for preference on mathematics - from 3: how did students apply for this university (undergraduate) - from4: a probably bad indicator for family background. 0 with more wealth, 4 with more poverty

The final indicator y: - 0, one fails to apply for the graduate school, who may apply again or search jobs in the future - 1, success, inland - 2, success, abroad

The dataset includes: Roll Number: Represent the roll number of the student.

Gender: Useful for analyzing performance differences between male and female students.

Race/Ethnicity: Allows analysis of academic performance trends across different racial or ethnic groups.

Parental Level of Education: Indicates the educational background of the student's family.

Lunch: Shows whether students receive a free or reduced lunch, which is often a socioeconomic indicator.

Test Preparation Course: This tells whether students completed a test prep course, which could impact their performance.

Math Score: Provides a measure of each student’s performance in math, used to calculate averages or trends across various demographics. Science Score: Evaluates students' Science knowledge, which can be analyzed to assess overall scentific knowledge of the student.

Reading Score: Measures performance in reading, allowing for insights into literacy and comprehension levels among students.

Writing Score: Evaluates students' writing skills, which can be analyzed to assess overall literacy and expression.

Total Score: Shows the total number achieved by the student out of 400.

Grade: Gade achieved by the student. "A" grade if Total marks >= 320, "B" grade if Total marks >= 250, "C" grade if Total marks >= 200, "D" grade if Total marks >= 150 and Fail if <150.

This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful (see paper source for more details).

Attributes for both Maths.csv (Math course) and Portuguese.csv (Portuguese language course) datasets:

These grades are related with the course subject, Math or Portuguese:

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This dataset includes the attendance rate for public school students PK-12 by student group and by district during the 2021-2022 school year. Student groups include: Students experiencing homelessness Students with disabilities Students who qualify for free/reduced lunch English learners All high needs students Non-high needs students Students by race/ethnicity (Hispanic/Latino of any race, Black or African American, White, All other races) Attendance rates are provided for each student group by district and for the state. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

Dataset Name: Fictional Student Performance Dataset

Description: The "Fictional Student Performance Dataset" is a comprehensive collection of fictional student records designed for educational and analytical purposes. This dataset comprises 500 student profiles and their associated attributes, making it a valuable resource for exploring various aspects of student performance and data analysis.

Attributes:

StudentID: A unique identifier for each student, facilitating individual tracking and analysis. Name: The name of each student, ensuring the dataset's personalization. Age: The age of each student, providing demographic information. Gender: The gender of each student, offering insights into gender-based performance trends. Grade: A continuous variable representing the academic performance of students, which can be used for regression and prediction tasks. Attendance: A percentage value denoting the attendance rate of each student, enabling attendance-related analyses. FinalExamScore: A continuous variable indicating the final exam score achieved by each student, making it suitable for evaluation and prediction tasks. Use Cases:

Educational Research: This dataset is ideal for educational institutions and researchers to analyze student performance and identify factors that influence academic outcomes. Machine Learning Practice: It is an excellent resource for data science enthusiasts and students looking to practice various machine learning techniques, such as regression, classification, and clustering. Predictive Modeling: The "Grade" and "FinalExamScore" attributes can be used to develop predictive models to forecast student performance. Gender-Based Analysis: Explore gender-based trends in student performance and attendance. Attendance Impact: Investigate the correlation between attendance and academic success. Disclaimer: Please note that this dataset is entirely fictional and created for educational and practice purposes. Any resemblance to real individuals or institutions is purely coincidental.

Citation: If you use this dataset in your research or projects, kindly acknowledge its source as the "Fictional Student Performance Dataset"

Data Generation: The dataset was generated using a combination of randomization and scripting to ensure that it does not contain any real or personally identifiable information.

Feel free to explore and utilize this dataset for educational purposes, data analysis, or machine learning exercises. It is intended to foster learning and experimentation in data science.

The Federal School Code List contains the unique codes assigned by the Department of Education for schools participating in the Title IV federal student aid programs. Students can enter these codes on the Free Application for Federal Student Aid (FAFSA) to indicate which postsecondary schools they want to receive their financial application results. The Federal School Code List is a searchable document in Excel format. The list will be updated on the first of February, May, August, and November of each calendar year.

