diff --git "a/data_profile_report.html" "b/data_profile_report.html" new file mode 100644--- /dev/null +++ "b/data_profile_report.html" @@ -0,0 +1,393 @@ +📊 Detailed Data Report

Overview

Brought to you by YData

Dataset statistics
Number of variables19
Number of observations6607
Missing cells157
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 MiB
Average record size in memory799.7 B

Variable types
Numeric6
Categorical10
Boolean3

Alerts

internet_access is highly imbalanced (61.4%)Imbalance
learning_disabilities is highly imbalanced (51.5%)Imbalance
parental_education_level has 90 (1.4%) missing valuesMissing
distance_from_home has 67 (1.0%) missing valuesMissing
tutoring_sessions has 1513 (22.9%) zerosZeros

Reproduction
Analysis started2025-11-09 13:03:17.274384
Analysis finished2025-11-09 13:03:38.720780
Duration21.45 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

hours_studied
Real number (ℝ)

Distinct41
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.975329
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2025-11-09T18:33:39.066726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile10
Q116
median20
Q324
95-th percentile30
Maximum44
Range43
Interquartile range (IQR)8

Descriptive statistics
Standard deviation5.9905943
Coefficient of variation (CV)0.29989966
Kurtosis0.017770627
Mean19.975329
Median Absolute Deviation (MAD)4
Skewness0.013498909
Sum131977
Variance35.887221
MonotonicityNot monotonic

2025-11-09T18:33:39.446659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
20465
 
7.0%
19441
 
6.7%
21431
 
6.5%
23411
 
6.2%
22402
 
6.1%
18401
 
6.1%
17381
 
5.8%
24357
 
5.4%
16351
 
5.3%
15315
 
4.8%
Other values (31)2652
40.1%
ValueCountFrequency (%)
13
 
< 0.1%
26
 
0.1%
312
 
0.2%
417
 
0.3%
521
 
0.3%
617
 
0.3%
751
0.8%
858
0.9%
986
1.3%
1094
1.4%
ValueCountFrequency (%)
441
 
< 0.1%
431
 
< 0.1%
397
 
0.1%
387
 
0.1%
376
 
0.1%
3611
 
0.2%
3520
 
0.3%
3429
0.4%
3340
0.6%
3254
0.8%

parental_involvement
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size399.0 KiB
Medium
3362 
High
1908 
Low
1337 

Length
Max length6
Median length6
Mean length4.8153474
Min length3

Characters and Unicode
Total characters31815
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowLow
2nd rowLow
3rd rowMedium
4th rowLow
5th rowMedium

Common Values

ValueCountFrequency (%)
Medium3362
50.9%
High1908
28.9%
Low1337
 
20.2%

Length

2025-11-09T18:33:39.797602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-09T18:33:40.164539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
medium3362
50.9%
high1908
28.9%
low1337
 
20.2%

Most occurring characters

ValueCountFrequency (%)
i5270
16.6%
M3362
10.6%
e3362
10.6%
d3362
10.6%
u3362
10.6%
m3362
10.6%
H1908
 
6.0%
g1908
 
6.0%
h1908
 
6.0%
L1337
 
4.2%
Other values (2)2674
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)31815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i5270
16.6%
M3362
10.6%
e3362
10.6%
d3362
10.6%
u3362
10.6%
m3362
10.6%
H1908
 
6.0%
g1908
 
6.0%
h1908
 
6.0%
L1337
 
4.2%
Other values (2)2674
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)31815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i5270
16.6%
M3362
10.6%
e3362
10.6%
d3362
10.6%
u3362
10.6%
m3362
10.6%
H1908
 
6.0%
g1908
 
6.0%
h1908
 
6.0%
L1337
 
4.2%
Other values (2)2674
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)31815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i5270
16.6%
M3362
10.6%
e3362
10.6%
d3362
10.6%
u3362
10.6%
m3362
10.6%
H1908
 
