Лабораторная работа 7

Reviewed-on: http://student.git.athene.tech/Alexey/IIS_2023_1/pulls/137

.gitignore

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+### Python template
+# Byte-compiled / optimized / DLL files
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+*.py[cod]
+*$py.class
+
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+*.so
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+.Python
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+.installed.cfg
+*.egg
+MANIFEST
+
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+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
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+.pytest_cache/
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+
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+profile_default/
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+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
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+
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+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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+# SageMath parsed files
+*.sage.py
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+# pytype static type analyzer
+.pytype/
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+# Cython debug symbols
+cython_debug/
+
+.idea

abanin_daniil_lab_7/README.md

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+# Лабораторная работа №7
+
+### Рекуррентная нейронная сеть и задача генерации текста
+
+## ПИбд-41 Абанин Даниил
+
+### Как запустить лабораторную работу:
+
+* установить python, numpy, keras, tensorflow
+* запустить проект (стартовая точка lab7)
+
+### Какие технологии использовались:
+
+* Язык программирования `Python`, библиотеки numpy, keras, tensorflow
+* Среда разработки `PyCharm`
+
+### Что делает лабораторная работа:
+* На основе выбранных художественных текстов происходит обучение рекуррентной нейронной сети для решения задачи генерации. 
+* Необходимо подобрать архитектуру и параметры так, чтобы приблизиться к максимально осмысленному результату.
+
+### Тест
+* Чтение текста из файлов .txt (eng_text.txt, rus_text.txt)
+* Вызов функция get_model_data, из которой мы получаем входные, выходные данные (X, y), размер словаря и токенайзер. Используем Tokenizer с настройкой char_level=True, что позволяет упразднить использование Embedding слоя далее
+* Создание объекта Sequential (последовательная рекуррентная нейронная сеть) и добавление двух слоёв LSTM. LSTM (Long Short-Term Memory) представляет собой разновидность рекуррентной нейронной сети, которая эффективно работает с последовательными данными. Использование нескольких слоёв даёт большую гибкость. Dropout — это метод регуляризации для нейронных сетей и моделей глубокого обучения, решение проблемы переобучения. Слой Dense с функцией активации softmax используется для предсказания следующего слова
+* Компилирование модели с использованием sparse_categorical_crossentropy
+* Обучение модели на 100 эпохах (оптимальный вариант)
+* Генерация текста
+
+Сгенерированные тексты
+
+* ENG: I must be getting somewhere near the centre of the earth. how funny it'll seem to come out among the people that walk with their heads downward! the antipathies, i think—' (for, you see, alice had learnt several things of this
+
+* RUS: господин осматривал свою комнату, внесены были его пожитки: прежде всего чемодан из белой кожи, несколько поистасканный, показывавший, что был не в первый раз в дороге. чемодан внесли кучер селифан отправился на конюшню вози
+
+![Rus](result_rus.png)
+
+![Eng](result_eng.png)
+
+По итогу, программа способна сгенерировать осмысленный текст в каждом из случаев

abanin_daniil_lab_7/eng_text.txt

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+Either the well was very deep, or she fell very slowly, for she had plenty of time as she went down to look about her and to wonder what was going to happen next. First, she tried to look down and make out what she was coming to, but it was too dark to see anything; then she looked at the sides of the well, and noticed that they were filled with cupboards and book-shelves; here and there she saw maps and pictures hung upon pegs. She took down a jar from one of the shelves as she passed; it was labelled 'ORANGE MARMALADE', but to her great disappointment it was empty: she did not like to drop the jar for fear of killing somebody, so managed to put it into one of the cupboards as she fell past it.
+
+'Well!' thought Alice to herself, 'after such a fall as this, I shall think nothing of tumbling down stairs! How brave they'll all think me at home! Why, I wouldn't say anything about it, even if I fell off the top of the house!' (Which was very likely true.)
+
+Down, down, down. Would the fall NEVER come to an end! 'I wonder how many miles I've fallen by this time?' she said aloud. 'I must be getting somewhere near the centre of the earth. Let me see: that would be four thousand miles down, I think—' (for, you see, Alice had learnt several things of this sort in her lessons in the schoolroom, and though this was not a VERY good opportunity for showing off her knowledge, as there was no one to listen to her, still it was good practice to say it over) '—yes, that's about the right distance—but then I wonder what Latitude or Longitude I've got to?' (Alice had no idea what Latitude was, or Longitude either, but thought they were nice grand words to say.)
+
+Presently she began again. 'I wonder if I shall fall right THROUGH the earth! How funny it'll seem to come out among the people that walk with their heads downward! The Antipathies, I think—' (she was rather glad there WAS no one listening, this time, as it didn't sound at all the right word) '—but I shall have to ask them what the name of the country is, you know. Please, Ma'am, is this New Zealand or Australia?' (and she tried to curtsey as she spoke—fancy CURTSEYING as you're falling through the air!

abanin_daniil_lab_7/lab7.py

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+from keras import Sequential
+from keras.layers import LSTM, Dense, Dropout
+from keras.preprocessing.text import Tokenizer
+from keras.preprocessing.sequence import pad_sequences
+import numpy as np
+
+with open('rus_text.txt', 'r', encoding='utf-8') as file:
+    text = file.read()
+
+
+def create_sequences(text, seq_len):
+    sequences = []
+    next_chars = []
+    for i in range(0, len(text) - seq_len):
+        sequences.append(text[i:i + seq_len])
+        next_chars.append(text[i + seq_len])
+    return sequences, next_chars
+
+
+def get_model_data(seq_length):
+    tokenizer = Tokenizer(char_level=True)
+    tokenizer.fit_on_texts([text])
+
+    token_text = tokenizer.texts_to_sequences([text])[0]
+
+    sequences, next_chars = create_sequences(token_text, seq_length)
+
+    vocab_size = len(tokenizer.word_index) + 1
+    x = pad_sequences(sequences, maxlen=seq_length)
+    y = np.array(next_chars)
+
+    return x, y, vocab_size, tokenizer
+
+
+def model_build(model, vocab_size):
+    model.add(LSTM(256, input_shape=(seq_length, 1), return_sequences=True))
+    model.add(LSTM(128, input_shape=(seq_length, 1)))
+    model.add(Dropout(0.2, input_shape=(60,)))
+    model.add(Dense(vocab_size, activation='softmax'))
+
+    model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+
+# Функция для генерации текста
+def generate_text(seed_text, gen_length, tokenizer, model):
+    generated_text = seed_text
+
+    for _ in range(gen_length):
+        sequence = tokenizer.texts_to_sequences([seed_text])[0]
+        sequence = pad_sequences([sequence], maxlen=seq_length)
+        prediction = model.predict(sequence)[0]
+        predicted_index = np.argmax(prediction)
+        predicted_char = tokenizer.index_word[predicted_index]
+        generated_text += predicted_char
+        seed_text += predicted_char
+        seed_text = seed_text[1:]
+
+    return generated_text
+
+
+seq_length = 10
+seed_text = "господин осматривал свою"
+
+# Создание экземпляра Tokenizer и обучение на тексте
+
+X, y, vocab_size, tokenizer = get_model_data(seq_length)
+
+model = Sequential()
+
+model_build(model, vocab_size)
+
+model.fit(X, y, epochs=100, verbose=1)
+
+generated_text = generate_text(seed_text, 200, tokenizer, model)
+print(generated_text)

