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## Лабораторная работа №5
### Регрессия
### Задание
Использовать регрессию по варианту для данных из таблицы 1 по варианту(таблица 10),самостоятельно сформулировав задачу. Оценить, насколько хорошо онаподходит для решения сформулированной вами задачи.
### Как запустить лабораторную работу:
* установить python, numpy, matplotlib, sklearn
* запустить проект при помощи IDE pyCharm
### Какие технологии использовались:
* Язык программирования `Python`, библиотеки matplotlib, sklearn, pandas
* Среда разработки `PyCharm`
### Что делает лабораторная работа:
Программа реализует модель логистической регрессии для предсказания пола (мужской/женский) на основе уровня активности, веса и роста. Она обучается на тренировочных данных, затем делает предсказания на тестовом наборе, вычисляет метрики оценки модели (точность, матрицу ошибок, отчет о классификации) и визуализирует результаты на диаграмме рассеяния.
### Результаты:
![img.png](img.png)
Accuracy (Точность): 80% - доля правильных предсказаний;
Confusion Matrix (Матрица ошибок):
True Positive (TP): 10 - правильно предсказанные Female;
True Negative (TN): 6 - правильно предсказанные Male;
False Positive (FP): 3 - ошибочно предсказанные Female (вместо Male);
False Negative (FN): 1 - ошибочно предсказанные Male (вместо Female);
Classification Report (Отчет о классификации):
* Precision (Точность) для Female: 91% - доля правильно предсказанных Female относительно всех предсказанных Female;
* Recall (Полнота) для Female: 77% - доля правильно предсказанных Female относительно всех реальных Female;
* F1-score для Female: 83% - среднее гармоническое между точностью и полнотой для Female;
* Поддержка (support) для Female: 13 - количество образцов Female в тестовом наборе;
* Аналогичные метрики для Male также представлены.
### Вывод:
Модель логистической регрессии, обученная на данных об уровне активности, весе и росте, достигла точности 80%. Она успешно предсказывает пол (Female/Male) с высокой точностью и полнотой, как указано в матрице ошибок и отчете о классификации.

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verina_daria_lab_5/main.py Normal file
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import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
# Загрузка данных
data = pd.read_csv('person_types.csv')
# Выбор переменных для модели
features = ['ACTIVITY_LEVEL', 'WEIGHT', 'HEIGHT']
# Отбор нужных столбцов
df = data[features + ['SEX']]
# Удаление строк с пропущенными значениями
df = df.dropna()
# Преобразование строковых значений в числа
le = LabelEncoder()
df['ACTIVITY_LEVEL'] = le.fit_transform(df['ACTIVITY_LEVEL'])