6.0%
g1908
 
6.0%
h1908
 
6.0%
L1337
 
4.2%
Other values (2)2674
8.4%

access_to_resources
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size398.9 KiB
Medium
3319 
High
1975 
Low
1313 

Length
Max length6
Median length6
Mean length4.8059634
Min length3

Characters and Unicode
Total characters31753
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowHigh
2nd rowMedium
3rd rowMedium
4th rowMedium
5th rowMedium

Common Values

ValueCountFrequency (%)
Medium3319
50.2%
High1975
29.9%
Low1313
 
19.9%

Length

2025-11-09T18:33:40.488482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-09T18:33:40.817429image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
medium3319
50.2%
high1975
29.9%
low1313
 
19.9%

Most occurring characters

ValueCountFrequency (%)
i5294
16.7%
M3319
10.5%
e3319
10.5%
d3319
10.5%
u3319
10.5%
m3319
10.5%
H1975
 
6.2%
g1975
 
6.2%
h1975
 
6.2%
L1313
 
4.1%
Other values (2)2626
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)31753
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i5294
16.7%
M3319
10.5%
e3319
10.5%
d3319
10.5%
u3319
10.5%
m3319
10.5%
H1975
 
6.2%
g1975
 
6.2%
h1975
 
6.2%
L1313
 
4.1%
Other values (2)2626
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)31753
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i5294
16.7%
M3319
10.5%
e3319
10.5%
d3319
10.5%
u3319
10.5%
m3319
10.5%
H1975
 
6.2%
g1975
 
6.2%
h1975
 
6.2%
L1313
 
4.1%
Other values (2)2626
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)31753
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i5294
16.7%
M3319
10.5%
e3319
10.5%
d3319
10.5%
u3319
10.5%
m3319
10.5%
H1975
 
6.2%
g1975
 
6.2%
h1975
 
6.2%
L1313
 
4.1%
Other values (2)2626
8.3%

extracurricular_activities
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
True
3938 
False
2669 
ValueCountFrequency (%)
True3938
59.6%
False2669
40.4%
2025-11-09T18:33:41.130378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

sleep_hours
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0290601
Minimum4
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2025-11-09T18:33:41.365333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics
Minimum4
5-th percentile5
Q16
median7
Q38
95-th percentile9
Maximum10
Range6
Interquartile range (IQR)2

Descriptive statistics
Standard deviation1.4681202
Coefficient of variation (CV)0.20886437
Kurtosis-0.50369743
Mean7.0290601
Median Absolute Deviation (MAD)1
Skewness-0.023805437
Sum46441
Variance2.155377
MonotonicityNot monotonic

2025-11-09T18:33:41.627294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
71741
26.4%
81399
21.2%
61376
20.8%
9775
11.7%
5695
 
10.5%
10312
 
4.7%
4309
 
4.7%
ValueCountFrequency (%)
4309
 
4.7%
5695
 
10.5%
61376
20.8%
71741
26.4%
81399
21.2%
9775
11.7%
10312
 
4.7%
ValueCountFrequency (%)
10312
 
4.7%
9775
11.7%
81399
21.2%
71741
26.4%
61376
20.8%
5695
 
10.5%
4309
 
4.7%

previous_scores
Real number (ℝ)

Distinct51
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.070531
Minimum50
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2025-11-09T18:33:41.964235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics
Minimum50
5-th percentile53
Q163
median75
Q388
95-th percentile97
Maximum100
Range50
Interquartile range (IQR)25

Descriptive statistics
Standard deviation14.399784
Coefficient of variation (CV)0.19181674
Kurtosis-1.1910804
Mean75.070531
Median Absolute Deviation (MAD)12
Skewness-0.0037365338
Sum495991
Variance207.35379
MonotonicityNot monotonic

2025-11-09T18:33:42.367167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66165
 