Predict students' dropout and academic success

Investigating the Impact of Social and Economic Factors

By [source]

About this dataset

This dataset provides a comprehensive view of students enrolled in various undergraduate degrees offered at a higher education institution. It includes demographic data, social-economic factors and academic performance information that can be used to analyze the possible predictors of student dropout and academic success. This dataset contains multiple disjoint databases consisting of relevant information available at the time of enrollment, such as application mode, marital status, course chosen and more. Additionally, this data can be used to estimate overall student performance at the end of each semester by assessing curricular units credited/enrolled/evaluated/approved as well as their respective grades. Finally, we have unemployment rate, inflation rate and GDP from the region which can help us further understand how economic factors play into student dropout rates or academic success outcomes. This powerful analysis tool will provide valuable insight into what motivates students to stay in school or abandon their studies for a wide range of disciplines such as agronomy, design, education nursing journalism management social service or technologies

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How to use the dataset

This dataset can be used to understand and predict student dropouts and academic outcomes. The data includes a variety of demographic, social-economic and academic performance factors related to the students enrolled in higher education institutions. The dataset provides valuable insights into the factors that affect student success and could be used to guide interventions and policies related to student retention.

Using this dataset, researchers can investigate two key questions: - which specific predictive factors are linked with student dropout or completion? - how do different features interact with each other? For example, researchers could explore if there any demographic characteristics (e.g., gender, age at enrollment etc.) or immersion conditions (e.g., unemployment rate in region) are associated with higher student success rates, as well as understand what implications poverty has for educational outcomes. By answering these questions, research insight is generated which can provide critical information for administrators on formulating strategies that promote successful degree completion among students from diverse backgrounds in their institutions.

In order to use this dataset effectively it is important that scientists familiarize themselves with all variables provided in the dataset including categorical (qualitative) variables such as gender or application mode; numerical variables such as number of curricular units at the beginning of semesters or age at enrollment; ordinal data measurement type variables such as marital status; studied trends over time such as inflation rate or GDP; frequency measurements variables like percentage of scholarship holders; etc.. Additionally scientists should make sure they aware off all potential bias included in the data prior running analysis–for example understanding if one population is underrepresented compared another -as this phenomenon could lead unexpected results if not taken into consideration while conducting research undertaken using this data set.. Finally it would be important for practitioners realize that this current Kaggle Dataset contains only one semester-worth information on each admission intake whereas additional studies conducted for a longer time period might be able provide more accurate results related selected topic area due further deterioration retention achievement coefficients obtained from those gradually accurate experiments unfolding different year-long admissions seasons

Research Ideas

• Prediction of Student Retention: This dataset can be used to develop predictive models that can identify student risk factors for dropout and take early interventions to improve student retention rate.
• Improved Academic Performance: By using this data, higher education institutions could better understand their students' academic progress and identify areas of improvement from both an individual and institutional perspective. This will enable them to develop targeted courses, activities, or initiatives that enhance academic performance more effectively and efficiently.
• Accessibility Assistance: Using the demographic information included in the dataset, institutions could develop s...

Full Description Eligibility indicates students from families whose total income is at or below 185 percent of the poverty level. Household income below 130 percent of the poverty level qualifies students for free meals. Household income between 130 and 185 percent of the poverty level qualifies students for reduced-price meals. Connecticut State Department of Education collects data for grades PreK through 12 on a school year basis. CTdata.org carries annual school year data for grades K through 3.

The dataset provides a collection of 160 instances belonging to two classes (pass' = 136 andfail' = 24). The data is an anonymised, statistically sound and reliable representation of the original data collected from students studying computer science modules at a UK University. Each instance is made up of 19 features plus the class label. Eight of the features represent students' online behaviour including bio information retrieved from Virtual Learning Environment. Eleven of the features represent students' neighbourhood influence retrieved from Office for Students database. The data has been compiled and made available in de-facto/de-jure standard open formats (CSV and JSON).

This data was collected and used in a research study undertaken by academics and researchers at Computer Science Department, Edge Hill University, United Kingdom. To encourage reproducibility of the experiments and results reported, the data is provided in the exact training-validation-testing splits used in the experiments.

The experiences of first-generation college students (FGCS) can guide the development of effective practices for supporting and retaining this population in higher education settings. Multiple themes emerged via qualitative interviews with ten FCGS participants, including: challenges/barriers within instruction/classroom communication, financial struggles, academic strategies, and perseverance/motivations related to family and academics. Findings show needs for clear communication/expectations within higher education settings, social supports/relationships outside of the campus settings, as well as acknowledgment and reinforcement for academic successes. Additionally, these findings align with previous research showing FGCS to be underprepared and under-supported in applying for, enrolling in, and paying for college.

