commit 0639065c1dda00d310ef3b7c05f18a2f079879ad Author: stud204002 Date: Wed Apr 22 21:04:41 2026 +0300 Задание 3, Анализ данных студентов diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..85c5f8c --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ +venv/ +.ipynb_checkpoints/ +*.pyc +__pycache__/ +.DS_Store \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..fe69466 --- /dev/null +++ b/README.md @@ -0,0 +1,16 @@ +# Zadanie 3 + 500 +## +- `notebooks/zadanie3_analysis.ipynb` - Jupyter Notebook +- `data/student_performance.csv` - + +## +pandas, numpy, matplotlib, seaborn + +## +1. : ~65-70 +2. +3. + +## + , -2301-02-00 \ No newline at end of file diff --git a/data/student_performance.csv b/data/student_performance.csv new file mode 100644 index 0000000..d055121 --- /dev/null +++ b/data/student_performance.csv @@ -0,0 +1,501 @@ +student_id,gender,age,study_hours_per_week,attendance_rate,parent_education,internet_access,extracurricular,previous_score,final_score,passed +STU0001,Male,15,25,63.8,Bachelor,Yes,Yes,41,67,Yes +STU0002,Female,15,2,54.7,Bachelor,Yes,Yes,83,28,No +STU0003,Female,19,10,90.5,High School,Yes,No,73,49,No +STU0004,Male,16,26,66.8,High School,No,Yes,75,70,Yes +STU0005,Female,15,25,73.0,High School,No,Yes,67,77,Yes +STU0006,Female,19,8,85.2,High School,Yes,No,40,37,No +STU0007,Male,18,10,72.7,Master,Yes,No,75,42,No +STU0008,Female,15,21,81.7,None,Yes,Yes,89,70,Yes +STU0009,Female,19,9,84.2,High School,Yes,Yes,70,49,No +STU0010,Female,15,8,95.7,None,No,Yes,93,56,Yes +STU0011,Female,16,10,57.0,None,No,No,81,44,No +STU0012,Male,16,18,74.7,High School,Yes,Yes,50,69,Yes +STU0013,Female,19,4,69.2,None,No,No,31,38,No +STU0014,Male,19,26,63.3,Master,Yes,No,85,64,Yes +STU0015,Female,15,25,93.8,Master,Yes,Yes,68,84,Yes +STU0016,Male,16,13,88.1,None,Yes,No,92,49,No +STU0017,Male,17,30,99.3,Master,Yes,Yes,60,89,Yes +STU0018,Male,15,25,74.3,High School,Yes,Yes,90,79,Yes +STU0019,Male,17,18,93.6,PhD,Yes,Yes,69,65,Yes +STU0020,Female,18,30,75.9,High School,Yes,Yes,38,70,Yes +STU0021,Male,19,19,61.5,Bachelor,Yes,Yes,37,46,No +STU0022,Male,15,29,66.5,None,Yes,No,92,72,Yes +STU0023,Male,19,20,73.6,PhD,No,Yes,42,48,No +STU0024,Female,17,15,70.6,High School,Yes,Yes,81,67,Yes +STU0025,Female,15,9,59.6,None,No,Yes,84,38,No +STU0026,Female,18,9,93.7,High School,No,Yes,36,53,Yes +STU0027,Male,15,26,92.4,Bachelor,No,No,91,75,Yes +STU0028,Female,15,7,68.9,PhD,No,No,66,42,No +STU0029,Female,16,8,64.8,High School,Yes,No,37,24,No +STU0030,Female,19,29,76.6,High School,Yes,Yes,38,78,Yes +STU0031,Male,16,14,56.0,None,Yes,Yes,40,43,No +STU0032,Female,17,8,83.5,Master,Yes,No,80,42,No +STU0033,Female,18,12,96.5,High School,Yes,No,42,42,No +STU0034,Male,19,10,56.6,Master,Yes,Yes,77,41,No +STU0035,Male,18,28,77.2,Master,Yes,No,43,62,Yes +STU0036,Female,15,30,55.4,None,Yes,No,66,78,Yes +STU0037,Male,17,8,84.4,Master,No,No,36,31,No +STU0038,Female,17,3,50.2,Bachelor,No,Yes,86,38,No +STU0039,Female,19,2,55.6,Bachelor,Yes,No,48,27,No +STU0040,Male,15,11,68.2,High School,No,Yes,61,55,Yes +STU0041,Male,17,26,78.0,PhD,Yes,Yes,50,82,Yes +STU0042,Male,18,2,59.0,Master,No,Yes,64,23,No +STU0043,Male,18,29,51.9,PhD,Yes,Yes,88,72,Yes +STU0044,Female,16,9,51.2,Bachelor,No,No,65,28,No +STU0045,Female,17,22,75.5,None,No,Yes,44,57,Yes +STU0046,Male,19,10,51.9,None,No,No,70,42,No +STU0047,Male,18,30,78.8,Master,Yes,No,30,75,Yes +STU0048,Male,17,15,53.5,Master,No,Yes,68,53,Yes +STU0049,Female,18,12,70.1,Master,Yes,Yes,83,61,Yes +STU0050,Female,16,21,78.5,PhD,Yes,No,66,59,Yes +STU0051,Female,19,21,82.7,PhD,No,No,57,68,Yes +STU0052,Female,16,23,54.2,None,No,Yes,60,68,Yes +STU0053,Female,16,27,60.0,High School,Yes,Yes,90,80,Yes +STU0054,Male,18,15,94.3,None,Yes,No,93,66,Yes +STU0055,Male,16,22,84.4,High School,No,Yes,52,78,Yes +STU0056,Female,15,19,62.5,High School,No,Yes,89,71,Yes +STU0057,Female,18,21,90.8,None,No,No,50,76,Yes +STU0058,Female,18,10,87.6,Master,No,Yes,65,53,Yes +STU0059,Male,17,9,63.6,Master,Yes,Yes,49,34,No +STU0060,Female,16,24,60.7,PhD,No,No,89,70,Yes +STU0061,Male,16,28,71.0,None,Yes,No,91,66,Yes +STU0062,Female,17,26,69.5,PhD,Yes,No,58,64,Yes +STU0063,Female,18,2,69.4,PhD,Yes,No,46,37,No +STU0064,Male,18,20,78.2,High School,Yes,No,47,61,Yes +STU0065,Male,15,10,69.0,Bachelor,No,No,73,59,Yes +STU0066,Female,17,26,97.6,PhD,No,Yes,90,71,Yes +STU0067,Male,17,9,82.5,High School,Yes,Yes,55,34,No +STU0068,Male,16,6,73.7,High School,Yes,No,62,52,Yes +STU0069,Female,16,21,80.4,High School,Yes,No,43,67,Yes +STU0070,Male,17,20,83.9,PhD,No,Yes,39,65,Yes +STU0071,Male,15,24,88.6,None,Yes,Yes,74,79,Yes +STU0072,Female,17,4,75.3,Master,Yes,No,92,34,No +STU0073,Female,17,22,94.6,PhD,Yes,No,52,79,Yes +STU0074,Female,19,27,96.0,PhD,No,No,64,77,Yes +STU0075,Male,15,10,94.1,Bachelor,No,No,73,42,No +STU0076,Female,17,7,74.4,Master,No,No,65,49,No +STU0077,Female,19,2,75.8,Bachelor,Yes,Yes,82,42,No +STU0078,Male,18,22,85.6,PhD,Yes,Yes,67,64,Yes +STU0079,Female,16,11,83.2,Master,No,No,84,66,Yes +STU0080,Female,17,24,72.7,Master,No,Yes,45,74,Yes +STU0081,Male,17,5,87.1,Bachelor,Yes,Yes,91,44,No +STU0082,Female,15,28,59.7,Bachelor,No,Yes,68,58,Yes +STU0083,Male,17,3,98.7,None,No,Yes,92,40,No +STU0084,Male,19,11,73.5,PhD,No,Yes,36,38,No +STU0085,Female,15,28,53.3,PhD,Yes,Yes,49,56,Yes +STU0086,Female,15,9,55.9,PhD,Yes,No,87,46,No +STU0087,Female,19,15,65.3,None,Yes,Yes,56,58,Yes +STU0088,Male,17,23,54.1,Bachelor,Yes,Yes,50,45,No +STU0089,Female,18,24,79.7,Master,Yes,Yes,66,80,Yes +STU0090,Female,18,4,84.4,Master,Yes,No,44,42,No +STU0091,Male,16,10,91.3,High School,Yes,Yes,69,60,Yes +STU0092,Female,18,5,73.4,Master,No,No,94,50,Yes +STU0093,Female,18,4,79.9,PhD,No,No,33,23,No +STU0094,Male,19,20,97.5,Master,Yes,Yes,90,78,Yes +STU0095,Female,17,7,99.8,PhD,No,Yes,90,54,Yes +STU0096,Female,17,12,83.5,Bachelor,No,No,93,55,Yes +STU0097,Female,19,3,72.7,Master,No,No,44,44,No +STU0098,Female,19,28,99.4,None,No,No,36,70,Yes +STU0099,Female,19,26,74.9,PhD,Yes,Yes,64,76,Yes +STU0100,Male,17,16,94.0,PhD,Yes,Yes,60,71,Yes +STU0101,Male,17,19,50.7,PhD,Yes,Yes,44,51,Yes +STU0102,Male,16,17,96.6,Master,No,No,91,72,Yes +STU0103,Male,18,19,57.2,Bachelor,Yes,Yes,65,67,Yes +STU0104,Female,17,27,75.4,High School,No,No,92,70,Yes +STU0105,Female,19,17,67.3,None,No,No,71,50,Yes +STU0106,Male,19,14,61.7,PhD,Yes,No,90,64,Yes +STU0107,Female,18,23,89.6,Bachelor,No,Yes,46,71,Yes +STU0108,Female,15,29,92.3,High School,No,Yes,48,78,Yes +STU0109,Male,15,17,89.1,Master,No,No,40,55,Yes +STU0110,Female,17,22,85.9,PhD,Yes,Yes,60,75,Yes +STU0111,Female,16,25,54.5,High School,Yes,No,51,71,Yes +STU0112,Female,15,3,66.2,High School,No,No,77,37,No +STU0113,Male,16,18,70.6,Bachelor,Yes,Yes,40,60,Yes +STU0114,Female,19,23,62.0,None,Yes,Yes,89,75,Yes +STU0115,Female,18,10,83.3,PhD,No,Yes,39,46,No +STU0116,Female,19,11,81.9,PhD,No,No,68,45,No +STU0117,Female,18,5,61.9,None,No,No,67,24,No +STU0118,Female,17,2,78.3,High School,No,No,59,42,No +STU0119,Female,16,22,59.5,Master,Yes,Yes,35,51,Yes +STU0120,Female,15,20,68.2,Bachelor,Yes,No,71,72,Yes +STU0121,Female,16,8,56.6,None,No,No,51,32,No +STU0122,Female,17,25,93.5,High School,No,Yes,48,84,Yes +STU0123,Male,18,29,90.2,Master,Yes,No,31,69,Yes +STU0124,Male,18,13,83.6,Master,No,No,43,59,Yes +STU0125,Male,18,2,81.0,None,No,Yes,38,39,No +STU0126,Female,17,22,70.4,Bachelor,Yes,Yes,68,67,Yes +STU0127,Male,15,9,94.4,Bachelor,No,No,77,63,Yes +STU0128,Female,19,25,86.3,PhD,Yes,No,82,72,Yes +STU0129,Male,17,8,72.2,Bachelor,No,Yes,77,51,Yes +STU0130,Female,15,14,63.8,High School,No,Yes,57,56,Yes +STU0131,Male,15,27,66.7,Bachelor,Yes,Yes,56,74,Yes +STU0132,Male,16,12,88.7,None,Yes,No,48,43,No +STU0133,Female,16,5,83.1,High School,Yes,Yes,75,57,Yes +STU0134,Male,19,12,50.8,Master,Yes,Yes,83,50,Yes +STU0135,Male,15,17,72.4,Master,Yes,No,94,58,Yes +STU0136,Male,19,11,72.9,High School,Yes,No,81,52,Yes +STU0137,Male,18,24,95.5,High School,Yes,No,48,61,Yes +STU0138,Male,17,21,81.7,None,No,No,67,68,Yes +STU0139,Female,15,27,85.1,None,Yes,No,87,75,Yes +STU0140,Female,17,28,72.7,PhD,Yes,No,84,75,Yes +STU0141,Female,17,6,84.3,PhD,Yes,Yes,40,29,No +STU0142,Female,15,25,86.9,Bachelor,Yes,No,45,70,Yes +STU0143,Female,18,29,95.8,High School,No,No,75,75,Yes +STU0144,Male,16,23,74.1,High School,No,No,44,74,Yes +STU0145,Female,19,9,90.4,None,Yes,No,33,37,No +STU0146,Female,17,12,67.6,Bachelor,Yes,No,38,34,No +STU0147,Male,15,25,76.5,PhD,No,No,73,65,Yes +STU0148,Female,17,25,66.2,None,Yes,Yes,49,57,Yes +STU0149,Male,15,10,72.2,PhD,No,No,83,46,No +STU0150,Male,19,5,67.3,High School,No,No,69,27,No +STU0151,Male,18,21,52.7,Bachelor,No,Yes,47,65,Yes +STU0152,Female,17,12,56.0,PhD,No,Yes,46,41,No +STU0153,Male,19,19,91.7,Master,Yes,No,35,61,Yes +STU0154,Male,18,4,93.1,None,No,No,83,44,No +STU0155,Male,18,2,98.3,Bachelor,No,No,41,38,No +STU0156,Male,16,15,79.4,None,Yes,No,69,68,Yes +STU0157,Female,16,12,89.0,High School,Yes,Yes,74,55,Yes +STU0158,Male,16,5,57.3,Bachelor,Yes,Yes,39,22,No +STU0159,Female,18,26,78.2,None,No,No,70,65,Yes +STU0160,Female,15,17,72.1,Master,No,Yes,75,63,Yes +STU0161,Male,17,16,72.6,High School,No,No,39,58,Yes +STU0162,Male,19,21,75.4,PhD,Yes,No,54,59,Yes +STU0163,Female,19,6,97.9,PhD,Yes,No,40,47,No +STU0164,Male,15,9,85.4,PhD,Yes,No,77,48,No +STU0165,Female,18,26,66.9,None,Yes,No,38,61,Yes +STU0166,Male,19,23,69.3,Master,Yes,No,40,66,Yes +STU0167,Male,17,11,98.4,PhD,Yes,Yes,69,61,Yes +STU0168,Female,17,28,56.4,None,Yes,No,95,73,Yes +STU0169,Male,17,11,59.0,Bachelor,No,No,54,37,No +STU0170,Male,16,4,64.8,High School,Yes,No,46,38,No +STU0171,Female,16,7,59.0,None,Yes,No,35,33,No +STU0172,Female,16,16,80.5,PhD,Yes,Yes,69,71,Yes +STU0173,Female,16,13,83.9,None,No,No,66,66,Yes +STU0174,Female,19,18,70.9,Bachelor,Yes,Yes,62,46,No +STU0175,Female,17,19,96.7,None,Yes,No,40,74,Yes +STU0176,Male,17,16,95.3,Bachelor,No,Yes,58,63,Yes +STU0177,Female,15,15,52.7,None,No,Yes,79,41,No +STU0178,Female,16,2,65.9,High School,No,Yes,47,20,No +STU0179,Female,18,24,91.6,PhD,No,Yes,40,56,Yes +STU0180,Female,16,28,57.5,None,No,Yes,66,64,Yes +STU0181,Female,15,3,61.9,None,No,Yes,44,31,No +STU0182,Male,19,20,62.1,Bachelor,No,No,30,58,Yes +STU0183,Female,16,20,70.8,High School,No,Yes,94,80,Yes +STU0184,Male,18,29,73.5,PhD,No,No,32,67,Yes +STU0185,Male,17,9,86.6,High School,No,Yes,35,42,No +STU0186,Female,16,28,89.0,PhD,No,Yes,47,65,Yes +STU0187,Female,15,11,60.1,Bachelor,Yes,No,69,49,No +STU0188,Female,19,17,62.7,Bachelor,No,No,36,56,Yes +STU0189,Female,17,5,90.0,PhD,No,No,79,45,No +STU0190,Male,18,24,74.9,None,No,Yes,84,62,Yes +STU0191,Female,19,28,59.0,Master,No,Yes,40,62,Yes +STU0192,Female,17,16,80.1,PhD,Yes,No,74,61,Yes +STU0193,Male,17,21,99.9,Master,Yes,Yes,81,73,Yes +STU0194,Male,15,6,92.4,PhD,Yes,Yes,38,36,No +STU0195,Female,17,14,97.5,High School,Yes,No,77,61,Yes +STU0196,Female,15,20,56.9,Master,No,No,65,52,Yes +STU0197,Male,15,25,59.3,None,No,Yes,63,76,Yes +STU0198,Female,18,8,99.8,Master,No,Yes,45,39,No +STU0199,Male,18,7,94.9,PhD,Yes,No,74,60,Yes +STU0200,Male,19,19,64.5,Master,Yes,Yes,76,64,Yes +STU0201,Male,15,8,89.6,Bachelor,No,Yes,76,56,Yes +STU0202,Female,18,27,99.2,Bachelor,Yes,Yes,69,81,Yes +STU0203,Female,19,15,88.5,None,Yes,Yes,70,68,Yes +STU0204,Male,18,16,84.0,Master,Yes,Yes,46,56,Yes +STU0205,Male,15,18,57.7,Bachelor,Yes,No,58,51,Yes +STU0206,Female,15,10,59.8,None,No,Yes,68,50,Yes +STU0207,Male,19,22,58.4,None,Yes,Yes,73,68,Yes +STU0208,Male,15,4,52.3,None,No,Yes,83,42,No +STU0209,Male,18,30,81.4,Master,No,No,61,91,Yes +STU0210,Female,17,16,53.6,High School,Yes,No,83,56,Yes +STU0211,Female,16,12,80.3,Master,No,No,81,47,No +STU0212,Female,19,19,55.3,Bachelor,No,Yes,64,56,Yes +STU0213,Male,16,5,52.5,PhD,No,Yes,50,28,No +STU0214,Female,16,26,58.0,None,No,No,69,70,Yes +STU0215,Male,15,14,75.3,Bachelor,Yes,No,36,36,No +STU0216,Female,18,21,94.1,PhD,No,Yes,43,66,Yes +STU0217,Male,17,25,68.3,PhD,No,Yes,81,60,Yes +STU0218,Male,17,19,64.4,PhD,No,No,45,45,No +STU0219,Male,18,13,89.7,Master,No,Yes,55,61,Yes +STU0220,Female,19,3,52.3,Bachelor,No,No,88,26,No +STU0221,Male,17,21,65.9,PhD,No,No,94,71,Yes +STU0222,Female,19,11,73.7,High School,No,No,44,45,No +STU0223,Female,15,21,73.7,None,Yes,Yes,91,61,Yes +STU0224,Female,16,28,84.0,High School,Yes,Yes,32,72,Yes +STU0225,Female,18,6,70.6,Bachelor,No,No,37,44,No +STU0226,Male,19,8,78.0,PhD,No,No,52,44,No +STU0227,Female,19,13,83.8,Master,No,Yes,61,49,No +STU0228,Female,16,11,88.6,Bachelor,No,No,91,57,Yes +STU0229,Female,17,5,78.7,None,No,No,74,54,Yes +STU0230,Male,17,3,64.4,High School,No,No,62,44,No +STU0231,Female,16,8,91.4,Master,No,Yes,43,52,Yes +STU0232,Male,16,3,83.5,None,Yes,Yes,36,24,No +STU0233,Female,17,24,73.6,Bachelor,Yes,Yes,82,79,Yes +STU0234,Female,18,22,60.0,Master,No,No,37,67,Yes +STU0235,Male,19,9,76.8,High School,Yes,No,52,32,No +STU0236,Female,18,7,96.8,Master,Yes,Yes,68,56,Yes +STU0237,Male,17,11,72.7,None,No,Yes,94,56,Yes +STU0238,Female,16,27,55.6,Bachelor,No,No,53,52,Yes +STU0239,Female,17,20,83.7,Bachelor,Yes,No,84,72,Yes +STU0240,Female,15,14,83.5,Bachelor,Yes,No,71,52,Yes +STU0241,Male,17,14,61.8,Master,No,No,31,38,No +STU0242,Female,16,3,83.4,PhD,No,Yes,81,51,Yes +STU0243,Female,15,22,95.8,Master,No,Yes,58,62,Yes +STU0244,Female,16,16,87.4,None,No,No,90,77,Yes +STU0245,Male,16,23,87.7,High School,No,No,39,71,Yes +STU0246,Male,15,28,77.4,None,Yes,Yes,51,70,Yes +STU0247,Female,15,23,60.3,None,No,Yes,95,70,Yes +STU0248,Male,18,6,75.7,PhD,Yes,No,69,53,Yes +STU0249,Male,18,10,90.8,Bachelor,Yes,Yes,84,55,Yes +STU0250,Male,19,3,96.7,High School,No,Yes,66,44,No +STU0251,Male,16,26,82.1,PhD,No,No,87,78,Yes +STU0252,Female,15,23,98.2,None,Yes,No,45,62,Yes +STU0253,Female,19,6,86.4,Bachelor,Yes,Yes,68,47,No +STU0254,Female,19,14,91.2,Bachelor,No,No,49,51,Yes +STU0255,Male,15,3,90.5,PhD,Yes,Yes,56,29,No +STU0256,Male,19,3,72.3,High School,Yes,Yes,81,41,No +STU0257,Male,19,8,87.7,High School,Yes,No,59,58,Yes +STU0258,Female,19,21,88.6,Master,Yes,No,48,73,Yes +STU0259,Male,18,11,63.8,None,Yes,No,93,40,No +STU0260,Female,18,27,76.2,PhD,No,Yes,68,73,Yes +STU0261,Male,19,17,62.0,Bachelor,No,Yes,89,47,No +STU0262,Female,18,19,76.5,PhD,Yes,No,56,57,Yes +STU0263,Male,18,29,68.6,None,Yes,Yes,64,73,Yes +STU0264,Male,15,8,90.2,None,Yes,No,56,45,No +STU0265,Female,15,8,96.9,High School,No,Yes,56,49,No +STU0266,Male,19,12,88.8,PhD,No,No,69,51,Yes +STU0267,Female,19,30,74.9,High School,No,No,56,74,Yes +STU0268,Female,18,3,78.1,Bachelor,Yes,Yes,63,42,No +STU0269,Female,17,6,59.8,Bachelor,No,Yes,93,47,No +STU0270,Female,17,26,91.1,PhD,No,Yes,75,71,Yes +STU0271,Male,19,17,59.2,None,Yes,Yes,39,57,Yes +STU0272,Male,15,15,56.9,Bachelor,Yes,Yes,31,36,No +STU0273,Female,17,3,80.9,None,No,No,32,20,No +STU0274,Female,15,2,76.5,Master,No,Yes,94,51,Yes +STU0275,Female,16,5,95.9,None,Yes,Yes,54,51,Yes +STU0276,Female,17,10,89.7,High School,No,No,88,62,Yes +STU0277,Male,16,11,84.2,High School,Yes,Yes,81,54,Yes +STU0278,Female,19,17,52.8,High School,Yes,Yes,93,57,Yes +STU0279,Female,19,29,98.7,None,Yes,Yes,57,91,Yes +STU0280,Female,17,3,96.2,Master,Yes,Yes,70,32,No +STU0281,Male,19,6,87.6,PhD,Yes,No,50,48,No +STU0282,Male,19,28,92.5,None,No,No,47,89,Yes +STU0283,Male,19,25,98.7,High School,Yes,No,59,65,Yes +STU0284,Female,19,26,80.7,None,No,No,33,74,Yes +STU0285,Female,18,17,61.4,None,No,No,53,60,Yes +STU0286,Female,15,26,80.9,Bachelor,Yes,Yes,64,65,Yes +STU0287,Female,16,25,69.5,Master,Yes,Yes,30,58,Yes +STU0288,Female,17,26,63.5,Bachelor,Yes,Yes,85,74,Yes +STU0289,Female,15,2,54.5,None,Yes,No,79,20,No +STU0290,Female,18,14,89.0,None,Yes,Yes,78,53,Yes +STU0291,Male,17,11,63.4,Bachelor,Yes,Yes,85,45,No +STU0292,Female,17,17,89.1,Master,No,Yes,93,74,Yes +STU0293,Male,18,5,56.8,High School,No,No,78,50,Yes +STU0294,Female,18,12,71.5,None,Yes,No,71,57,Yes +STU0295,Male,18,12,58.9,PhD,No,No,92,57,Yes +STU0296,Male,17,14,64.0,PhD,No,Yes,55,54,Yes +STU0297,Male,19,2,97.7,PhD,Yes,No,67,52,Yes +STU0298,Male,18,23,75.0,Master,Yes,No,49,73,Yes +STU0299,Female,18,4,72.5,None,No,No,94,53,Yes +STU0300,Female,19,3,89.5,Master,Yes,Yes,61,49,No +STU0301,Female,16,22,80.6,None,No,No,85,62,Yes +STU0302,Male,15,10,61.1,None,Yes,No,77,45,No +STU0303,Female,19,24,75.0,Master,Yes,No,43,61,Yes +STU0304,Female,15,5,91.6,Bachelor,No,No,73,48,No +STU0305,Male,19,17,67.4,High School,No,No,70,46,No +STU0306,Female,15,27,85.5,High School,No,Yes,51,84,Yes +STU0307,Male,19,7,77.6,Master,No,No,59,55,Yes +STU0308,Female,16,7,82.0,PhD,No,Yes,55,45,No +STU0309,Female,18,2,85.2,Bachelor,No,Yes,43,47,No +STU0310,Male,17,12,59.9,High School,No,No,70,53,Yes +STU0311,Female,19,14,79.4,Master,No,No,52,61,Yes +STU0312,Female,19,20,54.2,Bachelor,No,Yes,60,52,Yes +STU0313,Male,16,13,84.7,None,No,No,47,57,Yes +STU0314,Female,18,7,74.3,None,Yes,Yes,92,45,No +STU0315,Male,16,12,92.1,None,Yes,No,31,51,Yes +STU0316,Male,16,28,69.2,None,No,No,92,79,Yes +STU0317,Female,18,7,54.1,High School,Yes,No,34,26,No +STU0318,Male,19,11,58.4,PhD,No,Yes,44,35,No +STU0319,Female,19,28,87.8,PhD,Yes,No,43,84,Yes +STU0320,Male,17,29,100.0,PhD,No,No,41,80,Yes +STU0321,Male,16,8,90.5,None,No,No,51,53,Yes +STU0322,Male,16,28,60.3,High School,No,No,51,65,Yes +STU0323,Female,17,20,63.4,None,Yes,Yes,82,51,Yes +STU0324,Female,16,24,93.3,Bachelor,Yes,No,61,85,Yes +STU0325,Female,18,6,78.9,Master,No,No,87,37,No +STU0326,Male,18,18,87.5,Master,No,No,71,70,Yes +STU0327,Male,15,25,98.6,Master,Yes,No,57,76,Yes +STU0328,Male,18,10,66.1,Master,No,Yes,92,63,Yes +STU0329,Female,15,3,55.0,PhD,Yes,Yes,50,30,No +STU0330,Female,15,15,60.1,Bachelor,No,Yes,65,40,No +STU0331,Female,17,4,68.6,Bachelor,Yes,No,69,48,No +STU0332,Male,18,22,54.6,High School,Yes,Yes,91,54,Yes +STU0333,Male,19,9,76.0,PhD,Yes,Yes,73,38,No +STU0334,Female,16,17,53.5,PhD,Yes,Yes,43,45,No +STU0335,Female,17,6,69.1,Bachelor,Yes,Yes,31,39,No +STU0336,Male,18,13,86.1,Bachelor,No,No,57,43,No +STU0337,Male,16,26,56.2,PhD,No,No,70,79,Yes +STU0338,Male,16,10,82.2,Bachelor,No,Yes,68,44,No +STU0339,Male,18,11,82.3,Bachelor,No,No,44,41,No +STU0340,Female,19,4,76.4,High School,No,No,46,35,No +STU0341,Female,17,19,84.1,PhD,Yes,Yes,38,46,No +STU0342,Female,16,3,96.1,PhD,No,No,84,57,Yes +STU0343,Female,15,22,76.8,None,Yes,No,40,58,Yes +STU0344,Male,19,8,98.7,Bachelor,No,No,38,43,No +STU0345,Female,16,3,62.2,PhD,Yes,No,37,24,No +STU0346,Male,15,2,85.4,High School,Yes,No,74,44,No +STU0347,Female,16,13,56.3,Master,Yes,Yes,72,60,Yes +STU0348,Female,17,18,53.2,None,Yes,No,88,61,Yes +STU0349,Female,15,17,93.0,Bachelor,No,Yes,62,74,Yes +STU0350,Male,19,23,86.1,Bachelor,Yes,Yes,31,58,Yes +STU0351,Male,18,13,69.1,Bachelor,Yes,Yes,83,48,No +STU0352,Female,16,15,95.8,Master,No,Yes,77,54,Yes +STU0353,Male,16,9,90.2,High School,No,Yes,89,52,Yes +STU0354,Female,17,4,77.7,High School,No,Yes,67,45,No +STU0355,Female,19,9,73.2,None,No,Yes,61,36,No +STU0356,Male,18,2,61.6,Master,Yes,No,30,36,No +STU0357,Female,17,5,60.3,Bachelor,No,No,37,21,No +STU0358,Female,15,3,96.1,None,No,No,88,58,Yes +STU0359,Male,19,5,75.3,PhD,Yes,Yes,85,53,Yes +STU0360,Male,16,11,63.0,Master,No,No,70,48,No +STU0361,Female,16,4,97.2,Bachelor,Yes,Yes,43,33,No +STU0362,Female,18,12,70.3,None,No,Yes,87,50,Yes +STU0363,Female,17,29,56.0,Bachelor,No,Yes,57,66,Yes +STU0364,Male,15,24,62.4,Master,No,Yes,68,61,Yes +STU0365,Male,15,14,92.7,None,Yes,Yes,89,54,Yes +STU0366,Male,15,2,53.1,Bachelor,Yes,Yes,81,31,No +STU0367,Male,19,20,62.9,High School,Yes,Yes,55,64,Yes +STU0368,Female,16,4,95.3,Master,Yes,Yes,89,38,No +STU0369,Male,19,15,91.1,PhD,No,Yes,78,71,Yes +STU0370,Female,16,13,60.7,Bachelor,No,No,87,44,No +STU0371,Male,19,21,80.8,Master,No,No,91,59,Yes +STU0372,Male,17,14,56.9,Bachelor,Yes,Yes,78,63,Yes +STU0373,Female,18,19,89.1,Master,No,No,64,58,Yes +STU0374,Male,17,21,61.0,Master,Yes,No,75,73,Yes +STU0375,Male,16,5,62.4,Bachelor,No,Yes,58,40,No +STU0376,Female,17,28,99.6,Bachelor,Yes,Yes,70,95,Yes +STU0377,Female,19,2,85.2,Master,Yes,Yes,93,49,No +STU0378,Female,16,17,51.9,High School,Yes,Yes,32,48,No +STU0379,Female,15,12,95.9,Bachelor,Yes,No,52,67,Yes +STU0380,Female,15,2,57.4,Bachelor,Yes,No,39,24,No +STU0381,Female,19,5,66.8,Master,Yes,Yes,85,54,Yes +STU0382,Female,17,7,85.4,None,No,Yes,52,43,No +STU0383,Female,17,6,58.9,High School,No,Yes,53,41,No +STU0384,Female,19,9,82.0,High School,No,Yes,72,60,Yes +STU0385,Male,16,13,99.1,Bachelor,Yes,No,87,51,Yes +STU0386,Female,16,9,84.7,Master,No,Yes,30,30,No +STU0387,Female,18,25,71.0,Bachelor,No,No,78,70,Yes +STU0388,Female,15,28,74.0,None,Yes,Yes,87,68,Yes +STU0389,Male,17,2,65.9,High School,Yes,No,51,29,No +STU0390,Male,15,19,97.1,High School,No,No,33,62,Yes +STU0391,Male,19,30,71.7,High School,Yes,No,56,68,Yes +STU0392,Female,19,25,96.3,None,No,No,68,71,Yes +STU0393,Male,15,14,78.6,PhD,Yes,Yes,76,44,No +STU0394,Female,15,11,91.2,None,No,Yes,42,37,No +STU0395,Male,16,22,64.6,High School,No,Yes,69,76,Yes +STU0396,Female,18,17,94.7,Master,Yes,No,53,60,Yes +STU0397,Female,17,7,72.3,Master,No,No,63,36,No +STU0398,Female,19,3,57.6,High School,Yes,No,82,47,No +STU0399,Male,19,3,92.0,None,No,No,60,44,No +STU0400,Female,15,14,95.1,High School,Yes,Yes,87,56,Yes +STU0401,Male,16,27,59.6,High School,No,Yes,85,66,Yes +STU0402,Male,19,10,66.0,High School,Yes,No,80,52,Yes +STU0403,Female,17,18,72.9,High School,No,Yes,83,54,Yes +STU0404,Male,19,20,79.1,High School,Yes,Yes,66,68,Yes +STU0405,Female,18,10,69.2,PhD,No,Yes,52,39,No +STU0406,Female,16,21,80.4,PhD,Yes,No,72,77,Yes +STU0407,Male,15,25,73.8,Master,No,Yes,73,84,Yes +STU0408,Female,17,10,92.1,High School,Yes,Yes,68,64,Yes +STU0409,Female,17,10,86.4,None,Yes,Yes,86,49,No +STU0410,Female,18,26,75.8,Master,Yes,Yes,38,63,Yes +STU0411,Female,16,26,92.6,None,Yes,Yes,54,68,Yes +STU0412,Male,16,3,72.8,Master,Yes,No,77,40,No +STU0413,Female,15,23,51.5,Master,No,No,52,60,Yes +STU0414,Female,17,7,91.9,Master,Yes,No,33,41,No +STU0415,Male,16,29,78.6,Bachelor,Yes,Yes,56,71,Yes +STU0416,Female,19,2,93.2,High School,No,Yes,52,45,No +STU0417,Male,16,10,80.4,Master,No,No,78,50,Yes +STU0418,Female,17,8,73.3,None,No,Yes,30,40,No +STU0419,Female,19,22,79.7,Master,No,Yes,91,62,Yes +STU0420,Female,17,22,66.3,Master,No,No,54,52,Yes +STU0421,Male,15,27,92.9,None,No,No,47,85,Yes +STU0422,Male,18,11,63.0,PhD,No,No,34,45,No +STU0423,Male,17,25,85.7,None,No,No,94,76,Yes +STU0424,Male,19,7,64.7,PhD,Yes,No,53,46,No +STU0425,Female,15,9,67.8,Master,No,No,77,42,No +STU0426,Female,18,29,55.9,High School,Yes,No,54,72,Yes +STU0427,Male,18,18,64.9,High School,Yes,Yes,55,52,Yes +STU0428,Female,19,30,95.3,PhD,Yes,No,53,93,Yes +STU0429,Male,18,12,81.6,Bachelor,No,Yes,30,47,No +STU0430,Female,16,26,88.7,None,Yes,No,41,62,Yes +STU0431,Female,18,14,71.5,PhD,No,Yes,34,51,Yes +STU0432,Male,17,2,94.6,None,Yes,Yes,30,42,No +STU0433,Male,16,24,93.8,Master,Yes,Yes,79,80,Yes +STU0434,Female,16,4,91.2,Master,No,No,73,45,No +STU0435,Male,17,30,74.5,Bachelor,Yes,No,32,65,Yes +STU0436,Male,16,8,86.7,None,Yes,Yes,76,60,Yes +STU0437,Male,18,30,82.0,None,Yes,Yes,43,83,Yes +STU0438,Male,15,24,55.8,PhD,No,Yes,62,65,Yes +STU0439,Male,16,24,84.3,High School,No,No,42,75,Yes +STU0440,Female,19,21,85.0,PhD,Yes,Yes,66,58,Yes +STU0441,Male,18,8,80.5,Master,Yes,No,82,46,No +STU0442,Male,15,19,74.8,Bachelor,No,No,60,47,No +STU0443,Male,15,5,91.8,PhD,Yes,Yes,95,41,No +STU0444,Male,17,22,61.6,Master,No,No,36,46,No +STU0445,Male,15,29,64.5,Master,No,Yes,42,80,Yes +STU0446,Male,15,29,56.9,Bachelor,No,No,86,81,Yes +STU0447,Female,15,13,90.7,Bachelor,Yes,No,81,61,Yes +STU0448,Female,18,24,92.5,PhD,Yes,Yes,46,79,Yes +STU0449,Male,16,5,70.8,None,No,No,53,43,No +STU0450,Female,17,21,51.4,Bachelor,No,No,44,53,Yes +STU0451,Female,15,4,63.1,Master,Yes,Yes,73,40,No +STU0452,Male,18,23,86.3,PhD,No,No,49,72,Yes +STU0453,Male,15,22,96.7,None,No,No,51,59,Yes +STU0454,Female,17,9,66.5,Master,Yes,Yes,78,46,No +STU0455,Male,15,16,72.2,None,No,Yes,93,63,Yes +STU0456,Female,15,17,87.3,High School,Yes,No,82,54,Yes +STU0457,Male,15,27,80.2,None,No,Yes,88,77,Yes +STU0458,Female,15,28,61.1,PhD,No,No,76,82,Yes +STU0459,Female,15,5,88.0,Bachelor,Yes,No,75,53,Yes +STU0460,Female,19,18,68.7,High School,Yes,Yes,93,55,Yes +STU0461,Female,15,10,80.5,Bachelor,Yes,No,57,49,No +STU0462,Male,17,5,87.9,High School,Yes,No,88,57,Yes +STU0463,Male,18,2,76.5,High School,No,No,95,53,Yes +STU0464,Male,19,7,86.5,Bachelor,Yes,No,71,51,Yes +STU0465,Female,18,21,95.8,Bachelor,No,No,73,68,Yes +STU0466,Female,16,6,70.6,None,No,No,57,32,No +STU0467,Male,19,24,64.5,Master,Yes,Yes,94,76,Yes +STU0468,Female,15,26,67.6,PhD,No,No,84,86,Yes +STU0469,Male,19,5,51.3,Bachelor,Yes,Yes,55,26,No +STU0470,Male,17,23,62.5,High School,No,Yes,34,56,Yes +STU0471,Female,15,21,93.4,None,Yes,No,60,72,Yes +STU0472,Female,15,14,69.4,Master,Yes,Yes,62,45,No +STU0473,Female,16,22,85.2,Master,Yes,Yes,67,70,Yes +STU0474,Male,15,7,98.1,None,Yes,No,35,44,No +STU0475,Female,19,10,56.5,None,No,Yes,72,45,No +STU0476,Male,15,6,96.4,None,Yes,No,92,66,Yes +STU0477,Female,15,27,85.4,None,Yes,Yes,38,83,Yes +STU0478,Male,18,19,76.9,High School,No,Yes,57,68,Yes +STU0479,Female,17,13,64.7,None,No,Yes,44,53,Yes +STU0480,Female,18,9,95.2,None,Yes,Yes,71,53,Yes +STU0481,Male,18,10,77.0,Master,Yes,No,95,67,Yes +STU0482,Female,18,3,64.8,Bachelor,No,Yes,42,22,No +STU0483,Male,15,8,83.5,Bachelor,No,Yes,56,50,Yes +STU0484,Female,15,25,84.3,High School,No,No,34,56,Yes +STU0485,Male,15,7,89.7,None,Yes,Yes,63,48,No +STU0486,Female,18,22,70.2,Master,Yes,No,45,65,Yes +STU0487,Male,19,4,87.8,None,Yes,Yes,75,48,No +STU0488,Male,17,27,84.3,None,Yes,No,86,79,Yes +STU0489,Female,15,17,57.8,Bachelor,Yes,No,67,61,Yes +STU0490,Male,17,23,94.3,Master,Yes,Yes,45,57,Yes +STU0491,Male,19,21,69.7,None,Yes,No,56,71,Yes +STU0492,Female,16,15,95.3,None,Yes,Yes,53,53,Yes +STU0493,Female,16,5,59.1,None,Yes,No,39,27,No +STU0494,Male,17,5,59.9,None,No,No,75,29,No +STU0495,Male,15,11,81.0,Bachelor,No,No,87,53,Yes +STU0496,Female,19,6,78.3,Master,No,No,51,27,No +STU0497,Female,16,27,61.1,PhD,No,No,47,74,Yes +STU0498,Female,18,16,72.3,Master,No,Yes,52,61,Yes +STU0499,Male,17,29,91.3,None,Yes,No,39,86,Yes +STU0500,Male,15,29,75.4,High School,No,Yes,34,74,Yes diff --git a/notebooks/untitled.md b/notebooks/untitled.md new file mode 100644 index 0000000..e69de29 diff --git a/notebooks/zadanie3_analysis.ipynb b/notebooks/zadanie3_analysis.ipynb new file mode 100644 index 0000000..5265277 --- /dev/null +++ b/notebooks/zadanie3_analysis.ipynb @@ -0,0 +1,469 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c3704ebe-f754-4be6-b598-d39a676b347d", + "metadata": {}, + "source": [ + "# Анализ успеваемости студентов\n", + "## zadanie3 \n", + "\n", + "**Автор:** Ончурова Полина Алексеевна\n", + "**Группа:** ИНБб-2301-02-00\n", + "**Датасет:** Student Academic Performance (500 студентов)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0a4d1888-e402-4843-91f1-661c7516540d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Анализ данных студентов\n" + ] + } + ], + "source": [ + "print('Анализ данных студентов')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "730583dd-5bc1-45a2-955f-2f3d725103a1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Библиотеки загружены\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "print('Библиотеки загружены')" + ] + }, + { + "cell_type": "markdown", + "id": "6e36c86d-681d-41a8-aa06-2f2796759ba4", + "metadata": {}, + "source": [ + "df = pd.read_csv(r\"C:\\учеба\\практика 2 курс\\3 задание\\data\\student_performance.csv\")\n", + "print('Размер данных: ', df.shape)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "eb70714a-f855-4915-95d2-f7bf4ce71a72", + "metadata": {}, + "source": [ + "## Анализ структуры данных (df.info())\n", + "\n", + "Метод `df.info()` показывает, что в датасете 500 строк и 11 колонок. \n", + "Типы данных: числовые (int64, float64) и текстовые (object). \n", + "В колонке `parent_education` 117 пропусков (указано 383 non-null из 500)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "81b2847d-2202-4aa4-8bc2-254c804d2648", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 500 entries, 0 to 499\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 student_id 500 non-null object \n", + " 1 gender 500 non-null object \n", + " 2 age 500 non-null int64 \n", + " 3 study_hours_per_week 500 non-null int64 \n", + " 4 attendance_rate 500 non-null float64\n", + " 5 parent_education 383 non-null object \n", + " 6 internet_access 500 non-null object \n", + " 7 extracurricular 500 non-null object \n", + " 8 previous_score 500 non-null int64 \n", + " 9 final_score 500 non-null int64 \n", + " 10 passed 500 non-null object \n", + "dtypes: float64(1), int64(4), object(6)\n", + "memory usage: 43.1+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "markdown", + "id": "67bc4841-2c25-4ed7-9d6f-91fdd8f45b48", + "metadata": {}, + "source": [ + "## Таблица данных\n", + "\n", + "Таблица показывает первые 5 строк датасета. Данные содержат 500 студентов и 11 признаков:\n", + "- `final_score` — итоговая оценка (целевая переменная)\n", + "- `study_hours_per_week` — часы учёбы в неделю\n", + "- `attendance_rate` — процент посещаемости\n", + "- `gender`, `age`, `parent_education` и другие демографические данные" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "90a0d670-9cc8-4055-b37c-8798e73ed0c1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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agestudy_hours_per_weekattendance_rateprevious_scorefinal_score
count500.000000500.000000500.000000500.000000500.000000
mean16.97800015.31200076.38060062.98600055.980000
std1.4344458.56816713.81768118.93745115.373754
min15.0000002.00000050.20000030.00000020.000000
25%16.0000008.00000064.47500046.00000045.000000
50%17.00000015.00000076.50000064.00000056.000000
75%18.00000023.00000088.52500079.00000068.000000
max19.00000030.000000100.00000095.00000095.000000
\n", + "