2.5%
94155
 
2.3%
96153
 
2.3%
85150
 
2.3%
71146
 
2.2%
53144
 
2.2%
59142
 
2.1%
82141
 
2.1%
76140
 
2.1%
88139
 
2.1%
Other values (41)5132
77.7%
ValueCountFrequency (%)
5062
0.9%
51126
1.9%
52136
2.1%
53144
2.2%
54136
2.1%
55124
1.9%
56120
1.8%
57125
1.9%
58120
1.8%
59142
2.1%
ValueCountFrequency (%)
10069
1.0%
99125
1.9%
98131
2.0%
97115
1.7%
96153
2.3%
95129
2.0%
94155
2.3%
93129
2.0%
92126
1.9%
91136
2.1%

motivation_level
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size374.3 KiB
2
3351 
1
1937 
0
1319 

Length
Max length1
Median length1
Mean length1
Min length1

Characters and Unicode
Total characters6607
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st row1
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
23351
50.7%
11937
29.3%
01319
 
20.0%

Length

2025-11-09T18:33:42.719109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-09T18:33:43.016060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
23351
50.7%
11937
29.3%
01319
 
20.0%

Most occurring characters

ValueCountFrequency (%)
23351
50.7%
11937
29.3%
01319
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)6607
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
23351
50.7%
11937
29.3%
01319
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6607
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
23351
50.7%
11937
29.3%
01319
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6607
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
23351
50.7%
11937
29.3%
01319
 
20.0%

internet_access
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
True
6108 
False
 
499
ValueCountFrequency (%)
True6108
92.4%
False499
 
7.6%
2025-11-09T18:33:43.310016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

tutoring_sessions
Real number (ℝ)

Zeros 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4937188
Minimum0
Maximum8
Zeros1513
Zeros (%)22.9%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2025-11-09T18:33:43.560969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics
Standard deviation1.2305704
Coefficient of variation (CV)0.82383005
Kurtosis0.64371832
Mean1.4937188
Median Absolute Deviation (MAD)1
Skewness0.8155296
Sum9869
Variance1.5143036
MonotonicityNot monotonic

2025-11-09T18:33:43.806936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
12179
33.0%
21649
25.0%
01513
22.9%
3836
 
12.7%
4301
 
4.6%
5103
 
1.6%
618
 
0.3%
77
 
0.1%
81
 
< 0.1%
ValueCountFrequency (%)
01513
22.9%
12179
33.0%
21649
25.0%
3836
 
12.7%
4301
 
4.6%
5103
 
1.6%
618
 
0.3%
77
 
0.1%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
77
 
0.1%
618
 
0.3%
5103
 
1.6%
4301
 
4.6%
3836
 
12.7%
21649
25.0%
12179
33.0%
01513
22.9%

family_income
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size396.3 KiB
Low
2672 
Medium
2666 
High
1269 

Length
Max length6
Median length4
Mean length4.4026033
Min length3

Characters and Unicode
Total characters29088
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowLow
2nd rowMedium
3rd rowMedium
4th rowMedium
5th rowMedium

Common Values

ValueCountFrequency (%)
Low2672
40.4%
Medium2666
40.4%
High1269
19.2%

Length

2025-11-09T18:33:44.129871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-09T18:33:44.428818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
low2672
40.4%
medium2666
40.4%
high1269
19.2%

Most occurring characters

ValueCountFrequency (%)
i3935
13.5%
L2672
9.2%
o2672
9.2%
w2672
9.2%
M2666
9.2%
e2666
9.2%
d2666
9.2%
u2666
9.2%
m2666
9.2%
H1269
 
4.4%
Other values (2)2538
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)29088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i3935
13.5%
L2672
9.2%
o2672
9.2%
w2672
9.2%
M2666
9.2%
e2666
9.2%
d2666
9.2%
u2666
9.2%
m2666
9.2%
H1269
 