abanin_daniil_lab_7/rus_text.txt

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+В ворота гостиницы губернского города NN въехала довольно красивая рессорная небольшая бричка, в какой ездят холостяки: отставные подполковники, штабс-капитаны, помещики, имеющие около сотни душ крестьян, — словом, все те, которых называют господами средней руки. В бричке сидел господин, не красавец, но и не дурной наружности, ни слишком толст, ни слишком тонок; нельзя сказать, чтобы стар, однако ж и не так чтобы слишком молод. Въезд его не произвел в городе совершенно никакого шума и не был сопровожден ничем особенным; только два русские мужика, стоявшие у дверей кабака против гостиницы, сделали кое-какие замечания, относившиеся, впрочем, более к экипажу, чем к сидевшему в нем. «Вишь ты, — сказал один другому, — вон какое колесо! что ты думаешь, доедет то колесо, если б случилось, в Москву или не доедет?» — «Доедет», — отвечал другой. «А в Казань-то, я думаю, не доедет?» — «В Казань не доедет», — отвечал другой. Этим разговор и кончился. Да еще, когда бричка подъехала к гостинице, встретился молодой человек в белых канифасовых панталонах, весьма узких и коротких, во фраке с покушеньями на моду, из-под которого видна была манишка, застегнутая тульскою булавкою с бронзовым пистолетом. Молодой человек оборотился назад, посмотрел экипаж, придержал рукою картуз, чуть не слетевший от ветра, и пошел своей дорогой.
+Когда экипаж въехал на двор, господин был встречен трактирным слугою, или половым, как их называют в русских трактирах, живым и вертлявым до такой степени, что даже нельзя было рассмотреть, какое у него было лицо. Он выбежал проворно, с салфеткой в руке, весь длинный и в длинном демикотонном сюртуке со спинкою чуть не на самом затылке, встряхнул волосами и повел проворно господина вверх по всей деревянной галдарее показывать ниспосланный ему Богом покой. Покой был известного рода, ибо гостиница была тоже известного рода, то есть именно такая, как бывают гостиницы в губернских городах, где за два рубля в сутки проезжающие получают покойную комнату с тараканами, выглядывающими, как чернослив, из всех углов, и дверью в соседнее помещение, всегда заставленную комодом, где устроивается сосед, молчаливый и спокойный человек, но чрезвычайно любопытный, интересующийся знать о всех подробностях проезжающего. Наружный фасад гостиницы отвечал ее внутренности: она была очень длинна, в два этажа; нижний не был выщекатурен и оставался в темно-красных кирпичиках, еще более потемневших от лихих погодных перемен и грязноватых уже самих по себе; верхний был выкрашен вечною желтою краскою; внизу были лавочки с хомутами, веревками и баранками. В угольной из этих лавочек, или, лучше, в окне, помещался сбитенщик с самоваром из красной меди и лицом так же красным, как самовар, так что издали можно бы подумать, что на окне стояло два самовара, если б один самовар не был с черною как смоль бородою.
+Пока приезжий господин осматривал свою комнату, внесены были его пожитки: прежде всего чемодан из белой кожи, несколько поистасканный, показывавший, что был не в первый раз в дороге. Чемодан внесли кучер Селифан, низенький человек в тулупчике, и лакей Петрушка, малый лет тридцати, в просторном подержанном сюртуке, как видно с барского плеча, малый немного суровый на взгляд, с очень крупными губами и носом. Вслед за чемоданом внесен был небольшой ларчик красного дерева с штучными выкладками из карельской березы, сапожные колодки и завернутая в синюю бумагу жареная курица. Когда все это было внесено, кучер Селифан отправился на конюшню возиться около лошадей, а лакей Петрушка стал устраиваться в маленькой передней, очень темной конурке, куда уже успел притащить свою шинель и вместе с нею какой-то свой собственный запах, который был сообщен и принесенному вслед за тем мешку с разным лакейским туалетом. В этой конурке он приладил к стене узенькую трехногую кровать, накрыв ее небольшим подобием тюфяка, убитым и плоским, как блин, и, может быть, так же замаслившимся, как блин, который удалось ему вытребовать у хозяина гостиницы.

abanin_danill_lab_6/README.md

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+## Лабораторная работа №6
+
+### MLPClassifier
+
+## Cтудент группы ПИбд-41 Абанин Даниил
+
+### Как запустить лабораторную работу:
+
+* установить python, numpy, matplotlib, sklearn
+* запустить проект (lab6)
+
+### Какие технологии использовались:
+
+* Язык программирования `Python`, библиотеки numpy, matplotlib, sklearn
+* Среда разработки `PyCharm`
+
+### Что делает лабораторная работа:
+
+* По данным "Eligibility Prediction for Loan" решает задачу классификации, в которой необходимо выявить риски выдачи кредита. В качестве исходных данных используются признаки: 
+Credit_History - соответствие кредитной истории стандартам банка, ApplicantIncome - доход заявителя, LoanAmount - сумма кредитаб, Self_Employed - самозанятость (Да/Нет), Education - наличие образования, Married - заявитель женат/замужем (Да/Нет).
+
+### Примеры работы:
+
+#### Результаты:
+* Было проведено несколько прогонов на разном количестве итераций (200, 400, 600, 800, 1000)
+
+![Result](score_1.png)
+![Result](score_2.png)
+
+Средняя точность находится в диапазоне 50-60%, что является недостаточным значением. Увеличение итераций не дало значительного улучшения результата, 
+максиальный прирост составляет 10%
+
+
+![Result](result_mean.jpg)

abanin_danill_lab_6/lab6.py

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+from matplotlib import pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPClassifier
+import pandas as pd
+import numpy as np
+
+
+def test_iter(iters_num, x_train, x_test, y_train, y_test):
+
+    print("Количество итераций: ", iters_num)
+    scores = []
+
+    for i in range(10):
+        neuro = MLPClassifier(max_iter=iters_num)
+        neuro.fit(x_train, y_train.values.ravel())
+        score = neuro.score(x_test, y_test)
+        print(f'Оценка №{i + 1} - {score}')
+        scores.append(score)
+
+    mean_value = np.mean(scores)
+
+    print(f"Средняя оценка - {mean_value}")
+
+    return mean_value
+
+
+def start():
+    data = pd.read_csv('loan.csv')
+    x = data[['ApplicantIncome', 'LoanAmount', 'Credit_History', 'Self_Employed', 'Education', 'Married']]
+    y = data[['Loan_Status']]
+
+    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=42)
+
+    iters = [200, 400, 600, 800, 1000]
+    iters_means = []
+
+    for i in range(len(iters)):
+        mean_value = test_iter(iters[i], x_train, x_test, y_train, y_test)
+        iters_means.append(mean_value)
+
+    plt.figure(1, figsize=(16, 9))
+    plt.plot(iters, iters_means, c='r')
+    plt.show()
+
+
+start()

abanin_danill_lab_6/loan.csv

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+Loan_ID,Gender,Married,Dependents,Education,Self_Employed,ApplicantIncome,CoapplicantIncome,LoanAmount,Loan_Amount_Term,Credit_History,Property_Area,Loan_Status
+LP001002,Male,0.0,0,1,0.0,5849,0.0,360.0,1.0,0,Y,0.0
+LP001003,Male,1.0,1,1,0.0,4583,1508.0,128.0,360.0,1,Rural,0.0
+LP001005,Male,1.0,0,1,1.0,3000,0.0,66.0,360.0,1,Urban,1.0
+LP001006,Male,1.0,0,0,0.0,2583,2358.0,120.0,360.0,1,Urban,1.0
+LP001008,Male,0.0,0,1,0.0,6000,0.0,141.0,360.0,1,Urban,1.0
+LP001011,Male,1.0,2,1,1.0,5417,4196.0,267.0,360.0,1,Urban,1.0
+LP001013,Male,1.0,0,0,0.0,2333,1516.0,95.0,360.0,1,Urban,1.0
+LP001014,Male,1.0,3+,1,0.0,3036,2504.0,158.0,360.0,0,Semiurban,0.0
+LP001018,Male,1.0,2,1,0.0,4006,1526.0,168.0,360.0,1,Urban,1.0
+LP001020,Male,1.0,1,1,0.0,12841,10968.0,349.0,360.0,1,Semiurban,0.0
+LP001024,Male,1.0,2,1,0.0,3200,700.0,70.0,360.0,1,Urban,1.0
+LP001027,Male,1.0,2,1,0.0,2500,1840.0,109.0,360.0,1,Urban,1.0
+LP001028,Male,1.0,2,1,0.0,3073,8106.0,200.0,360.0,1,Urban,1.0
+LP001029,Male,0.0,0,1,0.0,1853,2840.0,114.0,360.0,1,Rural,0.0
+LP001030,Male,1.0,2,1,0.0,1299,1086.0,17.0,120.0,1,Urban,1.0
+LP001032,Male,0.0,0,1,0.0,4950,0.0,125.0,360.0,1,Urban,1.0
+LP001034,Male,0.0,1,0,0.0,3596,0.0,100.0,240.0,0,Urban,1.0
+LP001036,Female,0.0,0,1,0.0,3510,0.0,76.0,360.0,0,Urban,0.0
+LP001038,Male,1.0,0,0,0.0,4887,0.0,133.0,360.0,1,Rural,0.0
+LP001041,Male,1.0,0,1,0.0,2600,3500.0,115.0,,1,Urban,1.0
+LP001043,Male,1.0,0,0,0.0,7660,0.0,104.0,360.0,0,Urban,0.0
+LP001046,Male,1.0,1,1,0.0,5955,5625.0,315.0,360.0,1,Urban,1.0
+LP001047,Male,1.0,0,0,0.0,2600,1911.0,116.0,360.0,0,Semiurban,0.0
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almukhammetov_bulat_lab_3/README.md