In order to request access to this data please complete the data request form.* * University of Bristol staff should use this form instead. The PHIRST research team has worked in partnership with Hammersmith and Fulham colleagues from public health, children and adult services, to create an evaluation study that takes into account the priorities and concerns of all interested parties within the borough. It focuses on the following research questions: 1) Is UFSM feasible in secondary schools? 2) What is the impact of UFSM on student hunger, school attendance and behaviour, and food that is eaten in school? 3) What is the impact of UFSM on family finance and food security? 4) What do students, carers and school staff see as the reasons UFSM leads to these outcomes? 5) What are the things that help or prevent UFSM being delivered effectively in secondary schools? 6) Could UFSM in secondary schools be a cost-effective approach to addressing student hunger? We interviewed students, parents/carers, school staff and catering staff from the two schools receiving UFSM, and senior leaders in eight other secondary schools, ii) ran student surveys in the two UFSM schools and in two comparison schools, and iii) looked at information about student attendance, academic work and behaviour collected by the local authority and by schools before and after UFSM was introduced. We also worked with a group of student co-researchers in both UFSM schools. They advised on the content and format of our interviews and survey and helped us to plan observations of their school lunch times. These students did the observations themselves and shared their findings with the study team.

This repository was created for my Master's thesis in Computational Intelligence and Internet of Things at the University of Córdoba, Spain. The purpose of this repository is to store the datasets found that were used in some of the studies that served as research material for this Master's thesis. Also, the datasets used in the experimental part of this work are included.

Below are the datasets specified, along with the details of their references, authors, and download sources.

----------- STS-Gold Dataset ----------------

The dataset consists of 2026 tweets. The file consists of 3 columns: id, polarity, and tweet. The three columns denote the unique id, polarity index of the text and the tweet text respectively.

Reference: Saif, H., Fernandez, M., He, Y., & Alani, H. (2013). Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold.

File name: sts_gold_tweet.csv

----------- Amazon Sales Dataset ----------------

This dataset is having the data of 1K+ Amazon Product's Ratings and Reviews as per their details listed on the official website of Amazon. The data was scraped in the month of January 2023 from the Official Website of Amazon.

Owner: Karkavelraja J., Postgraduate student at Puducherry Technological University (Puducherry, Puducherry, India)

Features:

• product_id - Product ID
• product_name - Name of the Product
• category - Category of the Product
• discounted_price - Discounted Price of the Product
• actual_price - Actual Price of the Product
• discount_percentage - Percentage of Discount for the Product
• rating - Rating of the Product
• rating_count - Number of people who voted for the Amazon rating
• about_product - Description about the Product
• user_id - ID of the user who wrote review for the Product
• user_name - Name of the user who wrote review for the Product
• review_id - ID of the user review
• review_title - Short review
• review_content - Long review
• img_link - Image Link of the Product
• product_link - Official Website Link of the Product

License: CC BY-NC-SA 4.0

File name: amazon.csv

----------- Rotten Tomatoes Reviews Dataset ----------------

This rating inference dataset is a sentiment classification dataset, containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. On average, these reviews consist of 21 words. The first 5331 rows contains only negative samples and the last 5331 rows contain only positive samples, thus the data should be shuffled before usage.

This data is collected from https://www.cs.cornell.edu/people/pabo/movie-review-data/ as a txt file and converted into a csv file. The file consists of 2 columns: reviews and labels (1 for fresh (good) and 0 for rotten (bad)).

Reference: Bo Pang and Lillian Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pages 115–124, Ann Arbor, Michigan, June 2005. Association for Computational Linguistics

File name: data_rt.csv

----------- Preprocessed Dataset Sentiment Analysis ----------------

Preprocessed amazon product review data of Gen3EcoDot (Alexa) scrapped entirely from amazon.in
Stemmed and lemmatized using nltk.
Sentiment labels are generated using TextBlob polarity scores.

The file consists of 4 columns: index, review (stemmed and lemmatized review using nltk), polarity (score) and division (categorical label generated using polarity score).