# Разделение на признаки и целевую переменную
X = df.drop('SEX', axis=1)
y = df['SEX']
# Разделение на тренировочный и тестовый наборы
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Стандартизация признаков
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# Создание и обучение логистической регрессии
model = LogisticRegression()
model.fit(X_train_scaled, y_train)
# Предсказание на тестовом наборе
y_pred = model.predict(X_test_scaled)
# Оценка качества модели
accuracy = accuracy_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)
class_report = classification_report(y_test, y_pred)
print(f'Accuracy: {accuracy}')
print(f'Confusion Matrix:\n{conf_matrix}')
print(f'Classification Report:\n{class_report}')
# Визуализация результатов
plt.scatter(X_test['WEIGHT'], y_test, color='black', label='Actual')
plt.scatter(X_test['WEIGHT'], y_pred, color='blue', label='Predicted', marker='x')
plt.xlabel('Weight')
plt.ylabel('Sex')
plt.legend()
plt.show()

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verina_daria_lab_5/person_types.csv Normal file
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S_No,AGE,HEIGHT,WEIGHT,SEX,ACTIVITY_LEVEL,PAIN_1,PAIN_2,PAIN_3,PAIN_4,MBTI,E,I,S,N,T,F,J,P,POSTURE
1,53,62,125,Female,Low,0,0,0,0,ESFJ,18,3,17,9,9,13,18,4,A
2,52,69,157,Male,High,7,8,5,3,ISTJ,6,15,14,12,21,3,13,9,B
3,30,69,200,Male,High,0,0,0,0,ESTJ,15,6,16,10,15,9,12,10,A
4,51,66,175,Male,Moderate,9.5,9.5,9.5,1.5,ISTJ,6,15,21,5,13,11,19,3,D
5,45,63,199,Female,Moderate,4,5,2,2,ENFJ,14,7,20,6,9,15,16,6,A
6,68,74,182,Male,Low,0,2.5,1.5,0,ISFP,4,17,17,9,11,13,4,18,D
7,62,68,263,Male,Low,7,10,10,10,ISTP,7,14,20,6,14,10,9,13,B
8,65,61,143,Female,Low,0,9,5,10,ESTJ,17,4,17,9,19,5,17,5,D
9,66,67,180,Male,Low,0.5,3.5,0.5,9.5,ESFJ,19,2,18,8,11,13,13,9,C
10,58,69,165,Male,Low,0,7.5,7,3,INFJ,5,16,13,13,11,13,17,5,D
11,61,67,210,Male,Low,5,0,0,9,ENTP,11,10,6,20,16,8,10,12,B
12,33,62,120,Female,Low,0,3,0,0,ISFJ,2,19,22,4,9,15,12,10,B
13,48,64,127,Female,Low,5,7,8,7,ESFJ,13,8,14,12,9,15,14,8,D
14,57,68,185,Male,Low,2.5,7.5,1.5,5.5,ENTJ,16,5,12,14,15,9,17,5,B
15,30,69,190,Male,Moderate,0,0,4,7,ESTP,20,1,22,4,13,11,7,15,A
16,62,71,165,Male,Low,0,1,0,0,ISTJ,2,19,14,12,22,2,19,3,D
17,59,66,138,Female,Low,6,3,6,6,ESTJ,12,9,18,8,14,10,20,2,A
18,33,72,171,Male,Moderate,4,9,8,0,ENFP,17,4,10,16,11,13,4,18,B
19,23,65,110,Female,Low,4.5,8.5,0,0,ESFP,13,8,15,11,12,12,9,13,B
20,48,63,154,Female,Low,8,8,6,9,ISFP,9,13,16,10,11,13,9,13,C
21,63,66,185,Female,Low,8,0,3,7,INFP,2,19,10,16,10,14,11,11,D
22,64,69,176,Male,Low,0.5,0.5,0.5,0.5,ESTP,13,8,25,1,18,6,8,14,B
23,71,68,156,Female,Low,2,8,0,6,ESTP,13,8,15,11,16,8,7,15,B
24,71,61,140,Female,Low,8,3,3,8,ESFJ,12,9,23,3,5,19,16,6,D