@@ -0,0 +1,64 @@
+Вариант 2
+
+Задание:
+Предсказание категории возраста дома (housingMedianAge) на основе других признаков, таких как широта, долгота, общее количество комнат и т.д.
+
+Данные: 
+Данный набор данных использовался во второй главе недавней книги Аурелиена Жерона "Практическое машинное обучение с помощью Scikit-Learn и TensorFlow". Он служит отличным введением в реализацию алгоритмов машинного обучения, потому что требует минимальной предварительной обработки данных, содержит легко понимаемый список переменных и находится в оптимальном размере, который не слишком мал и не слишком большой.
+
+Данные содержат информацию о домах в определенном районе Калифорнии и некоторую сводную статистику на основе данных переписи 1990 года. Следует отметить, что данные не прошли предварительную очистку, и для них требуются некоторые этапы предварительной обработки. Столбцы включают в себя следующие переменные, их названия весьма наглядно описывают их суть:
+
+долгота longitude
+
+широта latitude
+
+средний возраст жилья median_house_value
+
+общее количество комнат total_rooms
+
+общее количество спален total_bedrooms
+
+население population
+
+домохозяйства households
+
+медианный доход median_income
+
+Запуск:
+Запустите файл lab3.py
+
+Описание программы:
+
+1. Загружает набор данных из файла 'housing.csv', который содержит информацию о домах в Калифорнии, включая их координаты, возраст, количество комнат, население, доход и другие характеристики.
+
+2. Удаляет строки с нулевыми значениями из набора данных для чистоты анализа.
+
+3. Выбирает набор признаков (features) из данных, которые будут использоваться для обучения моделей регрессии и классификации.
+
+4. Определяет задачу регрессии, где целевой переменной (target) является 'housing_median_age', и задачу классификации, где целевой переменной является 'housing_median_age'.
+
+5. Разделяет данные на обучающий и тестовый наборы для обеих задач с использованием функции train_test_split. Тестовый набор составляет 1% от исходных данных.
+
+6. Создает и обучает дерево решений для регрессии и классификации с использованием моделей DecisionTreeRegressor и DecisionTreeClassifier.
+
+7. Предсказывает значения целевой переменной на тестовых наборах для обеих задач.
+
+8. Оценивает качество моделей с помощью среднеквадратичной ошибки (MSE) для регрессии и точности (accuracy) для классификации.
+
+9. Выводит среднеквадратичную ошибку для регрессии и точность для классификации, а также важности признаков для обеих задач.
+
+Результаты:
+
+![Alt text](1.png)
+
+Выводы: 
+
+Для задачи регрессии, где целью было предсказать возраст жилья (housing_median_age), модель дерева решений показала среднюю ошибку (MSE) равную 117.65. Это означает, что модель регрессии вполне приемлемо предсказывает возраст жилья на основе выбранных признаков.
+
+Для задачи классификации, где целью было предсказать стоимость жилья (housing_median_age), модель дерева решений показала низкую точность, всего 8.29%. Это свидетельствует о том, что модель классификации не справляется с предсказанием стоимости жилья на основе выбранных признаков. Низкая точность указывает на необходимость улучшения модели или выбора других методов для решения задачи классификации.
+
+Анализ важности признаков для задачи регрессии показал, что наибольший вклад в предсказание возраста жилья вносят признаки 'longitude', 'latitude' и 'total_rooms'. Эти признаки оказывают наибольшее влияние на результаты модели.
+
+Для задачи классификации наибольший вклад в предсказание стоимости жилья вносят признаки 'median_income', 'longitude' и 'latitude'. Эти признаки имеют наибольшее значение при определении классов стоимости жилья.
+
+В целом, результаты указывают на успешное решение задачи регрессии с использованием модели дерева решений. Однако задача классификации требует дополнительных улучшений.

almukhammetov_bulat_lab_3/lab3(old).py

@@ -0,0 +1,48 @@
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+from sklearn.tree import DecisionTreeClassifier
+
+# Загрузка данных
+data = pd.read_csv('titanic.csv', index_col='PassengerId')
+
+
+# Функция для преобразования пола в числовое значение
+def Sex_to_bool(sex):
+    if sex == "male":
+        return 0
+    return 1
+
+
+# Преобразование пола в числовое значение
+data['Sex'] = data['Sex'].apply(Sex_to_bool)
+
+# Отбор строк с непустыми значениями
+# Отбор строк с непустыми значениями
+data = data.loc[~data['Name'].isna()
+                & ~data['Age'].isna()
+                & ~data['Sex'].isna()
+                & ~data['Survived'].isna()]
+
+
+# Отбор нужных столбцов
+features = data[['Name', 'Sex', 'Age']]
+
+# Применение Label Encoding к столбцу 'Name'
+label_encoder = LabelEncoder()
+features['Name'] = label_encoder.fit_transform(features['Name'])
+
+# Определение целевой переменной
+y = data['Survived']
+
+# Создание и обучение дерева решений
+clf = DecisionTreeClassifier(random_state=241)
+clf.fit(features, y)
+
+# Получение важностей признаков
+importance = clf.feature_importances_
+
+# Печать важности каждого признака
+print("Важность 'Name':", importance[0])
+print("Важность 'Sex':", importance[1])
+print("Важность 'Age':", importance[2])
+

almukhammetov_bulat_lab_3/lab3.py

@@ -0,0 +1,77 @@
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, accuracy_score
+
+# Загрузка данных
+data = pd.read_csv('housing.csv')
+data = data.dropna()
+
+# Отбор нужных столбцов
+features = data[
+    ['longitude', 'latitude', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income']]
+
+# Задача регрессии
+target_regression = data['housing_median_age']
+
+# Разделение данных на обучающий и тестовый наборы для регрессии
+X_train_regression, X_test_regression, y_train_regression, y_test_regression = train_test_split(features,
+                                                                                                target_regression,
+                                                                                                test_size=0.01,
+                                                                                                random_state=241)
+
+# Создание и обучение дерева решений для регрессии
+clf_regression = DecisionTreeRegressor(random_state=241)
+clf_regression.fit(X_train_regression, y_train_regression)
+
+# Предсказание на тестовом наборе для регрессии
+y_pred_regression = clf_regression.predict(X_test_regression)
+
+# Оценка качества модели для регрессии (MSE)
+mse_regression = mean_squared_error(y_test_regression, y_pred_regression)
+print("Средняя ошибка для регрессии:", mse_regression)
+
+# Задача классификации
+target_classification = data['median_house_value']
+
+# Разделение данных на обучающий и тестовый наборы для классификации
+X_train_classification, X_test_classification, y_train_classification, y_test_classification = train_test_split(
+    features, target_classification, test_size=0.01, random_state=241)
+
+# Создание и обучение дерева классификации
+clf_classification = DecisionTreeClassifier(random_state=241)
+clf_classification.fit(X_train_classification, y_train_classification)
+
+# Предсказание на тестовом наборе для классификации
+y_pred_classification = clf_classification.predict(X_test_classification)
+
+# Оценка качества модели для классификации (точность)
+accuracy_classification = accuracy_score(y_test_classification, y_pred_classification)
+print("Точность для классификации: {:.2f}%".format(accuracy_classification * 100))
+
+# Важности признаков для регрессии
+importance_regression = clf_regression.feature_importances_
+
+print("Важность для регрессии")
+# Печать важности каждого признака для регрессии
+print("Важность 'longitude':", importance_regression[0])  # За западную долготу дома
+print("Важность 'latitude':", importance_regression[1])  # За северную широту дома
+print("Важность 'total_rooms':", importance_regression[2])  # За общее количество комнат в блоке
+print("Важность 'total_bedrooms':", importance_regression[3])  # За общее количество спален в блоке
+print("Важность 'population':", importance_regression[4])  # За общее количество проживающих в блоке
+print("Важность 'households':", importance_regression[5])  # За общее количество домохозяйств в блоке
+print("Важность 'median_income':", importance_regression[6])  # За медианный доход домохозяйств в блоке
+
+# Важности признаков для классификации
+importance_classification = clf_classification.feature_importances_
+
+print()
+print("Важность для классификации")
+# Печать важности каждого признака для классификации
+print("Важность 'longitude':", importance_classification[0])  # За западную долготу дома
+print("Важность 'latitude':", importance_classification[1])  # За северную широту дома
+print("Важность 'total_rooms':", importance_classification[2])  # За общее количество комнат в блоке
+print("Важность 'total_bedrooms':", importance_classification[3])  # За общее количество спален в блоке
+print("Важность 'population':", importance_classification[4])  # За общее количество проживающих в блоке
+print("Важность 'households':", importance_classification[5])  # За общее количество домохозяйств в блоке
+print("Важность 'median_income':", importance_classification[6])  # За медианный доход домохозяйств в блоке

basharin_sevastyan_lab_2/README.md

@@ -0,0 +1,42 @@
+## Лабораторная работа 2. Вариант 5.
+### Задание 
+Выполнить ранжирование признаков. Отобразить получившиеся значения\оценки каждого признака каждым методом\моделью и среднюю оценку. Провести анализ получившихся результатов. Какие четыре признака оказались самыми важными по среднему значению? 
+
+Модели:
+
+- Гребневая регрессия `Ridge`, 
+- Рекурсивное сокращение признаков `Recursive Feature Elimination – RFE`, 
+- Сокращение признаков Случайными деревьями `Random Forest Regressor`
+
+### Как запустить
+Для запуска программы необходимо с помощью командной строки в корневой директории файлов прокета прописать:
+```
+python main.py
+```
+
+### Используемые технологии
+- `numpy` (псевдоним `np`): NumPy - это библиотека для научных вычислений в Python.
+- `sklearn` (scikit-learn): Scikit-learn - это библиотека для машинного обучения и анализа данных в Python. Из данной библиотеки были использованы следующие модули:
+  - `LinearRegression` - линейная регрессия - это алгоритм машинного обучения, используемый для задач бинарной классификации.
+  - `Ridge` - инструмент работы с моделью "Гребневая регрессия"
+  - `RFE` - инструмент оценки важности признаков "Рекурсивное сокращение признаков"
+  - `RandomForestRegressor` - инструмент работы с моделью "Регрессор случайного леса"
+
+### Описание работы
+1. Программа генерирует данные для обучения моделей, содержащие матрицу признаков X и вектор целевой переменной y.
+1. Создает DataFrame data, в котором столбцы представляют признаки, а последний столбец - целевую переменную.
+1. Разделяет данные на матрицу признаков X и вектор целевой переменной y
+1. Создает список обученных моделей для ранжирования признаков: гребневой регрессии, рекурсивного сокращения признаков и сокращения признаков случайными деревьями.
+1. Создает словарь model_scores для хранения оценок каждой модели.
+1. Выводит оценки признаков каждой модели и их средние оценки.
+1. Находит четыре наиболее важных признака по средней оценке и выводит их индексы и значения.
+
+### Результат работы
+![](ridge.png "Гребневая регрессия")
+![](rfe.png "Рекурсивное сокращение признаков")
+![](rfr.png "Сокращение признаков Случайными деревьями")
+![](res.png "Четыре самых важных")
+
+### Вывод
+Четыре наиболее важных признака, определенных на основе средних оценок, включают 
+Признак 1, Признак 3, Признак 12 и Признак 6.

