Student Data Analysis

This project analyzes student data from an Excel file (Python Data.xlsx) using Python, pandas, seaborn, and matplotlib. The analysis is performed in a Jupyter Notebook (23102B2002_internship.ipynb).

Project Structure

• 23102B2002_internship.ipynb: Main notebook containing all analysis code and visualizations.
• Python Data.xlsx: Source data file with student information.

Setup

1. Install dependencies
   Make sure you have Python 3.x installed.
   Install required packages:

   pip install pandas seaborn matplotlib numpy

2. Run the notebook
   Open 23102B2002_internship.ipynb in Jupyter Notebook or VS Code and run the cells.

Analysis Overview

The notebook covers the following analyses:

1. Unique Students: Counts the number of unique students.
2. Average CGPA: Calculates the average CGPA.
3. Graduation Year Distribution: Visualizes student distribution across graduation years.
4. Python Experience: Shows the distribution of students' experience with Python.
5. Average Family Income: Computes average family income (mapped to numeric values).
6. CGPA by College: Visualizes top 5 colleges by average CGPA.
7. Outlier Detection: Detects outliers in attendee status and course quantity.
8. CGPA by City: Plots average CGPA for each city.
9. Family Income vs CGPA: Examines relationship between family income and CGPA.
10. Expected Salary Factors: Visualizes how CGPA, family income, and Python experience affect expected salary.
11. Popular Events by Field: Identifies which events attract students from specific fields.
12. Leadership Analysis: Compares CGPA and expected salary for students with/without leadership skills.
13. Leadership Correlation: Calculates correlation between leadership skills and expected salary.
14. Graduating Students: Counts students graduating by end of 2024.
15. Promotion Channel Effectiveness: Shows which channels bring more student participation.
16. Data Science Event Attendance: Counts students attending Data Science events.
17. High CGPA & Experience Salary Expectation: Analyzes salary expectations for students with high CGPA and Python experience.
18. Event Awareness by College: Finds students who learned about the event from their college and lists top 5 colleges.

How to Use

• Update the path to Python Data.xlsx in the notebook if needed.
• Run each cell to see the analysis and visualizations.
• Modify or extend the notebook for additional insights.

License

This project is for educational and internship purposes.