25,50,60,118,Female,Moderate,7,5,2,0,INFJ,2,19,11,15,1,23,12,10,C
26,41,65,133,Female,Low,7,4,0,0,ISFJ,3,18,20,6,5,19,14,9,C
27,36,68,165,Male,High,0,2,0,0,ESTP,16,5,20,6,15,9,7,15,A
28,40,62,113,Female,Low,2,3,0,0,ISTP,7,14,15,11,14,10,6,16,D
29,26,65,150,Male,Low,1,0,1,2,ENTJ,15,6,12,14,13,11,19,3,D
30,43,63,175,Male,Moderate,0,0,0,5,ENFP,21,0,5,21,1,23,0,22,A
31,46,66,222,Male,Low,0,0,0,8,ESFP,18,3,14,12,12,12,2,20,A
32,47,69,152,Male,Low,0,0,3.5,5.5,ESTJ,12,9,19,7,14,10,17,5,D
33,20,67,135,Male,Low,0,7,0,0,ESTP,15,6,14,12,14,10,3,19,B
34,34,73,200,Male,Low,0,8,6,0,ENTJ,21,0,6,20,15,9,16,6,C
35,54,62,190,Female,Low,0,5,4,0,ISFP,4,17,21,5,9,15,4,18,D
36,66,63,142,Female,Low,2,0,0,0,ISTJ,4,17,16,10,17,7,18,4,C
37,42,68,170,Male,High,0,10,0,0,ENFJ,17,4,10,16,8,16,15,7,C
38,52,66,222,Male,Low,3,5,4,3,ENFP,17,4,5,21,1,23,2,20,A
39,61,63,155,Male,Low,3.5,3.5,3.5,6,ESTP,18,3,23,3,15,9,6,16,B
40,49,69,190,Male,Low,2,9,5,3,ENFJ,13,8,10,16,8,16,20,2,B
41,17,69,145,Male,Moderate,0,7,2,0,INFP,9,12,8,18,6,18,11,11,C
42,57,70,160,Male,Low,0,7,0,0,ISFP,6,14,17,9,12,12,11,11,D
43,82,60,159,Male,Low,0,0,0,0,ESFP,21,0,20,6,6,18,2,20,A
44,48,61,138,Female,Low,0,0,0,0,ENFP,21,0,10,16,4,20,1,21,A
45,80,59,115,Female,Low,0,0,0,0,ENFP,19,2,11,15,6,18,3,19,A
46,66,66,184,Male,Low,4,3,0,0,ISTJ,10,11,18,8,21,3,14,8,D
47,63,62,150,Female,Low,2,10,0,4,ISFP,9,12,20,6,0,24,4,18,D
48,13,62,127,Female,Low,3,2,2,5,ENFJ,21,0,7,17,2,21,13,9,B
49,20,68,155,Male,Low,1,4,1,2,ESTP,16,5,17,9,15,9,10,12,B
50,18,66,150,Female,Low,3,3,5,6,INFJ,7,12,13,13,8,16,18,4,B
51,56,62,130,Female,Low,0,2,0,0,ESFP,18,3,21,4,5,19,10,12,C
52,56,64,165,Male,Low,0,5,0,0,ENTP,16,5,12,14,16,8,4,17,B
53,50,65,172,Female,Low,0,0,0,3,ESFP,14,7,19,7,1,23,5,17,A
54,19,68,113,Female,Low,0,4,6,2,INFJ,7,14,18,8,12,12,14,8,D
55,47,63,128,Female,Low,7.5,4,3,7.5,ESFJ,16,5,16,10,10,14,14,8,B
56,62,61,150,Female,Low,2,2,2,2,ESTJ,17,4,20,6,8,16,16,6,B
57,29,68,145,Female,Moderate,0,9,2,0,ESFP,19,2,16,10,4,20,6,16,B
58,20,70,250,Male,Low,4,8,2,5,ESFP,17,4,14,12,7,17,4,18,B
59,43,62,160,Female,Low,0,5,0,0,ISFJ,9,12,22,4,5,19,18,4,C
60,43,64,183,Male,Low,0,3,4,0,ISTP,4,17,15,11,15,9,9,13,B
61,42,63,166,Female,Low,0,0,0,8,ESFJ,21,0,20,6,3,21,16,6,B
62,25,67,160,Male,Low,5,0,8,0,ESTP,16,5,19,7,19,5,7,15,A
63,21,62,118,Female,Low,7.5,2.5,6.5,0,ESFP,11,10,19,7,10,14,5,17,B
64,28,62,200,Male,Low,0,8,0,5,ENTP,18,3,11,15,20,4,9,13,C
65,42,70,170,Male,Low,0,4,0,0,ESFP,18,3,17,9,5,19,6,16,A
66,18,62,110,Female,Low,4,5,0,0,ISTJ,4,17,22,4,13,11,20,2,B
67,24,73,155,Male,High,2,3,0,0,INTJ,5,16,9,17,14,10,17,5,C
68,39,70,200,Male,Moderate,0,3,0,0,ENTP,18,3,5,21,18,6,7,15,B