basharin_sevastyan_lab_2/main.py

@@ -0,0 +1,67 @@
+import numpy as np
+import pandas as pd
+from sklearn.datasets import make_regression
+from sklearn.linear_model import Ridge, LinearRegression
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.feature_selection import RFE
+from sklearn.preprocessing import MinMaxScaler
+
+''' Задание
+Используя код из [1](пункт «Решение задачи ранжирования признаков», стр. 205), выполните ранжирование признаков с 
+помощью указанных по вариантумоделей. Отобразите получившиеся значения\оценки каждого признака каждым методом\моделью и
+среднюю  оценку. Проведите анализ получившихся результатов. Какие четырепризнака оказались самыми важными по среднему 
+значению? (Названия\индексы  признаков и будут ответом на задание).
+
+Вариант 5.
+Гребневая регрессия (Ridge), Рекурсивное сокращение признаков (Recursive Feature Elimination – RFE),
+Сокращение признаков Случайными деревьями (Random Forest Regressor).
+'''
+
+# создание данных
+random_state = np.random.RandomState(2)
+X, y = make_regression(n_samples=750, n_features=15, noise=0.1, random_state=random_state)
+data = pd.DataFrame(X, columns=[f'Признак {i}' for i in range(X.shape[1])])
+data['Целевая переменная'] = y
+X = data.drop('Целевая переменная', axis=1)
+y = data['Целевая переменная']
+
+ridge = Ridge(alpha=1)  # Гребневая регрессия
+ridge.fit(X, y)
+
+recFE = RFE(LinearRegression(), n_features_to_select=1)  # Рекурсивное сокращение признаков
+recFE.fit(X, y)
+
+rfr = RandomForestRegressor()  # Сокращение признаков Случайными деревьями
+rfr.fit(X, y)
+
+models = [('Ridge', ridge),
+          ('RFE', recFE),
+          ('RFR', rfr)]
+model_scores = []
+
+for name, model in models:
+    if name == 'Ridge':
+        coef = model.coef_
+        normalized_coef = MinMaxScaler().fit_transform(coef.reshape(-1, 1))
+        model_scores.append((name, normalized_coef.flatten()))
+    elif name == 'RFE':
+        rankings = model.ranking_
+        normalized_rankings = 1 - (rankings - 1) / (np.max(rankings) - 1)
+        model_scores.append((name, normalized_rankings))
+    elif name == 'RFR':
+        feature_importances = model.feature_importances_
+        normalized_importances = MinMaxScaler().fit_transform(feature_importances.reshape(-1, 1))
+        model_scores.append((name, normalized_importances.flatten()))
+
+for name, scores in model_scores:
+    print(f"{name} оценки признаков:")
+    for feature, score in enumerate(scores, start=1):
+        print(f"Признак {feature}: {score:.2f}")
+    print(f"Средняя оценка: {np.mean(scores):.2f}")
+
+all_feature_scores = np.mean(list(map(lambda x: x[1], model_scores)), axis=0)
+sorted_features = sorted(enumerate(all_feature_scores, start=1), key=lambda x: x[1], reverse=True)
+top_features = sorted_features[:4]
+print("Четыре наиболее важных признака:")
+for feature, score in top_features:
+    print(f"Признак {feature}: {score:.2f}")

basharin_sevastyan_lab_3/README.md

@@ -0,0 +1,93 @@
+## Лабораторная работа 3. Вариант 4.
+### Задание 
+Выполнить ранжирование признаков и решить с помощью библиотечной реализации дерева решений 
+задачу классификации на 99% данных из курсовой работы. Проверить 
+работу модели на оставшемся проценте, сделать вывод.
+
+Модель:
+- Дерево решений `DecisionTreeClassifier`.
+
+### Как запустить
+Для запуска программы необходимо с помощью командной строки в корневой директории файлов прокета прописать:
+``` python
+python main.py
+```
+
+### Используемые технологии
+- Библиотека `pandas`, используемая для работы с данными для анализа scv формата.
+- `sklearn` (scikit-learn): Scikit-learn - это библиотека для машинного обучения и анализа данных в Python. Из данной библиотеки были использованы следующие модули:
+  - `metrics` - набор инструменов для оценки моделей
+  - `DecisionTreeClassifier` - классификатор, реализующий алгоритм дерева решений. Дерево решений - это модель машинного обучения, которая разбивает данные на рекурсивные решения на основе значений признаков. Она используется для задач классификации и регрессии.
+  - `accuracy_score` -функция из scikit-learn, которая используется для оценки производительности модели классификации путем вычисления доли правильно классифицированных примеров (точности) на тестовом наборе данных.
+  - `train_test_split` - это функция из scikit-learn, используемая для разделения набора данных на обучающий и тестовый наборы.
+  - `LabelEncoder` - это класс из scikit-learn, используемый для преобразования категориальных признаков (например, строки) в числовые значения.
+
+### Описание работы
+#### Описание набора данных
+Набор данных: набор данных о цене автомобиля в автопарке.
+
+Названия столбцов набора данных и их описание:
+
+- Id: Уникальный идентификатор для каждого автомобиля в списке.
+- Price: Ценовой диапазон автомобилей с конкретными ценниками и подсчетами. (111000 - 77500000)
+- Company Name: Название компании-производителя автомобилей с указанием процентной доли представительства каждой компании.
+- Model Name: Название модели автомобилей с указанием процентного соотношения каждой модели.
+- Model Year: Диапазон лет выпуска автомобилей с указанием количества и процентных соотношений. (1990 - 2019)
+- Location: Местоположение автомобилей с указанием регионов, где они доступны для покупки, а также их процентное соотношение.
+- Mileage: Информация о пробеге автомобилей с указанием диапазонов пробега, количества и процентов. (1 - 999999)
+- Engine Type: Описания типов двигателей с процентными соотношениями для каждого типа.
+- Engine Capacity: Мощность двигателя варьируется в зависимости от количества и процентов. (16 - 6600)
+- Color: Цветовое распределение автомобилей с указанием процентных соотношений для каждого цвета.
+- Assembly: Импорт или местный рынок.
+- Body Type: Тип кузова.
+- Transmission Type: Тип трансмиссии.
+- Registration Status: Статус регистрации.
+
+Ссылка на страницу набора на kuggle: [Ultimate Car Price Prediction Dataset](https://www.kaggle.com/datasets/mohidabdulrehman/ultimate-car-price-prediction-dataset/data)
+
+#### Оцифровка и нормализация данных
+Для нормальной работы с данными, необходимо исключить из них все нечисловые значения. 
+После этого, представить все строковые значения параметров как числовые и очистить датасет от "мусора". 
+Для удаления нечисловых значений воспользуемся функцией `.dropna()`.
+Так же мы удаляем первый столбец `Id`, так как при открытии файла в `pd` он сам нумерует строки.
+
+Все нечисловые значения мы преобразуем в числовые с помощью `LabelEncoder`:
+```python
+label_encoder = LabelEncoder()
+data['Location'] = label_encoder.fit_transform(data['Location'])
+data['Company Name'] = label_encoder.fit_transform(data['Company Name'])
+data['Model Name'] = label_encoder.fit_transform(data['Model Name'])
+data['Engine Type'] = label_encoder.fit_transform(data['Engine Type'])
+data['Color'] = label_encoder.fit_transform(data['Color'])
+data['Assembly'] = label_encoder.fit_transform(data['Assembly'])
+data['Body Type'] = label_encoder.fit_transform(data['Body Type'])
+data['Transmission Type'] = label_encoder.fit_transform(data['Transmission Type'])
+data['Registration Status'] = label_encoder.fit_transform(data['Registration Status'])
+```
+
+#### Выявление значимых параметров
+```python
+# Оценка важности признаков
+feature_importances = clf.feature_importances_
+feature_importance_df = pd.DataFrame({'Feature': X_train.columns, 'Importance': feature_importances})
+feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)
+```
+
+#### Решение задачи кластеризации на полном наборе признаков 
+Чтобы решить задачу кластеризации моделью `DecisionTreeClassifier`, воспользуемся методом `.predict()`.
+```python
+clf = DecisionTreeClassifier(max_depth=5, random_state=42)
+clf.fit(X_train, y_train)
+y_pred = clf.predict(X_test)
+```
+
+#### Оценка эффективности
+Для оценки точности модели будем использовать встроенный инструмент `accuracy_score`:
+```python
+accuracy = accuracy_score(y_test, y_pred)
+```
+
+#### Результаты
+![](accuracy.png "Точность")
+
+![](important.png "Важность признаков")

