This project aims to explore and visualize the data using Jupyter Notebook and various packages such as pandas, seaborn, numpy, matplotlib and scipy. The data consists of 66 entries with 11 columns including "Application No", "HighSchool GPA", "Physics Marks", "Chem Marks", "Biology Marks", "Name", "Father Name", "DOB", "SUM", and "AVG".
The following packages are used in this project:
- pandas
- seaborn
- numpy
- matplotlib
- scipy
The data is a pandas.core.frame.DataFrame with 66 entries and 11 columns. All columns except "Unnamed: 5" have 66 non-null values. The data types include float64, int64, and object.
The project implements the following functionalities:
- Finding the average marks
- Finding the relationship between subject marks and total marks
- Classifying students based on the year and month they were born
- Finding the correlation coefficient between GPA and total marks
- Plotting scatter plots between GPA and physics, chemistry, and biology marks, and between different subject marks to understand the relationship between them.
To run the project, make sure you have Jupyter Notebook and the above-mentioned packages installed. The data file should also be in the same directory as the Jupyter Notebook file.