https://github.com/adilshamim8/placement_project_logistic_regression
The "Placement Project Logistic Regression" predicts student placement outcomes based on CGPA, IQ, and placement status using logistic regression to analyze their influence on employability.
https://github.com/adilshamim8/placement_project_logistic_regression
Last synced: 2 months ago
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The "Placement Project Logistic Regression" predicts student placement outcomes based on CGPA, IQ, and placement status using logistic regression to analyze their influence on employability.
- Host: GitHub
- URL: https://github.com/adilshamim8/placement_project_logistic_regression
- Owner: AdilShamim8
- Created: 2024-10-30T12:12:18.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-10-30T12:17:36.000Z (7 months ago)
- Last Synced: 2024-10-30T13:23:20.955Z (7 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 70.3 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Placement Project Logistic Regression
## Description
The "Placement Project Logistic Regression" focuses on predicting student placement outcomes based solely on three key features: CGPA, IQ, and placement status. This project applies logistic regression, a statistical method used for binary classification, to analyze how academic performance and cognitive ability can influence employability in the job market.
### Key Features
- **CGPA (Cumulative Grade Point Average)**: This feature represents a student’s overall academic performance. It is a significant indicator of a student's understanding of their field and can influence hiring decisions.
- **IQ (Intelligence Quotient)**: IQ serves as a measure of cognitive ability and potential. It suggests how well a student can understand and apply information, which is crucial in problem-solving and adaptability in professional environments.
- **Placement Status**: The target variable indicating whether a student was successfully placed in a job (1) or not placed (0). This binary outcome is what the model aims to predict based on the input features.
### Objective
The aim of this project is to analyze the relationship between CGPA, IQ, and placement status to develop a predictive model. By understanding how these two factors contribute to placement success, stakeholders such as educational institutions and students can leverage insights for improved outcomes in job placement efforts.
### Significance
By incorporating data analysis and machine learning, this project not only provides valuable predictions but also offers insights into the academic and cognitive traits that are most linked to successful job placements, thereby informing curriculum development and student support services.