Analysis and Prediction of Suicide Attempts
Abstract
Suicide is increasingly becoming a serious concern for society. In fact, it is one of the largest cause of deaths in today’s world. Hence it is necessary to stop this menace by developing accurate prediction systems based on available data. The paper primarily analysis the suicide data, identify significant attributes contributing towards suicide attempt and predict future such attempts with significant precision. A comparison between 2 machine learning algorithms: - Decision Tee, and Naïve Bayes for suicide prediction has been made here. The scope of this research is to understand the effectiveness of these algorithms for preventing future suicides.
Keywords: Data Analysis, Machine Learning, Data Prediction, Logistic Regression, Random Forest, Naïve Bayes.
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Block Diagram
Specifications
H/W System Configuration:
- Processor : I3/Intel Processor
- RAM : 4GB (min)
- Hard Disk: 160GB
- Key Board : Standard Windows Keyboard
- Mouse :Two or Three Button Mouse
- Monitor: SVGA
- Operating System : Windows 10
- Server side Script: Python
- IDE : Google colab or Jupyter notebookter Notebook
Learning Outcomes
LEARNING OUTCOMES:
· What is Feature Concept?
· Preprocessing techniques.
· Data correlation.
· Scope of Real-Time Application Scenarios.
· How the Internet Works.
· What is a search engine and how the browser works
· What type of technology versions are used.
· Use of HTML, CSS on UI Designs.
· Data Parsing Front-End to Back-End.
· Working Procedure.
· Introduction to basic technologies used for.
· How the project works.
· Input and Output modules.
· Framework use.
· Data sets properties.
· Machine learning algorithms.
· Data preprocessing methods.
· Principle component analysis.
· Project development skills:
o Problem analyzing skills.
o Problem solving skills.
o Creativity and imaginary skills.
o Programming skills.
o Deployment.
o Testing skills.
o Debugging skills.
o Project presentation skills.
o Thesis writing skills.
Paper Publishing
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