4.4%
Other values (2)2538
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)29088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i3935
13.5%
L2672
9.2%
o2672
9.2%
w2672
9.2%
M2666
9.2%
e2666
9.2%
d2666
9.2%
u2666
9.2%
m2666
9.2%
H1269
 
4.4%
Other values (2)2538
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)29088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i3935
13.5%
L2672
9.2%
o2672
9.2%
w2672
9.2%
M2666
9.2%
e2666
9.2%
d2666
9.2%
u2666
9.2%
m2666
9.2%
H1269
 
4.4%
Other values (2)2538
8.7%

teacher_quality
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size400.9 KiB
Medium
4003 
High
1947 
Low
657 

Length
Max length6
Median length6
Mean length5.1123051
Min length3

Characters and Unicode
Total characters33777
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowMedium
2nd rowMedium
3rd rowMedium
4th rowMedium
5th rowHigh

Common Values

ValueCountFrequency (%)
Medium4003
60.6%
High1947
29.5%
Low657
 
9.9%

Length

2025-11-09T18:33:44.731768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-09T18:33:45.065713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
medium4003
60.6%
high1947
29.5%
low657
 
9.9%

Most occurring characters

ValueCountFrequency (%)
i5950
17.6%
M4003
11.9%
e4003
11.9%
d4003
11.9%
u4003
11.9%
m4003
11.9%
H1947
 
5.8%
g1947
 
5.8%
h1947
 
5.8%
L657
 
1.9%
Other values (2)1314
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)33777
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i5950
17.6%
M4003
11.9%
e4003
11.9%
d4003
11.9%
u4003
11.9%
m4003
11.9%
H1947
 
5.8%
g1947
 
5.8%
h1947
 
5.8%
L657
 
1.9%
Other values (2)1314
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)33777
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i5950
17.6%
M4003
11.9%
e4003
11.9%
d4003
11.9%
u4003
11.9%
m4003
11.9%
H1947
 
5.8%
g1947
 
5.8%
h1947
 
5.8%
L657
 
1.9%
Other values (2)1314
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)33777
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i5950
17.6%
M4003
11.9%
e4003
11.9%
d4003
11.9%
u4003
11.9%
m4003
11.9%
H1947
 
5.8%
g1947
 
5.8%
h1947
 
5.8%
L657
 
1.9%
Other values (2)1314
 
3.9%

school_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size408.6 KiB
Public
4598 
Private
2009 

Length
Max length7
Median length6
Mean length6.3040714
Min length6

Characters and Unicode
Total characters41651
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowPublic
2nd rowPublic
3rd rowPublic
4th rowPublic
5th rowPublic

Common Values

ValueCountFrequency (%)
Public4598
69.6%
Private2009
30.4%

Length

2025-11-09T18:33:45.351669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-09T18:33:45.660613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
public4598
69.6%
private2009
30.4%

Most occurring characters

ValueCountFrequency (%)
P6607
15.9%
i6607
15.9%
u4598
11.0%
b4598
11.0%
l4598
11.0%
c4598
11.0%
r2009
 
4.8%
v2009
 
4.8%
a2009
 
4.8%
t2009
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)41651
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P6607
15.9%
i6607
15.9%
u4598
11.0%
b4598
11.0%
l4598
11.0%
c4598
11.0%
r2009
 
4.8%
v2009
 
4.8%
a2009
 
4.8%
t2009
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)41651
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P6607
15.9%
i6607
15.9%
u4598
11.0%
b4598
11.0%
l4598
11.0%
c4598
11.0%
r2009
 
4.8%
v2009
 
4.8%
a2009
 
4.8%
t2009
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)41651
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P6607
15.9%
i6607
15.9%
u4598
11.0%
b4598
11.0%
l4598
11.0%
c4598
11.0%
r2009
 
4.8%
v2009
 
4.8%
a2009
 
4.8%
t2009
 
4.8%

peer_influence
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size417.0 KiB
Positive
2638 
Neutral
2592 
Negative
1377 