DOI: 10.34740/kaggle/dsv/3877817

Citation: @misc{pradeesh arumadi_2022, title={Preprocessed Dataset Sentiment Analysis}, url={https://www.kaggle.com/dsv/3877817}, DOI={10.34740/KAGGLE/DSV/3877817}, publisher={Kaggle}, author={Pradeesh Arumadi}, year={2022} }

This dataset was used in the experimental phase of my research.

File name: EcoPreprocessed.csv

----------- Amazon Earphones Reviews ----------------

This dataset consists of a 9930 Amazon reviews, star ratings, for 10 latest (as of mid-2019) bluetooth earphone devices for learning how to train Machine for sentiment analysis.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 5 columns: ReviewTitle, ReviewBody, ReviewStar, Product and division (manually added - categorical label generated using ReviewStar score)

License: U.S. Government Works

Source: www.amazon.in

File name (original): AllProductReviews.csv (contains 14337 reviews)

File name (edited - used for my research) : AllProductReviews2.csv (contains 9930 reviews)

----------- Amazon Musical Instruments Reviews ----------------

This dataset contains 7137 comments/reviews of different musical instruments coming from Amazon.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 10 columns: reviewerID, asin (ID of the product), reviewerName, helpful (helpfulness rating of the review), reviewText, overall (rating of the product), summary (summary of the review), unixReviewTime (time of the review - unix time), reviewTime (time of the review (raw) and division (manually added - categorical label generated using overall score).

Source: http://jmcauley.ucsd.edu/data/amazon/

File name (original): Musical_instruments_reviews.csv (contains 10261 reviews)

File name (edited - used for my research) : Musical_instruments_reviews2.csv (contains 7137 reviews)

Full Description The variable examined is graduation status after four years of high school. Early and summer graduates are considered graduates after four years. The "other" rate includes students who dropped out of high school, enrolled in a GED program, transferred to post-secondary education, or have unknown status. Special education students in school after four years but subsequently graduated are not included in the "still enrolled" rate due to Individuals with Disabilities Education Act (IDEA) restrictions. The subgroups reported are gender, race/ethnicity, English language learners, special education students, and students eligible for free or reduced-price meals (FRPM). The data replace the rate of students enrolled in 12th grade in September who graduated the following June. Connecticut State Department of Education (SDE) collects data longitudinally by four-year cohorts. SDE reports and CTdata.org carries graduation rates of four-year cohorts annually.

We'll tailor a Udemy dataset to meet your unique needs, encompassing course titles, user engagement metrics, completion rates, demographic data of learners, enrollment numbers, review scores, and other pertinent metrics.

Leverage our Udemy datasets for diverse applications to bolster strategic planning and market analysis. Scrutinizing these datasets enables organizations to grasp learner preferences and online education trends, facilitating nuanced educational program development and learning initiatives. Customize your access to the entire dataset or specific subsets as per your business requisites.

Popular use cases involve optimizing educational content based on engagement insights, enhancing learning strategies through targeted learner segmentation, and identifying and forecasting trends to stay ahead in the online education landscape.

Problem Statement:

This project understands how the student's performance (test scores) is affected by other variables such as Gender, Ethnicity, Parental level of education, Lunch and Test preparation course.

Content

This data set consists of the marks secured by the students in various subjects. - gender : sex of students -> (Male/female) - race/ethnicity : ethnicity of students -> (Group A, B,C, D,E) - parental level of education : parents' final education ->(bachelor's degree,some college,master's degree,associate's degree,- high school) - lunch : having lunch before test (standard or free/reduced) - test preparation course : complete or not complete before test - math score - reading score - writing score

Inspiration:

To understand the influence of the parent's background, test preparation etc on students' performance

Increase the percentage of students covered under a 24/7 tobacco-free school policy from 74% in 2012 to 86% by 2018.

This dataset includes the attendance rate for public school students PK-12 by district during the 2020-2021 school year. Attendance rates are provided for each district for the overall student population and for the high needs student population. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

This dataset examines the opinions of undergraduate students in Hangzhou, China, who are studying management, on the use of Artificial Intelligence (AI) in education. The data set is guided by the Diffusion of Innovations Theory (DOI) and explores the factors that influence students' intentions to use AI technologies. The survey was conducted using a random sampling method to ensure comprehensive data collection from 420 Chinese students aged 18-21. The methodology used in this survey was rigorous.