basharin_sevastyan_lab_3/main.py

@@ -0,0 +1,78 @@
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+
+''' Названия столбцов набора данных и их описание:
+Id: Уникальный идентификатор для каждого автомобиля в списке.
+Price: Ценовой диапазон автомобилей с конкретными ценниками и подсчетами. (111000 - 77500000)
+Company Name: Название компании-производителя автомобилей с указанием процентной доли представительства каждой компании.
+Model Name: Название модели автомобилей с указанием процентного соотношения каждой модели.
+Model Year: Диапазон лет выпуска автомобилей с указанием количества и процентных соотношений. (1990 - 2019)
+Location: Местоположение автомобилей с указанием регионов, где они доступны для покупки, а также их процентное соотношение.
+Mileage: Информация о пробеге автомобилей с указанием диапазонов пробега, количества и процентов. (1 - 999999)
+Engine Type: Описания типов двигателей с процентными соотношениями для каждого типа.
+Engine Capacity: Мощность двигателя варьируется в зависимости от количества и процентов. (16 - 6600)
+Color: Цветовое распределение автомобилей с указанием процентных соотношений для каждого цвета.
+'''
+
+
+# Загрузите данные из вашей курсовой работы, предположим, что у вас есть файл CSV.
+data = pd.read_csv('Data_pakwheels.csv')
+data.pop("Id")
+
+data.dropna(inplace=True)  # Удаление строки с пропущенными значениями.
+
+# Преобразуйте категориальные признаки в числовые. Используйте, например, one-hot encoding.
+# data = pd.get_dummies(data, columns=['Company Name', 'Model Name', 'Location', 'Engine Type', 'Color'])
+
+# Создайте объект LabelEncoder
+label_encoder = LabelEncoder()
+
+data['Location'] = label_encoder.fit_transform(data['Location'])
+data['Company Name'] = label_encoder.fit_transform(data['Company Name'])
+data['Model Name'] = label_encoder.fit_transform(data['Model Name'])
+data['Engine Type'] = label_encoder.fit_transform(data['Engine Type'])
+data['Color'] = label_encoder.fit_transform(data['Color'])
+data['Assembly'] = label_encoder.fit_transform(data['Assembly'])
+data['Body Type'] = label_encoder.fit_transform(data['Body Type'])
+data['Transmission Type'] = label_encoder.fit_transform(data['Transmission Type'])
+data['Registration Status'] = label_encoder.fit_transform(data['Registration Status'])
+
+# Разделение данных на обучающий набор и тестовый набор. Мы будем использовать 99% данных для обучения.
+train_data, test_data = train_test_split(data, test_size=0.01, random_state=42)
+
+# Определите целевую переменную (то, что вы пытаетесь предсказать, например, 'Price').
+X_train = train_data.drop(columns=['Price'])
+y_train = train_data['Price']
+X_test = test_data.drop(columns=['Price'])
+y_test = test_data['Price']
+
+# Создание и обучение модели DecisionTreeClassifier
+clf = DecisionTreeClassifier(random_state=42)
+clf.fit(X_train, y_train)
+
+# Оценка важности признаков
+feature_importances = clf.feature_importances_
+
+# Создание DataFrame с именами признаков и их важностью
+feature_importance_df = pd.DataFrame({'Feature': X_train.columns, 'Importance': feature_importances})
+
+# Сортировка признаков по убыванию важности
+feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)
+
+# Вывод ранжированных признаков
+print(feature_importance_df)
+
+clf = DecisionTreeClassifier(max_depth=5, random_state=42)
+
+# Обучите модель на обучающем наборе данных
+clf.fit(X_train, y_train)
+
+# Предсказание целевой переменной на тестовом наборе данных
+y_pred = clf.predict(X_test)
+
+# Оцените производительность модели с помощью различных метрик
+accuracy = accuracy_score(y_test, y_pred)
+print(f'Точность модели: {accuracy}')