Length
Max length8
Median length8
Mean length7.6076888
Min length7

Characters and Unicode
Total characters50264
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowPositive
2nd rowNegative
3rd rowNeutral
4th rowNegative
5th rowNeutral

Common Values

ValueCountFrequency (%)
Positive2638
39.9%
Neutral2592
39.2%
Negative1377
20.8%

Length

2025-11-09T18:33:46.484475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-09T18:33:46.796424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
positive2638
39.9%
neutral2592
39.2%
negative1377
20.8%

Most occurring characters

ValueCountFrequency (%)
e7984
15.9%
i6653
13.2%
t6607
13.1%
v4015
8.0%
N3969
7.9%
a3969
7.9%
P2638
 
5.2%
o2638
 
5.2%
s2638
 
5.2%
u2592
 
5.2%
Other values (3)6561
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)50264
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e7984
15.9%
i6653
13.2%
t6607
13.1%
v4015
8.0%
N3969
7.9%
a3969
7.9%
P2638
 
5.2%
o2638
 
5.2%
s2638
 
5.2%
u2592
 
5.2%
Other values (3)6561
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)50264
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e7984
15.9%
i6653
13.2%
t6607
13.1%
v4015
8.0%
N3969
7.9%
a3969
7.9%
P2638
 
5.2%
o2638
 
5.2%
s2638
 
5.2%
u2592
 
5.2%
Other values (3)6561
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)50264
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e7984
15.9%
i6653
13.2%
t6607
13.1%
v4015
8.0%
N3969
7.9%
a3969
7.9%
P2638
 
5.2%
o2638
 
5.2%
s2638
 
5.2%
u2592
 
5.2%
Other values (3)6561
13.1%

physical_activity
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9676101
Minimum0
Maximum6
Zeros46
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2025-11-09T18:33:47.064377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics
Standard deviation1.0312311
Coefficient of variation (CV)0.34749548
Kurtosis-0.05943887
Mean2.9676101
Median Absolute Deviation (MAD)1
Skewness-0.031364712
Sum19607
Variance1.0634376
MonotonicityNot monotonic

2025-11-09T18:33:47.314330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
32545
38.5%
21627
24.6%
41575
23.8%
1421
 
6.4%
5361
 
5.5%
046
 
0.7%
632
 
0.5%
ValueCountFrequency (%)
046
 
0.7%
1421
 
6.4%
21627
24.6%
32545
38.5%
41575
23.8%
5361
 
5.5%
632
 
0.5%
ValueCountFrequency (%)
632
 
0.5%
5361
 
5.5%
41575
23.8%
32545
38.5%
21627
24.6%
1421
 
6.4%
046
 
0.7%

learning_disabilities
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
False
5912 
True
695 
ValueCountFrequency (%)
False5912
89.5%
True695
 
10.5%
2025-11-09T18:33:47.651280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

parental_education_level
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing90
Missing (%)1.4%
Memory size431.3 KiB
High School
3223 
College
1989 
Postgraduate
1305 

Length
Max length12
Median length11
Mean length9.9794384
Min length7

Characters and Unicode
Total characters65036
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowHigh School
2nd rowCollege
3rd rowPostgraduate
4th rowHigh School
5th rowCollege

Common Values

ValueCountFrequency (%)
High School3223
48.8%
College1989
30.1%
Postgraduate1305
19.8%
(Missing)90
 
1.4%

Length

2025-11-09T18:33:47.898236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-09T18:33:48.221180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
high3223
33.1%
school3223
33.1%
college1989
20.4%
postgraduate1305
13.4%

Most occurring characters

ValueCountFrequency (%)
o9740
15.0%
l7201
11.1%
g6517
10.0%
h6446
9.9%
e5283
 
8.1%
H3223
 
5.0%
3223
 
5.0%
S3223
 
5.0%
c3223
 
5.0%
i3223
 
5.0%
Other values (8)13734
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)65036
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o9740
15.0%
l7201
11.1%
g6517
10.0%
h6446
9.9%
e5283
 
