The dataset contained scores of 51 students in 12 tests conducted in a specific time frame (not mentioned in dataset). The dataset has been sourced from Kaggle under CC BY-NC-SA 4.0 liscence.
Student_ID: ID of individual students.Test_1,Test_2.....Test_12: Scores of individual students in different tests.
The dataset is cleaned, all the test scores are normalized and numeric with no null values.
Libraries used
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import klib
- Skewness for all the test scores are almost around zero, showing a kind of gaussian distribution suggesting a symmetrical distribution of test scores, with the majority of scores concentrated around the mean and approx equal number of scores on both sides.
- The average score in the tests shows a declining trend from Test_1 to Test_10 and a slight increase from Test_10 to Test_12, indicating decreasing enthusiasm and excitement for studies as the academic year progresses.
- The tests exhibit an increasing positive correlation towards test_12, indicating a higher level of correlation and interdependence among the tests. This shows that the tests were conducted for continous chapters in an academic year.
- Students with IDs 22000, 22031 and 22032 performed the best while 22022, 22005, 22010 and 22013 performed the worst.