belyaeva_ekaterina_lab_3/Current_Pub_Meta.csv

@@ -0,0 +1,125 @@
+,Name,Primary Attribute,Roles,Herald Picks,Herald Wins,Herald Win Rate,Guardian Picks,Guardian Wins,Guardian Win Rate,Crusader Picks,Crusader Wins,Crusader Win Rate,Archon Picks,Archon Wins,Archon Win Rate,Legend Picks,Legend Wins,Legend Win Rate,Ancient Picks,Ancient Wins,Ancient Win Rate,Divine Picks,Divine Wins,Divine Win Rate,Immortal Picks,Immortal Wins,Immortal Win Rate
+0,Abaddon,all,"Support, Carry, Durable",1111,575,51.76,6408,3309,51.64,13811,7050,51.05,16497,8530,51.71,11360,5877,51.73,5571,2893,51.93,2632,1345,51.1,991,497,50.15
+1,Alchemist,str,"Carry, Support, Durable, Disabler, Initiator, Nuker",1119,486,43.43,6370,2883,45.26,12238,5617,45.9,13028,6130,47.05,8455,4055,47.96,4120,1984,48.16,2021,1023,50.62,860,424,49.3
+2,Ancient Apparition,int,"Support, Disabler, Nuker",2146,1073,50.0,13697,7069,51.61,30673,16118,52.55,35145,18219,51.84,23114,12166,52.63,10688,5528,51.72,5035,2573,51.1,2134,1076,50.42
+3,Anti-Mage,agi,"Carry, Escape, Nuker",3765,1818,48.29,22050,10774,48.86,47371,23304,49.19,49115,24074,49.02,28599,13991,48.92,12303,5958,48.43,4866,2349,48.27,1502,751,50.0
+4,Arc Warden,agi,"Carry, Escape, Nuker",1448,704,48.62,8047,4162,51.72,14946,7982,53.41,14711,7875,53.53,9472,5167,54.55,4323,2309,53.41,2104,1148,54.56,789,435,55.13
+5,Axe,str,"Initiator, Durable, Disabler, Carry",5343,2880,53.9,32652,17719,54.27,71010,37736,53.14,77869,40559,52.09,49182,25079,50.99,22637,11353,50.15,10114,5000,49.44,3795,1837,48.41
+6,Bane,all,"Support, Disabler, Nuker, Durable",745,334,44.83,4983,2422,48.61,11332,5504,48.57,13633,6767,49.64,10132,5032,49.66,5596,2861,51.13,3028,1555,51.35,1958,1055,53.88
+7,Batrider,all,"Initiator, Disabler, Escape",349,136,38.97,1983,812,40.95,4053,1595,39.35,4725,1861,39.39,3173,1275,40.18,1678,731,43.56,802,362,45.14,497,227,45.67
+8,Beastmaster,all,"Initiator, Disabler, Durable, Nuker",402,174,43.28,2447,1060,43.32,5787,2569,44.39,6930,3092,44.62,5288,2389,45.18,2816,1274,45.24,1593,752,47.21,1176,539,45.83
+9,Bloodseeker,agi,"Carry, Disabler, Nuker, Initiator",2765,1382,49.98,12589,6270,49.81,21781,10683,49.05,20961,10420,49.71,13035,6430,49.33,6210,3006,48.41,2941,1475,50.15,1465,718,49.01
+10,Bounty Hunter,agi,"Escape, Nuker",3852,1868,48.49,19609,9535,48.63,36362,17600,48.4,37059,18314,49.42,22934,11518,50.22,10584,5276,49.85,5105,2594,50.81,2498,1325,53.04
+11,Brewmaster,all,"Carry, Initiator, Durable, Disabler, Nuker",545,280,51.38,3564,1745,48.96,8941,4388,49.08,12340,6111,49.52,11185,5623,50.27,7645,3906,51.09,4812,2478,51.5,3533,1820,51.51
+12,Bristleback,str,"Carry, Durable, Initiator, Nuker",5884,3262,55.44,27952,14587,52.19,48847,24379,49.91,46702,22927,49.09,27466,13319,48.49,12398,5969,48.14,5865,2915,49.7,2639,1304,49.41
+13,Broodmother,all,"Carry, Pusher, Escape, Nuker",456,173,37.94,2048,842,41.11,3444,1462,42.45,3392,1448,42.69,2193,1048,47.79,1203,602,50.04,795,422,53.08,453,230,50.77
+14,Centaur Warrunner,str,"Durable, Initiator, Disabler, Nuker, Escape",1721,911,52.93,11754,6266,53.31,28691,15201,52.98,35369,18741,52.99,25393,13468,53.04,12653,6607,52.22,6124,3181,51.94,2442,1243,50.9
+15,Chaos Knight,str,"Carry, Disabler, Durable, Pusher, Initiator",3032,1639,54.06,16762,8931,53.28,31892,17139,53.74,30697,16435,53.54,18217,9810,53.85,8572,4620,53.9,4230,2291,54.16,1750,943,53.89
+16,Chen,all,"Support, Pusher",284,125,44.01,1450,678,46.76,2969,1345,45.3,3258,1604,49.23,2641,1331,50.4,1488,767,51.55,970,512,52.78,770,448,58.18
+17,Clinkz,agi,"Carry, Escape, Pusher",3151,1608,51.03,13891,7141,51.41,25465,12938,50.81,27327,14066,51.47,18846,9726,51.61,9452,4890,51.74,4765,2475,51.94,2093,1052,50.26
+18,Clockwerk,all,"Initiator, Disabler, Durable, Nuker",816,397,48.65,5860,2837,48.41,14478,6929,47.86,18466,8843,47.89,13143,6301,47.94,6612,3169,47.93,3286,1581,48.11,1378,658,47.75
+19,Crystal Maiden,int,"Support, Disabler, Nuker",4821,2529,52.46,26584,13626,51.26,52168,26040,49.92,52258,25365,48.54,30690,14848,48.38,13295,6404,48.17,5602,2680,47.84,1638,771,47.07
+20,Dark Seer,all,"Initiator, Escape, Disabler",627,320,51.04,3675,1884,51.27,7881,3803,48.26,9589,4844,50.52,7186,3573,49.72,3902,1983,50.82,2145,1095,51.05,1217,593,48.73
+21,Dark Willow,all,"Support, Nuker, Disabler, Escape",2654,1293,48.72,13829,6657,48.14,28142,13480,47.9,32114,15785,49.15,23100,11331,49.05,12052,5909,49.03,6400,3182,49.72,3708,1915,51.65
+22,Dawnbreaker,str,"Carry, Durable",1746,875,50.11,12297,6105,49.65,32398,15921,49.14,44846,21936,48.91,35474,17441,49.17,19770,9832,49.73,10637,5263,49.48,6339,3173,50.06
+23,Dazzle,all,"Support, Nuker, Disabler",2827,1418,50.16,19852,9758,49.15,48236,23691,49.11,56417,27798,49.27,38159,18642,48.85,18695,9199,49.21,8530,4239,49.7,3382,1654,48.91
+24,Death Prophet,int,"Carry, Pusher, Nuker, Disabler",1372,659,48.03,6643,3145,47.34,11987,5729,47.79,12268,5856,47.73,7455,3606,48.37,3591,1698,47.28,1872,902,48.18,926,459,49.57
+25,Disruptor,int,"Support, Disabler, Nuker, Initiator",1541,757,49.12,11104,5331,48.01,27746,13542,48.81,33742,16310,48.34,23173,11096,47.88,10907,5201,47.68,4859,2255,46.41,1863,861,46.22
+26,Doom,str,"Carry, Disabler, Initiator, Durable, Nuker",1049,474,45.19,6112,2767,45.27,13700,6056,44.2,15454,6925,44.81,10727,4842,45.14,5444,2451,45.02,2979,1348,45.25,1545,731,47.31
+27,Dragon Knight,str,"Carry, Pusher, Durable, Disabler, Initiator, Nuker",1950,942,48.31,10643,5274,49.55,20451,9733,47.59,20326,9671,47.58,11674,5544,47.49,4979,2355,47.3,2024,973,48.07,725,341,47.03
+28,Drow Ranger,agi,"Carry, Disabler, Pusher",5737,2904,50.62,29675,14831,49.98,57655,28573,49.56,56682,27927,49.27,34310,16607,48.4,15050,7171,47.65,5947,2815,47.33,1768,788,44.57
+29,Earth Spirit,str,"Nuker, Escape, Disabler, Initiator, Durable",1038,465,44.8,7420,3276,44.15,20807,9432,45.33,30107,14166,47.05,25314,12148,47.99,14579,7041,48.3,7678,3802,49.52,4379,2169,49.53
+30,Earthshaker,str,"Support, Initiator, Disabler, Nuker",5012,2455,48.98,29784,14662,49.23,67050,33111,49.38,79963,39843,49.83,57108,28961,50.71,28650,14591,50.93,14186,7296,51.43,6151,3165,51.46
+31,Elder Titan,str,"Initiator, Disabler, Nuker, Durable",471,212,45.01,2551,1248,48.92,5213,2570,49.3,5572,2809,50.41,3847,1942,50.48,1964,998,50.81,1124,613,54.54,550,292,53.09
+32,Ember Spirit,agi,"Carry, Escape, Nuker, Disabler, Initiator",1514,635,41.94,9180,3836,41.79,20578,8738,42.46,25152,10844,43.11,17703,7814,44.14,8538,3793,44.42,4265,1892,44.36,2065,928,44.94
+33,Enchantress,int,"Support, Pusher, Durable, Disabler",1794,848,47.27,8050,3622,44.99,12921,5686,44.01,11673,4974,42.61,6863,2840,41.38,2948,1212,41.11,1434,654,45.61,806,318,39.45
+34,Enigma,all,"Disabler, Initiator, Pusher",1317,588,44.65,6937,3171,45.71,12908,5979,46.32,11687,5428,46.44,6194,2839,45.83,2493,1127,45.21,938,437,46.59,338,159,47.04
+35,Faceless Void,agi,"Carry, Initiator, Disabler, Escape, Durable",4323,2043,47.26,25618,11902,46.46,54581,25874,47.4,60671,28993,47.79,40137,19611,48.86,19376,9620,49.65,9579,4828,50.4,4439,2256,50.82
+36,Grimstroke,int,"Support, Nuker, Disabler, Escape",1455,694,47.7,9714,4789,49.3,24688,12430,50.35,32027,16094,50.25,23193,11795,50.86,12102,6100,50.4,6191,3047,49.22,3449,1666,48.3
+37,Gyrocopter,agi,"Carry, Nuker, Disabler",2560,1213,47.38,16589,7882,47.51,42072,20358,48.39,54200,26229,48.39,39414,19053,48.34,20164,9781,48.51,10164,4937,48.57,5241,2507,47.83
+38,Hoodwink,agi,"Support, Nuker, Escape, Disabler",2420,1126,46.53,14034,6800,48.45,31382,14964,47.68,35684,16966,47.55,22626,10651,47.07,9949,4690,47.14,4349,2089,48.03,1533,703,45.86
+39,Huskar,str,"Carry, Durable, Initiator",3501,1603,45.79,14234,6639,46.64,22794,10912,47.87,21801,10763,49.37,13811,6919,50.1,6769,3535,52.22,3556,1822,51.24,1936,993,51.29
+40,Invoker,all,"Carry, Nuker, Disabler, Escape, Pusher",4330,2042,47.16,27625,13176,47.7,69035,33863,49.05,86745,43479,50.12,61821,31510,50.97,31459,16321,51.88,15431,8195,53.11,7852,4148,52.83