8.1%
H3223
 
5.0%
3223
 
5.0%
S3223
 
5.0%
c3223
 
5.0%
i3223
 
5.0%
Other values (8)13734
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)65036
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o9740
15.0%
l7201
11.1%
g6517
10.0%
h6446
9.9%
e5283
 
8.1%
H3223
 
5.0%
3223
 
5.0%
S3223
 
5.0%
c3223
 
5.0%
i3223
 
5.0%
Other values (8)13734
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)65036
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o9740
15.0%
l7201
11.1%
g6517
10.0%
h6446
9.9%
e5283
 
8.1%
H3223
 
5.0%
3223
 
5.0%
S3223
 
5.0%
c3223
 
5.0%
i3223
 
5.0%
Other values (8)13734
21.1%

distance_from_home
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing67
Missing (%)1.0%
Memory size400.5 KiB
Near
3884 
Moderate
1998 
Far
658 

Length
Max length8
Median length4
Mean length5.1214067
Min length3

Characters and Unicode
Total characters33494
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowNear
2nd rowModerate
3rd rowNear
4th rowModerate
5th rowNear

Common Values

ValueCountFrequency (%)
Near3884
58.8%
Moderate1998
30.2%
Far658
 
10.0%
(Missing)67
 
1.0%

Length

2025-11-09T18:33:48.514133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-09T18:33:48.836087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
near3884
59.4%
moderate1998
30.6%
far658
 
10.1%

Most occurring characters

ValueCountFrequency (%)
e7880
23.5%
a6540
19.5%
r6540
19.5%
N3884
11.6%
M1998
 
6.0%
o1998
 
6.0%
d1998
 
6.0%
t1998
 
6.0%
F658
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)33494
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e7880
23.5%
a6540
19.5%
r6540
19.5%
N3884
11.6%
M1998
 
6.0%
o1998
 
6.0%
d1998
 
6.0%
t1998
 
6.0%
F658
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)33494
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e7880
23.5%
a6540
19.5%
r6540
19.5%
N3884
11.6%
M1998
 
6.0%
o1998
 
6.0%
d1998
 
6.0%
t1998
 
6.0%
F658
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)33494
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e7880
23.5%
a6540
19.5%
r6540
19.5%
N3884
11.6%
M1998
 
6.0%
o1998
 
6.0%
d1998
 
6.0%
t1998
 
6.0%
F658
 
2.0%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size399.2 KiB
Male
3814 
Female
2793 

Length
Max length6
Median length4
Mean length4.8454669
Min length4

Characters and Unicode
Total characters32014
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowMale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male3814
57.7%
Female2793
42.3%

Length

2025-11-09T18:33:49.190019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-09T18:33:49.537965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
male3814
57.7%
female2793
42.3%

Most occurring characters

ValueCountFrequency (%)
e9400
29.4%
a6607
20.6%
l6607
20.6%
M3814
11.9%
F2793
 
8.7%
m2793
 
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)32014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e9400
29.4%
a6607
20.6%
l6607
20.6%
M3814
11.9%
F2793
 
8.7%
m2793
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)32014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e9400
29.4%
a6607
20.6%
l6607
20.6%
M3814
11.9%
F2793
 
8.7%
m2793
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)32014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e9400
29.4%
a6607
20.6%
l6607
20.6%
M3814
11.9%
F2793
 
8.7%
m2793
 
8.7%

exam_score
Real number (ℝ)

Distinct45
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.235659
Minimum55
Maximum101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2025-11-09T18:33:49.844911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics
Minimum55
5-th percentile62
Q165
median67
Q369
95-th percentile73
Maximum101
Range46
Interquartile range (IQR)4

Descriptive statistics
Standard deviation3.8904558
Coefficient of variation (CV)0.057862983
Kurtosis10.575423
Mean67.235659
Median Absolute Deviation (MAD)2
Skewness1.6448083
Sum444226
Variance15.135646
MonotonicityNot monotonic