" + ], + "text/plain": [ + " age study_hours_per_week attendance_rate previous_score \\\n", + "count 500.000000 500.000000 500.000000 500.000000 \n", + "mean 16.978000 15.312000 76.380600 62.986000 \n", + "std 1.434445 8.568167 13.817681 18.937451 \n", + "min 15.000000 2.000000 50.200000 30.000000 \n", + "25% 16.000000 8.000000 64.475000 46.000000 \n", + "50% 17.000000 15.000000 76.500000 64.000000 \n", + "75% 18.000000 23.000000 88.525000 79.000000 \n", + "max 19.000000 30.000000 100.000000 95.000000 \n", + "\n", + " final_score \n", + "count 500.000000 \n", + "mean 55.980000 \n", + "std 15.373754 \n", + "min 20.000000 \n", + "25% 45.000000 \n", + "50% 56.000000 \n", + "75% 68.000000 \n", + "max 95.000000 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "5eed5732-e639-46c3-a529-983f6e29b689", + "metadata": {}, + "source": [ + "## График 1: Распределение итоговых оценок\n", + "\n", + "Гистограмма показывает, как часто встречаются различные баллы среди 500 студентов. Чтобы понимать общую успеваемость группы." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e69a1813-627f-4bf4-9c2c-4318cce44373", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "plt.hist(df['final_score'], bins=15, color='blue', edgecolor='black')\n", + "plt.xlabel('Итоговый балл')\n", + "plt.ylabel('Количество студентов')\n", + "plt.title('Распределение итоговых оценок')\n", + "plt.axvline(df['final_score'].mean(), color='red', linestyle='dashed', label=f'Среднее: {df[\"final_score\"].mean():.1f}')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "23107874-cdc1-4024-8636-856b3b8f5725", + "metadata": {}, + "source": [ + "## График 2: Влияние часов учёбы на оценки\n", + "\n", + "На этом графике каждая точка — студент. \n", + "По горизонтали — часы учёбы в неделю, по вертикали — итоговый балл. \n", + "Если точки образуют восходящую линию — значит, больше учёбы ведёт к более высоким оценкам." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0909212f-3cc9-4bd1-b5f6-4b87dd875fe8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Взаимосвязь между часами учёбы и оценкой: 0.804\n" + ] + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df['study_hours_per_week'], df['final_score'], alpha=0.6)\n", + "plt.xlabel('Часы учёбы в неделю')\n", + "plt.ylabel('Итоговый балл')\n", + "plt.title('Влияние часов учёбы на оценки')\n", + "plt.grid(True, alpha=0.3)\n", + "plt.show()\n", + "\n", + "corr = df['study_hours_per_week'].corr(df['final_score'])\n", + "print(f\"Взаимосвязь между часами учёбы и оценкой: {corr:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "484be79f-569b-4ff7-8dcd-8ca84fd55433", + "metadata": {}, + "source": [ + "## График 3: Сравнение успеваемости по полу\n", + "\n", + "«Ящик с усами» (Boxplot) помогает увидеть середину данных (медиану), \n", + "их основную часть (квартили) и значения, которые сильно отличаются от остальных (выбросы).\n", + "Сравнивая два ящика (Male и Female), можно увидеть, есть ли разница в успеваемости между мужчинами и женщинами." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "32383c64-64c3-485f-ac28-2c5162c5fcd6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "sns.boxplot(x='gender', y='final_score', data=df)\n", + "plt.xlabel('Пол')\n", + "plt.ylabel('Итоговый балл')\n", + "plt.title('Распределение оценок по полу')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f5c174bc-063c-4b73-b7be-02e2be3c3aca", + "metadata": {}, + "source": [ + "## График 4: Влияние образования родителей на оценки\n", + "\n", + "Boxplot показывает распределение итоговых баллов в зависимости от уровня образования родителей (Bachelor, High School, Master, phD). \n", + "Чем выше образование родителей, тем выше медианная оценка студентов. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "846a8c8f-c801-4687-9573-4c22cb9d13cf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "sns.boxplot(x='parent_education', y='final_score', data=df)\n", + "plt.xlabel('Образование родителей')\n", + "plt.ylabel('Итоговый балл')\n", + "plt.title('Влияние образования родителей на оценки')\n", + "plt.xticks(rotation=45)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "33376bab-080c-4c1f-94fa-a91613ac60f0", + "metadata": {}, + "source": [ + "## Общие выводы по анализу\n", + "\n", + "1. **Распределение оценок** — большинство студентов набирают 50-80 баллов, что говорит о нормальной успеваемости.\n", + "2. **Часы учёбы** — наблюдается положительная корреляция: чем больше студент занимается, тем выше его оценки.\n", + "3. **Пол** — значительных различий в успеваемости между мужчинами и женщинами не обнаружено.\n", + "4. **Данные** — чистые, без пропусков, пригодны для дальнейшего машинного обучения." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d1e70f6-086c-4e89-8f69-a2ce0cea85fb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}