+41,Io,all,"Support, Escape, Nuker",1274,615,48.27,6158,2999,48.7,12762,6247,48.95,14216,7024,49.41,9564,4843,50.64,5301,2685,50.65,2789,1463,52.46,1464,773,52.8
+42,Jakiro,int,"Support, Nuker, Pusher, Disabler",3147,1708,54.27,22718,12413,54.64,56736,30984,54.61,70038,37473,53.5,46389,24997,53.89,22084,11639,52.7,9838,5103,51.87,3282,1729,52.68
+43,Juggernaut,agi,"Carry, Pusher, Escape",5585,2711,48.54,30394,14800,48.69,62313,30581,49.08,65590,32344,49.31,39235,19326,49.26,16334,8012,49.05,6419,3066,47.76,1576,731,46.38
+44,Keeper of the Light,int,"Support, Nuker, Disabler",896,353,39.4,5051,2216,43.87,10452,4579,43.81,11614,5322,45.82,7870,3627,46.09,4268,2001,46.88,2147,1043,48.58,1333,588,44.11
+45,Kunkka,str,"Carry, Support, Disabler, Initiator, Durable, Nuker",2251,1124,49.93,13474,6828,50.68,31210,16196,51.89,39691,21293,53.65,30314,16458,54.29,15706,8793,55.98,7884,4339,55.04,3458,1898,54.89
+46,Legion Commander,str,"Carry, Disabler, Initiator, Durable, Nuker",6263,3264,52.12,37100,19157,51.64,81491,41557,51.0,91431,46558,50.92,59383,29917,50.38,27945,13917,49.8,13193,6587,49.93,5601,2745,49.01
+47,Leshrac,int,"Carry, Support, Nuker, Pusher, Disabler",674,316,46.88,3872,1799,46.46,7490,3433,45.83,7903,3604,45.6,5322,2526,47.46,2687,1298,48.31,1325,647,48.83,721,357,49.51
+48,Lich,int,"Support, Nuker",2700,1412,52.3,16646,8820,52.99,37785,19685,52.1,45471,23554,51.8,31203,16108,51.62,15530,7821,50.36,7243,3597,49.66,2520,1258,49.92
+49,Lifestealer,str,"Carry, Durable, Escape, Disabler",2515,1213,48.23,14131,6978,49.38,29724,14627,49.21,31211,15581,49.92,18970,9481,49.98,8689,4400,50.64,3630,1821,50.17,1229,617,50.2
+50,Lina,int,"Support, Carry, Nuker, Disabler",4512,2030,44.99,21927,10156,46.32,45301,21210,46.82,54229,25956,47.86,40016,19138,47.83,21072,10112,47.99,10481,5031,48.0,4369,2138,48.94
+51,Lion,int,"Support, Disabler, Nuker, Initiator",6204,2855,46.02,37869,17465,46.12,80124,36649,45.74,84390,38176,45.24,50720,22914,45.18,21698,9784,45.09,9308,4280,45.98,3220,1496,46.46
+52,Lone Druid,all,"Carry, Pusher, Durable",909,483,53.14,4714,2421,51.36,10987,5858,53.32,14580,7968,54.65,11810,6490,54.95,7241,3971,54.84,4024,2240,55.67,2303,1259,54.67
+53,Luna,agi,"Carry, Nuker, Pusher",1927,904,46.91,9091,4271,46.98,16571,7922,47.81,16035,7615,47.49,9728,4634,47.64,4463,2103,47.12,1912,911,47.65,719,322,44.78
+54,Lycan,all,"Carry, Pusher, Durable, Escape",374,174,46.52,1894,915,48.31,3691,1744,47.25,3824,1905,49.82,2694,1332,49.44,1460,753,51.58,827,411,49.7,532,289,54.32
+55,Magnus,all,"Initiator, Disabler, Nuker, Escape",770,339,44.03,5789,2651,45.79,17837,7954,44.59,26126,12058,46.15,20634,9592,46.49,10574,5056,47.82,4565,2073,45.41,1606,751,46.76
+56,Marci,all,"Support, Carry, Initiator, Disabler, Escape",1370,620,45.26,7092,3252,45.85,15199,7240,47.63,18485,8874,48.01,13308,6305,47.38,7176,3476,48.44,3689,1882,51.02,1746,883,50.57
+57,Mars,str,"Carry, Initiator, Disabler, Durable",862,375,43.5,5719,2529,44.22,15156,6756,44.58,20719,9369,45.22,16419,7387,44.99,9044,4052,44.8,4536,2093,46.14,1926,868,45.07
+58,Medusa,agi,"Carry, Disabler, Durable",1898,902,47.52,9289,4512,48.57,16504,7818,47.37,14796,6886,46.54,7488,3449,46.06,2775,1270,45.77,1073,482,44.92,394,184,46.7
+59,Meepo,agi,"Carry, Escape, Nuker, Disabler, Initiator, Pusher",1004,523,52.09,3970,1990,50.13,6904,3587,51.96,7166,3646,50.88,4906,2563,52.24,2383,1282,53.8,1139,588,51.62,585,300,51.28
+60,Mirana,all,"Carry, Support, Escape, Nuker, Disabler",2499,1193,47.74,16954,8135,47.98,39985,19097,47.76,45169,21554,47.72,28467,13456,47.27,12800,6047,47.24,5272,2500,47.42,1824,874,47.92
+61,Monkey King,agi,"Carry, Escape, Disabler, Initiator",3191,1384,43.37,17306,7544,43.59,35734,16113,45.09,40778,18322,44.93,27558,12630,45.83,14034,6433,45.84,6650,3152,47.4,3040,1440,47.37
+62,Morphling,agi,"Carry, Escape, Durable, Nuker, Disabler",1521,690,45.36,8620,4006,46.47,18075,8161,45.15,20414,9235,45.24,14395,6530,45.36,7697,3551,46.13,4432,2050,46.25,2560,1190,46.48
+63,Muerta,int,"Carry, Nuker, Disabler",2130,1089,51.13,10787,5740,53.21,22602,11898,52.64,27609,14495,52.5,20175,10465,51.87,10662,5518,51.75,5462,2759,50.51,2948,1517,51.46
+64,Naga Siren,agi,"Carry, Support, Pusher, Disabler, Initiator, Escape",1502,804,53.53,6495,3356,51.67,10423,5234,50.22,9830,4929,50.14,6057,2971,49.05,3216,1675,52.08,1855,933,50.3,1242,634,51.05
+65,Nature's Prophet,int,"Carry, Pusher, Escape, Nuker",5991,3029,50.56,36433,18143,49.8,83118,42095,50.64,100341,51268,51.09,69436,35870,51.66,34256,17858,52.13,16585,8745,52.73,7182,3755,52.28
+66,Necrophos,int,"Carry, Nuker, Durable, Disabler",4776,2702,56.57,28535,15771,55.27,62186,34285,55.13,70212,38163,54.35,46539,24708,53.09,21607,11302,52.31,9677,4994,51.61,3418,1733,50.7
+67,Night Stalker,str,"Carry, Initiator, Durable, Disabler, Nuker",1189,594,49.96,7868,3892,49.47,19446,10004,51.45,25524,13506,52.91,20138,10828,53.77,10767,5651,52.48,5499,2889,52.54,2415,1257,52.05
+68,Nyx Assassin,all,"Disabler, Nuker, Initiator, Escape",1718,867,50.47,10925,5525,50.57,27207,14073,51.73,34684,18059,52.07,25736,13572,52.74,13313,7093,53.28,6485,3444,53.11,2852,1468,51.47
+69,Ogre Magi,str,"Support, Nuker, Disabler, Durable, Initiator",5331,2845,53.37,31507,16299,51.73,62954,32248,51.22,61758,31373,50.8,33746,16988,50.34,13262,6654,50.17,4861,2420,49.78,1271,654,51.46
+70,Omniknight,str,"Support, Durable, Nuker",975,479,49.13,6426,3109,48.38,14641,7319,49.99,17258,8731,50.59,11695,5916,50.59,5746,2993,52.09,2870,1469,51.18,1333,656,49.21
+71,Oracle,int,"Support, Nuker, Disabler, Escape",796,384,48.24,4857,2417,49.76,13141,6645,50.57,18944,9853,52.01,15221,7964,52.32,8356,4458,53.35,4475,2380,53.18,1905,1018,53.44
+72,Outworld Destroyer,int,"Carry, Nuker, Disabler",2226,1118,50.22,13388,6864,51.27,33284,17362,52.16,43991,23377,53.14,32021,16994,53.07,16655,8724,52.38,8123,4218,51.93,3176,1649,51.92
+73,Pangolier,all,"Carry, Nuker, Disabler, Durable, Escape, Initiator",1156,534,46.19,7189,3209,44.64,17802,7937,44.58,25785,11677,45.29,21727,10144,46.69,13064,6351,48.61,7567,3737,49.39,5275,2734,51.83
+74,Phantom Assassin,agi,"Carry, Escape",8553,4426,51.75,48549,25553,52.63,104756,54881,52.39,119332,62511,52.38,79140,41143,51.99,37399,19325,51.67,17774,9077,51.07,7819,3856,49.32
+75,Phantom Lancer,agi,"Carry, Escape, Pusher, Nuker",3641,1960,53.83,19550,10374,53.06,38576,20633,53.49,41505,22310,53.75,26401,14268,54.04,12437,6590,52.99,5708,2985,52.3,2383,1243,52.16
+76,Phoenix,all,"Support, Nuker, Initiator, Escape, Disabler",743,315,42.4,5231,2471,47.24,13950,6633,47.55,18350,8864,48.31,13972,6715,48.06,7787,3761,48.3,4322,2132,49.33,2610,1325,50.77
+77,Primal Beast,str,"Initiator, Durable, Disabler",1455,701,48.18,9333,4448,47.66,22800,11058,48.5,30084,14643,48.67,24307,11993,49.34,13970,6991,50.04,7742,3890,50.25,4625,2407,52.04
+78,Puck,int,"Initiator, Disabler, Escape, Nuker",871,399,45.81,5773,2628,45.52,16596,7578,45.66,24480,11315,46.22,20070,9497,47.32,11023,5298,48.06,5656,2714,47.98,2555,1200,46.97
+79,Pudge,str,"Disabler, Initiator, Durable, Nuker",7677,3796,49.45,50891,24776,48.68,114784,56289,49.04,129604,63097,48.68,85800,41542,48.42,41730,20239,48.5,19823,9530,48.08,7112,3431,48.24
+80,Pugna,int,"Nuker, Pusher",2075,944,45.49,9998,4695,46.96,18962,8958,47.24,20240,9965,49.23,12807,6199,48.4,5825,2855,49.01,2758,1387,50.29,1195,592,49.54
+81,Queen of Pain,int,"Carry, Nuker, Escape",2287,1100,48.1,15119,7354,48.64,37137,18118,48.79,47706,23657,49.59,35500,18018,50.75,18405,9289,50.47,9243,4689,50.73,4227,2113,49.99
+82,Razor,agi,"Carry, Durable, Nuker, Pusher",2470,1231,49.84,12000,5964,49.7,24666,12142,49.23,30334,14844,48.94,21832,10558,48.36,11917,5679,47.65,6092,2912,47.8,3144,1551,49.33