69,60,68,222,Male,Low,0.5,0.5,0.5,2.5,ISTP,7,14,19,7,15,9,3,19,C
70,37,60,105,Female,Low,2,8,2,6,INFP,9,12,9,17,4,20,2,20,D
71,30,67,135,Female,Low,5,5,0,1,ESFP,19,2,21,5,12,12,8,14,A
72,45,67,148,Male,Low,2,3,0,5,ISFJ,5,16,18,8,12,12,17,5,C
73,45,65,160,Female,Low,2,3,0,0,ENTJ,16,5,13,13,14,10,13,9,B
74,53,73,170,Male,Low,0,0,0,0,ESFJ,16,5,14,12,12,12,17,5,C
75,49,71,230,Male,Moderate,0,4,0,4,ESTJ,15,6,15,11,18,6,17,5,B
76,45,67,160,Female,Low,2,0,2,0,ISFJ,3,18,17,9,12,12,18,4,A
77,55,74,240,Male,Low,0,7,0,4,ESTP,16,5,14,12,17,7,11,11,C
78,45,64,118,Female,Moderate,0,4,0,4,ISFP,5,16,18,8,11,13,6,16,D
79,75,63,143,Female,Low,4,6,4,6,ESTJ,12,9,17,9,13,11,16,6,B
80,44,66,200,Female,Low,2,3,2,0,ENFP,18,3,9,17,5,19,11,11,B
81,48,71,145,Male,Low,0,7,4,0,INFP,3,18,11,15,6,18,4,18,C
82,49,64,155,Female,Low,4,7,3,5,ESFP,12,9,16,10,4,19,9,13,C
83,16,58,81,Female,Low,0,0,2,0,INFP,8,13,13,13,11,13,5,17,D
84,39,60,88,Female,Low,3,1,0,0,ESFP,13,8,12,14,7,16,5,17,B
85,19,68,125,Female,Low,0,3,0,0,ENTJ,18,3,9,17,6,18,12,10,D
86,11,60,68,Male,High,5,3,1,0,ENFP,20,1,7,19,3,21,0,22,A
87,55,65,198,Male,Low,4,2.5,3,8,ENTP,20,1,12,14,18,6,2,20,A
88,28,67,180,Female,Low,0,0,0,0,ESFJ,11,10,22,14,8,16,14,8,B
89,22,65,193,Female,Low,5,7,7,0,ESFJ,17,4,14,12,7,17,15,7,B
90,56,67,150,Female,Low,0,7,0,0,ISFP,9,12,15,11,4,20,5,17,C
91,29,65,125,Female,Moderate,2,0,0,4,ENFP,19,2,13,13,12,12,10,12,A
92,16,69,130,Female,Moderate,5,0,5,7,ENFJ,19,2,9,17,2,22,12,10,B
93,16,58,100,Male,Moderate,0,0,0,3,ESTP,19,2,22,4,19,5,2,20,B
94,45,62,134,Female,Moderate,0,4,0,0,ESFJ,11,10,17,9,6,18,13,9,B
95,43,69,188,Male,Moderate,2,0,0,0,ENFP,12,9,9,17,6,18,2,20,A
96,28,67,180,Female,Low,0,0,0,0,ESFJ,11,10,22,14,8,16,14,8,B
97,43,69,188,Male,Moderate,4,0,0,0,ENFP,12,9,9,17,6,18,2,20,A
1 S_No AGE HEIGHT WEIGHT SEX ACTIVITY_LEVEL PAIN_1 PAIN_2 PAIN_3 PAIN_4 MBTI E I S N T F J P POSTURE
2 1 53 62 125 Female Low 0 0 0 0 ESFJ 18 3 17 9 9 13 18 4 A
3 2 52 69 157 Male High 7 8 5 3 ISTJ 6 15 14 12 21 3 13 9 B
4 3 30 69 200 Male High 0 0 0 0 ESTJ 15 6 16 10 15 9 12 10 A
5 4 51 66 175 Male Moderate 9.5 9.5 9.5 1.5 ISTJ 6 15 21 5 13 11 19 3 D
6 5 45 63 199 Female Moderate 4 5 2 2 ENFJ 14 7 20 6 9 15 16 6 A
7 6 68 74 182 Male Low 0 2.5 1.5 0 ISFP 4 17 17 9 11 13 4 18 D
8 7 62 68 263 Male Low 7 10 10 10 ISTP 7 14 20 6 14 10 9 13 B
9 8 65 61 143 Female Low 0 9 5 10 ESTJ 17 4 17 9 19 5 17 5 D
10 9 66 67 180 Male Low 0.5 3.5 0.5 9.5 ESFJ 19 2 18 8 11 13 13 9 C
11 10 58 69 165 Male Low 0 7.5 7 3 INFJ 5 16 13 13 11 13 17 5 D
12 11 61 67 210 Male Low 5 0 0 9 ENTP 11 10 6 20 16 8 10 12 B
13 12 33 62 120 Female Low 0 3 0 0 ISFJ 2 19 22 4 9 15 12 10 B
14 13 48 64 127 Female Low 5 7 8 7 ESFJ 13 8 14 12 9 15 14 8 D
