1. About the Dataset:

Description: Dive into the world of college placements with this dataset designed to unravel the factors influencing student placement outcomes. The dataset comprises crucial parameters such as IQ scores, CGPA (Cumulative Grade Point Average), and placement status. Aspiring data scientists, researchers, and enthusiasts can leverage this dataset to uncover patterns and insights that contribute to a deeper understanding of successful college placements.

2. Projects Ideas:

Project Idea 1: Predictive Modeling for College Placements Utilize machine learning algorithms to build a predictive model that forecasts a student's likelihood of placement based on their IQ scores and CGPA. Evaluate and compare the effectiveness of different algorithms to enhance prediction accuracy.

Project Idea 2: Feature Importance Analysis Conduct a feature importance analysis to identify the key factors that significantly influence placement outcomes. Gain insights into whether IQ, CGPA, or a combination of both plays a more dominant role in determining success.

Project Idea 3: Clustering Analysis of Placement Trends Apply clustering techniques to group students based on their placement outcomes. Explore whether distinct clusters emerge, shedding light on common characteristics or trends among students who secure placements.

Project Idea 4: Correlation Analysis with External Factors Investigate the correlation between the provided data (IQ, CGPA, placement) and external factors such as internship experience, extracurricular activities, or industry demand. Assess how these external factors may complement or influence placement success.

Project Idea 5: Visualization of Placement Dynamics Over Time Create dynamic visualizations to illustrate how placement trends evolve over time. Analyze trends, patterns, and fluctuations in placement rates to identify potential cyclical or seasonal influences on student placements.

3. Columns Explanation:

• IQ:

  • Definition: Intelligence Quotient, a measure of a person's intellectual abilities.
  • Data Type: Numeric
  • Range: Typically, IQ scores range from 70 to 130, with 100 being the average.

• CGPA:

  • Definition: Cumulative Grade Point Average, a measure of a student's overall academic performance.
  • Data Type: Numeric
  • Range: Typically, CGPA is on a scale of 0 to 4, with 4 being the highest possible score.

• Placement:

  • Definition: Binary variable indicating whether a student secured a placement (1) or not (0).
  • Data Type: Categorical (Binary)
  • Values: 1 (Placement secured) or 0 (No placement).

These columns collectively provide a comprehensive snapshot of a student's intellectual abilities, academic performance, and their success in securing a placement. Analyzing this dataset can offer valuable insights into the dynamics of college placements and inform strategies for optimizing student outcomes.

Context

Information on more than 180,000 Terrorist Attacks

The Global Terrorism Database (GTD) is an open-source database including information on terrorist attacks around the world from 1970 through 2017. The GTD includes systematic data on domestic as well as international terrorist incidents that have occurred during this time period and now includes more than 180,000 attacks. The database is maintained by researchers at the National Consortium for the Study of Terrorism and Responses to Terrorism (START), headquartered at the University of Maryland. [More Information][1]

Content

Geography: Worldwide

Time period: 1970-2017, except 1993

Unit of analysis: Attack

Variables: >100 variables on location, tactics, perpetrators, targets, and outcomes

Sources: Unclassified media articles (Note: Please interpret changes over time with caution. Global patterns are driven by diverse trends in particular regions, and data collection is influenced by fluctuations in access to media coverage over both time and place.)

Definition of terrorism:

"The threatened or actual use of illegal force and violence by a non-state actor to attain a political, economic, religious, or social goal through fear, coercion, or intimidation."

See the [GTD Codebook][2] for important details on data collection methodology, definitions, and coding schema.

Acknowledgements

The Global Terrorism Database is funded through START, by the US Department of State (Contract Number: SAQMMA12M1292) and the US Department of Homeland Security Science and Technology Directorate’s Office of University Programs (Award Number 2012-ST-061-CS0001, CSTAB 3.1). The coding decisions and classifications contained in the database are determined independently by START researchers and should not be interpreted as necessarily representing the official views or policies of the United States Government.

[GTD Team][3]

Publications

The GTD has been leveraged extensively in [scholarly publications][4], [reports][5], and [media articles][6]. [Putting Terrorism in Context: Lessons from the Global Terrorism Database][7], by GTD principal investigators LaFree, Dugan, and Miller investigates patterns of terrorism and provides perspective on the challenges of data collection and analysis. The GTD's data collection manager, Michael Jensen, discusses important [Benefits and Drawbacks of Methodological Advancements in Data Collection and Coding][8].

Terms of Use

Use of the data signifies your agreement to the following [terms and conditions][9].

END USER LICENSE AGREEMENT WITH UNIVERSITY OF MARYLAND

IMPORTANT – THIS IS A LEGAL AGREEMENT BETWEEN YOU ("You") AND THE UNIVERSITY OF MARYLAND, a public agency and instrumentality of the State of Maryland, by and through the National Consortium for the Study of Terrorism and Responses to Terrorism (“START,” “US,” “WE” or “University”). PLEASE READ THIS END USER LICENSE AGREEMENT (“EULA”) BEFORE ACCESSING THE Global Terrorism Database (“GTD”). THE TERMS OF THIS EULA GOVERN YOUR ACCESS TO AND USE OF THE GTD WEBSITE, THE DATA, THE CODEBOOK, AND ANY AUXILIARY MATERIALS. BY ACCESSING THE GTD, YOU SIGNIFY THAT YOU HAVE READ, UNDERSTAND, ACCEPT, AND AGREE TO ABIDE BY THESE TERMS AND CONDITIONS. IF YOU DO NOT ACCEPT THE TERMS OF THIS EULA, DO NOT ACCESS THE GTD.

TERMS AND CONDITIONS

1. GTD means Global Terrorism Database data and the online user interface (www.start.umd.edu/gtd) produced and maintained by the National Consortium for the Study of Terrorism and Responses to Terrorism (START). This includes the data and codebook, any auxiliary materials present, and the user interface by which the data are presented.

2. LICENSE GRANT. University hereby grants You a revocable, non-exclusive, non-transferable right and license to access the GTD and use the data, the codebook, and any auxiliary materials solely for non-commercial research and analysis.

3. RESTRICTIONS. You agree to NOT: a. publicly post or display the data, the codebook, or any auxiliary materials without express written permission by University of Maryland (this excludes publication of analysis or visualization of the data for non-commercial purposes); b. sell, license, sublicense, or otherwise distribute the data, the codebook, or any auxiliary materials to third parties for cash or other considerations; c. modify, hide, delete or interfere with any notices that are included on the GTD or the codebook, or any auxiliary materials; d. use the GTD to draw conclusions about the official legal status or criminal record of an individual, or the status of a criminal or civil investigation; e. interfere with or disrupt the GTD website or servers and networks connected to the GTD website; or f. use robots, spiders, crawlers, automated devices and similar technologies to screen-scrape the site or to engage in data aggregation or indexing of the da...

Abstract Anaerobic biological treatment can comprise a viable route of CH4 production through the energy recovery of organic solid waste (OSW). This work therefore presented a bibliometric analysis of research trends on the theme "Applicability of biological treatment via Anaerobic Digestion in Organic Solid Waste", considering experimental articles published from 2018 to 2022 in the Web of Science database™; the analysis used VoSviewer software to define research trends, and found that the main terms addressed in the mapped scientific articles were anaerobic, waste, sludge, waste food, municipal waste, anaerobic co-digestion, sewage sludge, organic fraction, co-digestion and biogas. As a product of such mapping, an Interaction Network Diagram was constructed comprising the main terms in addition to a theoretical foundation on anaerobic digestion and biochemical and microbiological aspects about the process.