This project involves an exploratory data analysis (EDA) of campus placement data from a college. The dataset includes information on various factors such as academic performance, gender, work experience, and more, aiming to identify patterns that influence on-campus placements.
- Notebook File: analysis.ipynb
- Dataset: data.csv
Based on the plots derived and other statistical methods, the following conclusions were made.
-
Gender Influence:
- Male students have a slightly higher chance of getting placed than female students. Gender doesn't play a decisive role in campus placements.
-
Academic Performance:
- Higher percentages in SSC and HSC indicate higher chances of placements, possibly reflecting early skill development or work ethic.
-
Degree Type Impact:
- Students with undergrad degrees other than science and commerce have a lower probability of getting placed.
-
Employability Test Results:
- Higher marks in employability tests show a minor increase in placement probability, suggesting potential irrelevance of test content.
-
MBA Percentage:
- MBA percentage doesn't significantly impact placements; life skills such as communication, marketing, and work experience matter more.
-
Work Experience:
- Students with work experience have a higher probability of getting placed, highlighting the importance of work experience in campus placements.
- Clone the repository to your local machine.
- Install the required dependencies using
pip install -r requirements.txt(if a requirements file is present). - Open the notebook using Jupyter Notebook or Jupyter Lab.
- Execute the cells in sequential order.
Feel free to explore and contribute to the project. If you encounter any issues or have suggestions, please open an issue on the GitHub repository.
Happy analyzing!