15 14 57 68 185 Male Low 2.5 7.5 1.5 5.5 ENTJ 16 5 12 14 15 9 17 5 B
16 15 30 69 190 Male Moderate 0 0 4 7 ESTP 20 1 22 4 13 11 7 15 A
17 16 62 71 165 Male Low 0 1 0 0 ISTJ 2 19 14 12 22 2 19 3 D
18 17 59 66 138 Female Low 6 3 6 6 ESTJ 12 9 18 8 14 10 20 2 A
19 18 33 72 171 Male Moderate 4 9 8 0 ENFP 17 4 10 16 11 13 4 18 B
20 19 23 65 110 Female Low 4.5 8.5 0 0 ESFP 13 8 15 11 12 12 9 13 B
21 20 48 63 154 Female Low 8 8 6 9 ISFP 9 13 16 10 11 13 9 13 C
22 21 63 66 185 Female Low 8 0 3 7 INFP 2 19 10 16 10 14 11 11 D
23 22 64 69 176 Male Low 0.5 0.5 0.5 0.5 ESTP 13 8 25 1 18 6 8 14 B
24 23 71 68 156 Female Low 2 8 0 6 ESTP 13 8 15 11 16 8 7 15 B
25 24 71 61 140 Female Low 8 3 3 8 ESFJ 12 9 23 3 5 19 16 6 D
26 25 50 60 118 Female Moderate 7 5 2 0 INFJ 2 19 11 15 1 23 12 10 C
27 26 41 65 133 Female Low 7 4 0 0 ISFJ 3 18 20 6 5 19 14 9 C
28 27 36 68 165 Male High 0 2 0 0 ESTP 16 5 20 6 15 9 7 15 A
29 28 40 62 113 Female Low 2 3 0 0 ISTP 7 14 15 11 14 10 6 16 D
30 29 26 65 150 Male Low 1 0 1 2 ENTJ 15 6 12 14 13 11 19 3 D
31 30 43 63 175 Male Moderate 0 0 0 5 ENFP 21 0 5 21 1 23 0 22 A
32 31 46 66 222 Male Low 0 0 0 8 ESFP 18 3 14 12 12 12 2 20 A
33 32 47 69 152 Male Low 0 0 3.5 5.5 ESTJ 12 9 19 7 14 10 17 5 D
34 33 20 67 135 Male Low 0 7 0 0 ESTP 15 6 14 12 14 10 3 19 B
35 34 34 73 200 Male Low 0 8 6 0 ENTJ 21 0 6 20 15 9 16 6 C
36 35 54 62 190 Female Low 0 5 4 0 ISFP 4 17 21 5 9 15 4 18 D
37 36 66 63 142 Female Low 2 0 0 0 ISTJ 4 17 16 10 17 7 18 4 C
38 37 42 68 170 Male High 0 10 0 0 ENFJ 17 4 10 16 8 16 15 7 C
39 38 52 66 222 Male Low 3 5 4 3 ENFP 17 4 5 21 1 23 2 20 A
40 39 61 63 155 Male Low 3.5 3.5 3.5 6 ESTP 18 3 23 3 15 9 6 16 B
41 40 49 69 190 Male Low 2 9 5 3 ENFJ 13 8 10 16 8 16 20 2 B
42 41 17 69 145 Male Moderate 0 7 2 0 INFP 9 12 8 18 6 18 11 11 C
43 42 57 70 160 Male Low 0 7 0 0 ISFP 6 14 17 9 12 12 11 11 D
44 43 82 60 159 Male Low 0 0 0 0 ESFP 21 0 20 6 6 18 2 20 A
45 44 48 61 138 Female Low 0 0 0 0 ENFP 21 0 10 16 4 20 1 21 A
46 45 80 59 115 Female Low 0 0 0 0 ENFP 19 2 11 15 6 18 3 19 A
47 46 66 66 184 Male Low 4 3 0 0 ISTJ 10 11 18 8 21 3 14 8 D
48 47 63 62 150 Female Low 2 10 0 4 ISFP 9 12 20 6 0 24 4 18 D
49 48 13 62 127 Female Low 3 2 2 5 ENFJ 21 0 7 17 2 21 13 9 B
50 49 20 68 155 Male Low 1 4 1 2 ESTP 16 5 17 9 15 9 10 12 B
51 50 18 66 150 Female Low 3 3 5 6 INFJ 7 12 13 13 8 16 18 4 B
52 51 56 62 130 Female Low 0 2 0 0 ESFP 18 3 21 4 5 19 10 12 C
53 52 56 64 165 Male Low 0 5 0 0 ENTP 16 5 12 14 16 8 4 17 B
54 53 50 65 172 Female Low 0 0 0 3 ESFP 14 7 19 7 1 23 5 17 A
55 54 19 68 113 Female Low 0 4 6 2 INFJ 7 14 18 8 12 12 14 8 D
56 55 47 63 128 Female Low 7.5 4 3 7.5 ESFJ 16 5 16 10 10 14 14 8 B