2025-11-09T18:33:50.265842image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
68759
11.5%
66751
11.4%
67717
10.9%
65679
10.3%
69624
9.4%
70542
8.2%
64501
7.6%
71408
6.2%
63371
 
5.6%
72304
 
4.6%
Other values (35)951
14.4%
ValueCountFrequency (%)
551
 
< 0.1%
561
 
< 0.1%
574
 
0.1%
5822
 
0.3%
5940
 
0.6%
6077
 
1.2%
61171
 
2.6%
62264
4.0%
63371
5.6%
64501
7.6%
ValueCountFrequency (%)
1011
 
< 0.1%
1001
 
< 0.1%
992
< 0.1%
983
< 0.1%
973
< 0.1%
961
 
< 0.1%
952
< 0.1%
944
0.1%
932
< 0.1%
922
< 0.1%

Interactions

2025-11-09T18:33:33.565629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:22.600477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:24.711121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:26.691789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:28.643457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:31.475998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:33.910571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:23.081395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:25.035068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:26.993737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:29.430325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:31.793930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:34.745432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:23.443334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:25.386137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:27.329678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:29.795264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:32.134868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:35.332329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:23.762286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:25.702952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:27.629633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:30.173203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:32.494810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:35.718270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:24.089226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:26.061891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:28.002566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:30.578135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:32.847749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:36.127201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:24.402177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:26.388840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:28.325513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:31.031059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-11-09T18:33:33.205695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2025-11-09T18:33:50.639776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
access_to_resourcesdistance_from_homeexam_scoreextracurricular_activitiesfamily_incomegenderhours_studiedinternet_accesslearning_disabilitiesmotivation_levelparental_education_levelparental_involvementpeer_influencephysical_activityprevious_scoresschool_typesleep_hoursteacher_qualitytutoring_sessions
access_to_resources1.0000.0060.1310.0060.0100.0000.0330.0000.0000.0000.0000.0210.0000.0130.0180.0260.0060.0040.001
distance_from_home0.0061.0000.0800.0190.0070.0000.0000.0000.0000.0000.0000.0000.0000.0070.0180.0000.0000.0000.000
exam_score0.1310.0801.0000.0640.0610.0000.4810.0490.1200.0720.0780.1180.0690.0290.1920.000-0.0080.0620.164
extracurricular_activities0.0060.0190.0641.0000.0000.0000.0000.0000.0000.0000.0000.0180.0290.0000.0270.0000.0000.0120.011
family_income0.0100.0070.0610.0001.0000.0000.0190.0050.0000.0000.0000.0000.0000.0270.0090.0000.0000.0090.000
gender0.0000.0000.0000.0000.0001.0000.0000.0080.0080.0000.0000.0180.0080.0000.0280.0000.0110.0000.000
hours_studied0.0330.0000.4810.0000.0190.0001.0000.0000.0330.0000.0150.0230.029-0.0030.0240.0000.0110.000-0.013
internet_access0.0000.0000.0490.0000.0050.0080.0001.0000.0000.0110.0180.0000.0000.0000.0000.0000.0160.0000.018
learning_disabilities0.0000.0000.1200.0000.0000.0080.0330.0001.0000.0080.0000.0000.0000.0000.0000.0000.0050.0000.016
motivation_level0.0000.0000.0720.0000.0000.0000.0000.0110.0081.0000.0000.0050.0070.0210.0090.0000.0000.0260.009
parental_education_level0.0000.0000.0780.0000.0000.0000.0150.0180.0000.0001.0000.0000.0160.0060.0000.0180.0000.0000.000
parental_involvement0.0210.0000.1180.0180.0000.0180.0230.0000.0000.0050.0001.0000.0100.0160.0000.0130.0000.0000.019
peer_influence0.0000.0000.0690.0290.0000.0080.0290.0000.0000.0070.0160.0101.0000.0000.0100.0000.0160.0000.000
physical_activity0.0130.0070.0290.0000.0270.000-0.0030.0000.0000.0210.0060.0160.0001.000-0.0080.0000.0010.0180.008
previous_scores0.0180.0180.1920.0270.0090.0280.0240.0000.0000.0090.0000.0000.010-0.0081.0000.000-0.0220.031-0.018
school_type0.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0130.0000.0000.0001.0000.0000.0000.000
sleep_hours0.0060.000-0.0080.0000.0000.0110.0110.0160.0050.0000.0000.0000.0160.001-0.0220.0001.0000.018-0.006
teacher_quality0.0040.0000.0620.0120.0090.0000.0000.0000.0000.0260.0000.0000.0000.0180.0310.0000.0181.0000.012
tutoring_sessions0.0010.0000.1640.0110.0000.000-0.0130.0180.0160.0090.0000.0190.0000.008-0.0180.000-0.0060.0121.000