57 56 62 61 150 Female Low 2 2 2 2 ESTJ 17 4 20 6 8 16 16 6 B
58 57 29 68 145 Female Moderate 0 9 2 0 ESFP 19 2 16 10 4 20 6 16 B
59 58 20 70 250 Male Low 4 8 2 5 ESFP 17 4 14 12 7 17 4 18 B
60 59 43 62 160 Female Low 0 5 0 0 ISFJ 9 12 22 4 5 19 18 4 C
61 60 43 64 183 Male Low 0 3 4 0 ISTP 4 17 15 11 15 9 9 13 B
62 61 42 63 166 Female Low 0 0 0 8 ESFJ 21 0 20 6 3 21 16 6 B
63 62 25 67 160 Male Low 5 0 8 0 ESTP 16 5 19 7 19 5 7 15 A
64 63 21 62 118 Female Low 7.5 2.5 6.5 0 ESFP 11 10 19 7 10 14 5 17 B
65 64 28 62 200 Male Low 0 8 0 5 ENTP 18 3 11 15 20 4 9 13 C
66 65 42 70 170 Male Low 0 4 0 0 ESFP 18 3 17 9 5 19 6 16 A
67 66 18 62 110 Female Low 4 5 0 0 ISTJ 4 17 22 4 13 11 20 2 B
68 67 24 73 155 Male High 2 3 0 0 INTJ 5 16 9 17 14 10 17 5 C
69 68 39 70 200 Male Moderate 0 3 0 0 ENTP 18 3 5 21 18 6 7 15 B
70 69 60 68 222 Male Low 0.5 0.5 0.5 2.5 ISTP 7 14 19 7 15 9 3 19 C
71 70 37 60 105 Female Low 2 8 2 6 INFP 9 12 9 17 4 20 2 20 D
72 71 30 67 135 Female Low 5 5 0 1 ESFP 19 2 21 5 12 12 8 14 A
73 72 45 67 148 Male Low 2 3 0 5 ISFJ 5 16 18 8 12 12 17 5 C
74 73 45 65 160 Female Low 2 3 0 0 ENTJ 16 5 13 13 14 10 13 9 B
75 74 53 73 170 Male Low 0 0 0 0 ESFJ 16 5 14 12 12 12 17 5 C
76 75 49 71 230 Male Moderate 0 4 0 4 ESTJ 15 6 15 11 18 6 17 5 B
77 76 45 67 160 Female Low 2 0 2 0 ISFJ 3 18 17 9 12 12 18 4 A
78 77 55 74 240 Male Low 0 7 0 4 ESTP 16 5 14 12 17 7 11 11 C
79 78 45 64 118 Female Moderate 0 4 0 4 ISFP 5 16 18 8 11 13 6 16 D
80 79 75 63 143 Female Low 4 6 4 6 ESTJ 12 9 17 9 13 11 16 6 B
81 80 44 66 200 Female Low 2 3 2 0 ENFP 18 3 9 17 5 19 11 11 B
82 81 48 71 145 Male Low 0 7 4 0 INFP 3 18 11 15 6 18 4 18 C
83 82 49 64 155 Female Low 4 7 3 5 ESFP 12 9 16 10 4 19 9 13 C
84 83 16 58 81 Female Low 0 0 2 0 INFP 8 13 13 13 11 13 5 17 D
85 84 39 60 88 Female Low 3 1 0 0 ESFP 13 8 12 14 7 16 5 17 B
86 85 19 68 125 Female Low 0 3 0 0 ENTJ 18 3 9 17 6 18 12 10 D
87 86 11 60 68 Male High 5 3 1 0 ENFP 20 1 7 19 3 21 0 22 A
88 87 55 65 198 Male Low 4 2.5 3 8 ENTP 20 1 12 14 18 6 2 20 A
89 88 28 67 180 Female Low 0 0 0 0 ESFJ 11 10 22 14 8 16 14 8 B
90 89 22 65 193 Female Low 5 7 7 0 ESFJ 17 4 14 12 7 17 15 7 B
91 90 56 67 150 Female Low 0 7 0 0 ISFP 9 12 15 11 4 20 5 17 C
92 91 29 65 125 Female Moderate 2 0 0 4 ENFP 19 2 13 13 12 12 10 12 A
93 92 16 69 130 Female Moderate 5 0 5 7 ENFJ 19 2 9 17 2 22 12 10 B
94 93 16 58 100 Male Moderate 0 0 0 3 ESTP 19 2 22 4 19 5 2 20 B
95 94 45 62 134 Female Moderate 0 4 0 0 ESFJ 11 10 17 9 6 18 13 9 B
96 95 43 69 188 Male Moderate 2 0 0 0 ENFP 12 9 9 17 6 18 2 20 A
97 96 28 67 180 Female Low 0 0 0 0 ESFJ 11 10 22 14 8 16 14 8 B
98 97 43 69 188 Male Moderate 4 0 0 0 ENFP 12 9 9 17 6 18 2 20 A