Missing values

2025-11-09T18:33:36.745093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-09T18:33:37.766919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-09T18:33:38.508820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

hours_studiedparental_involvementaccess_to_resourcesextracurricular_activitiessleep_hoursprevious_scoresmotivation_levelinternet_accesstutoring_sessionsfamily_incometeacher_qualityschool_typepeer_influencephysical_activitylearning_disabilitiesparental_education_leveldistance_from_homegenderexam_score
023LowHighNo7731Yes0LowMediumPublicPositive3NoHigh SchoolNearMale67
119LowMediumNo8591Yes2MediumMediumPublicNegative4NoCollegeModerateFemale61
224MediumMediumYes7912Yes2MediumMediumPublicNeutral4NoPostgraduateNearMale74
329LowMediumYes8982Yes1MediumMediumPublicNegative4NoHigh SchoolModerateMale71
419MediumMediumYes6652Yes3MediumHighPublicNeutral4NoCollegeNearFemale70
519MediumMediumYes8892Yes3MediumMediumPublicPositive3NoPostgraduateNearMale71
629MediumLowYes7681Yes1LowMediumPrivateNeutral2NoHigh SchoolModerateMale67
725LowHighYes6502Yes1HighHighPublicNegative2NoHigh SchoolFarMale66
817MediumHighNo6800Yes0MediumLowPrivateNeutral1NoCollegeNearMale69
923MediumMediumYes8712Yes0HighHighPublicPositive5NoHigh SchoolModerateMale72
hours_studiedparental_involvementaccess_to_resourcesextracurricular_activitiessleep_hoursprevious_scoresmotivation_levelinternet_accesstutoring_sessionsfamily_incometeacher_qualityschool_typepeer_influencephysical_activitylearning_disabilitiesparental_education_leveldistance_from_homegenderexam_score
659716HighMediumYes6720Yes0HighHighPublicNegative2NoPostgraduateNearFemale70
65989LowMediumYes6642Yes1HighMediumPublicNeutral2NoHigh SchoolNearFemale64
659930MediumLowNo5521No3HighMediumPrivateNeutral2NoPostgraduateModerateFemale70
660012MediumLowYes4542Yes2MediumHighPrivateNeutral3NoHigh SchoolNearFemale67
660120MediumLowNo6511Yes2MediumMediumPublicNeutral4NoHigh SchoolModerateFemale65
660225HighMediumNo7762Yes1HighMediumPublicPositive2NoHigh SchoolNearFemale68
660323HighMediumNo8812Yes3LowHighPublicPositive2NoHigh SchoolNearFemale69
660420MediumLowYes6651Yes3LowMediumPublicNegative2NoPostgraduateNearFemale68
660510HighHighYes6910Yes2LowMediumPrivatePositive3NoHigh SchoolFarFemale68
660615MediumLowYes9942Yes0MediumMediumPublicPositive4NoPostgraduateNearMale64
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