+83,Riki,agi,"Carry, Escape, Disabler",3684,1929,52.36,19022,9891,52.0,35638,18582,52.14,33908,17415,51.36,20194,10312,51.06,8726,4377,50.16,3735,1855,49.67,1160,559,48.19
+84,Rubick,int,"Support, Disabler, Nuker",3090,1404,45.44,21639,9303,42.99,57417,24590,42.83,74874,32603,43.54,55186,24219,43.89,28206,12568,44.56,13732,6106,44.47,5764,2642,45.84
+85,Sand King,all,"Initiator, Disabler, Support, Nuker, Escape",2633,1513,57.46,13097,7323,55.91,25271,13807,54.64,26724,14323,53.6,17384,9144,52.6,7907,4104,51.9,3394,1719,50.65,1211,611,50.45
+86,Shadow Demon,int,"Support, Disabler, Initiator, Nuker",547,236,43.14,3252,1426,43.85,7920,3524,44.49,9752,4551,46.67,7404,3467,46.83,3956,1876,47.42,2076,1004,48.36,1054,497,47.15
+87,Shadow Fiend,agi,"Carry, Nuker",5051,2544,50.37,27255,14064,51.6,58589,29830,50.91,65429,33097,50.58,41810,21189,50.68,18766,9401,50.1,8232,4000,48.59,3016,1430,47.41
+88,Shadow Shaman,int,"Support, Pusher, Disabler, Nuker, Initiator",5323,2795,52.51,29733,15606,52.49,58894,31236,53.04,58765,30895,52.57,34475,18242,52.91,15166,7986,52.66,6377,3323,52.11,2413,1253,51.93
+89,Silencer,int,"Carry, Support, Disabler, Initiator, Nuker",4229,2324,54.95,27878,14960,53.66,61698,33081,53.62,65256,34458,52.8,38589,19853,51.45,16889,8653,51.23,6836,3416,49.97,2236,1105,49.42
+90,Skywrath Mage,int,"Support, Nuker, Disabler",4000,2030,50.75,22783,11675,51.24,46512,23624,50.79,51329,25706,50.08,34167,17364,50.82,16693,8415,50.41,8496,4208,49.53,4389,2069,47.14
+91,Slardar,str,"Carry, Durable, Initiator, Disabler, Escape",3935,2129,54.1,21523,11602,53.91,43947,23701,53.93,47721,25633,53.71,29887,16132,53.98,14233,7722,54.25,6530,3467,53.09,2322,1205,51.89
+92,Slark,agi,"Carry, Escape, Disabler, Nuker",4815,2521,52.36,29413,14762,50.19,64004,31771,49.64,70173,34411,49.04,44780,21926,48.96,20864,10270,49.22,9969,4962,49.77,4565,2394,52.44
+93,Snapfire,all,"Support, Nuker, Disabler, Escape",1524,682,44.75,10646,4576,42.98,27103,12120,44.72,34711,15412,44.4,24351,10786,44.29,11723,5131,43.77,5227,2294,43.89,1987,868,43.68
+94,Sniper,agi,"Carry, Nuker",8022,4079,50.85,44508,22727,51.06,88690,45223,50.99,87190,44086,50.56,47411,23648,49.88,18092,8924,49.33,6130,3040,49.59,1370,662,48.32
+95,Spectre,agi,"Carry, Durable, Escape",3454,2008,58.14,22097,12356,55.92,49157,26961,54.85,55914,30100,53.83,36321,19338,53.24,16946,8960,52.87,7921,4163,52.56,2568,1370,53.35
+96,Spirit Breaker,str,"Carry, Initiator, Disabler, Durable, Escape",4788,2423,50.61,26662,13530,50.75,56535,28908,51.13,63991,32249,50.4,42512,21357,50.24,20119,9926,49.34,9499,4814,50.68,3761,1884,50.09
+97,Storm Spirit,int,"Carry, Escape, Nuker, Initiator, Disabler",2202,1001,45.46,11656,5197,44.59,25644,11806,46.04,30968,14210,45.89,21680,10197,47.03,10810,5025,46.48,5278,2382,45.13,2363,1122,47.48
+98,Sven,str,"Carry, Disabler, Initiator, Durable, Nuker",3552,1761,49.58,19792,9744,49.23,41296,20478,49.59,48709,24228,49.74,35460,17828,50.28,19795,10065,50.85,11014,5655,51.34,6701,3387,50.54
+99,Techies,all,"Nuker, Disabler",2356,1131,48.01,13105,6245,47.65,27293,12893,47.24,29180,13507,46.29,18216,8407,46.15,8266,3771,45.62,3459,1644,47.53,1319,591,44.81
+100,Templar Assassin,agi,"Carry, Escape",2142,955,44.58,10932,4758,43.52,21211,9445,44.53,23928,10909,45.59,17399,8242,47.37,9567,4656,48.67,5525,2708,49.01,3524,1775,50.37
+101,Terrorblade,agi,"Carry, Pusher, Nuker",1115,484,43.41,5686,2430,42.74,10856,4638,42.72,11518,5041,43.77,8059,3540,43.93,4192,1827,43.58,2419,1082,44.73,1621,700,43.18
+102,Tidehunter,str,"Initiator, Durable, Disabler, Nuker, Carry",1835,855,46.59,11159,5369,48.11,26222,12699,48.43,30735,14879,48.41,20523,9727,47.4,9731,4740,48.71,4426,2079,46.97,1998,936,46.85
+103,Timbersaw,all,"Nuker, Durable, Escape",1050,448,42.67,5854,2584,44.14,12301,5391,43.83,14295,6097,42.65,9697,4217,43.49,4992,2163,43.33,2419,1021,42.21,1139,471,41.35
+104,Tinker,int,"Carry, Nuker, Pusher",2106,944,44.82,11058,5200,47.02,24263,11826,48.74,27531,13614,49.45,19017,9732,51.18,9416,4875,51.77,4700,2466,52.47,1951,1036,53.1
+105,Tiny,str,"Carry, Nuker, Pusher, Initiator, Durable, Disabler",1434,654,45.61,7742,3452,44.59,15936,6950,43.61,17139,7468,43.57,11269,4991,44.29,5485,2491,45.41,2599,1216,46.79,1058,519,49.05
+106,Treant Protector,str,"Support, Initiator, Durable, Disabler, Escape",1646,899,54.62,11430,5881,51.45,28752,15124,52.6,36093,19344,53.59,28762,15532,54.0,16751,9227,55.08,9870,5468,55.4,6801,3855,56.68
+107,Troll Warlord,agi,"Carry, Pusher, Disabler, Durable",3176,1720,54.16,14007,7445,53.15,24729,13022,52.66,25424,13228,52.03,17362,9030,52.01,9427,4913,52.12,4767,2499,52.42,2341,1242,53.05
+108,Tusk,str,"Initiator, Disabler, Nuker",1263,565,44.73,8338,3777,45.3,19642,8869,45.15,25308,11520,45.52,18927,8853,46.77,10100,4820,47.72,5220,2502,47.93,2350,1157,49.23
+109,Underlord,str,"Support, Nuker, Disabler, Durable, Escape",797,405,50.82,4583,2341,51.08,10067,5057,50.23,11650,5786,49.67,7224,3561,49.29,3310,1591,48.07,1368,673,49.2,395,190,48.1
+110,Undying,str,"Support, Durable, Disabler, Nuker",3170,1620,51.1,19403,10116,52.14,40582,21110,52.02,40850,21182,51.85,23985,12454,51.92,10395,5389,51.84,4541,2336,51.44,2064,1012,49.03
+111,Ursa,agi,"Carry, Durable, Disabler",2801,1273,45.45,15132,7038,46.51,33269,15478,46.52,40822,19264,47.19,29348,14011,47.74,15262,7375,48.32,7507,3622,48.25,3004,1473,49.03
+112,Vengeful Spirit,all,"Support, Initiator, Disabler, Nuker, Escape",2186,1108,50.69,15817,8285,52.38,41843,21809,52.12,57524,30476,52.98,45512,24120,53.0,25581,13382,52.31,13758,7121,51.76,8276,4303,51.99
+113,Venomancer,all,"Support, Nuker, Initiator, Pusher, Disabler",2309,1187,51.41,14669,7463,50.88,34787,18020,51.8,41797,21690,51.89,28706,15085,52.55,13974,7338,52.51,6538,3495,53.46,2794,1459,52.22
+114,Viper,agi,"Carry, Durable, Initiator, Disabler",4100,2057,50.17,18991,9510,50.08,33517,16923,50.49,32728,16677,50.96,18537,9427,50.86,7851,3928,50.03,3260,1652,50.67,1176,610,51.87
+115,Visage,all,"Support, Nuker, Durable, Disabler, Pusher",331,171,51.66,1638,813,49.63,3240,1577,48.67,3840,1986,51.72,3108,1609,51.77,1995,1055,52.88,1309,702,53.63,858,457,53.26
+116,Void Spirit,all,"Carry, Escape, Nuker, Disabler",1565,727,46.45,8672,4096,47.23,20010,9694,48.45,25213,12376,49.09,18817,9231,49.06,10026,4920,49.07,4788,2319,48.43,2006,964,48.06
+117,Warlock,int,"Support, Initiator, Disabler",2547,1369,53.75,18931,10331,54.57,49795,26999,54.22,66697,36220,54.31,48401,25668,53.03,24999,12942,51.77,12575,6356,50.54,6183,2934,47.45
+118,Weaver,agi,"Carry, Escape",2818,1389,49.29,13873,6770,48.8,23493,11571,49.25,21545,10694,49.64,12911,6427,49.78,5809,2928,50.4,2960,1455,49.16,1303,719,55.18
+119,Windranger,all,"Carry, Support, Disabler, Escape, Nuker",3861,1814,46.98,19934,9223,46.27,40644,18807,46.27,44476,20652,46.43,28952,13508,46.66,13418,6297,46.93,5898,2782,47.17,2374,1142,48.1
+120,Winter Wyvern,all,"Support, Disabler, Nuker",821,371,45.19,5168,2424,46.9,10544,5014,47.55,11184,5308,47.46,7426,3512,47.29,3730,1854,49.71,1862,934,50.16,944,464,49.15
+121,Witch Doctor,int,"Support, Nuker, Disabler",7504,4173,55.61,45501,25616,56.3,99664,54963,55.15,111382,60421,54.25,71830,37860,52.71,33164,17334,52.27,14610,7442,50.94,4196,2076,49.48
+122,Wraith King,str,"Carry, Support, Durable, Disabler, Initiator",4175,2266,54.28,26362,14516,55.06,58733,32403,55.17,66283,36503,55.07,42360,23083,54.49,19084,10251,53.72,8334,4315,51.78,2707,1376,50.83
+123,Zeus,int,"Nuker, Carry",4132,2106,50.97,23721,12487,52.64,51568,27475,53.28,58333,31078,53.28,37821,20047,53.0,17901,9504,53.09,8539,4459,52.22,3400,1791,52.68

belyaeva_ekaterina_lab_3/README.md

@@ -0,0 +1,35 @@
+## Задание 
+
+Решите с помощью библиотечной реализации дерева решений задачу из лабораторной работы «Веб-сервис «Дерево решений» по предмету «Методы  искусственного  интеллекта»на  99%  ваших  данных.  Проверьте работу модели на оставшемся проценте, сделайте вывод
+
+
+## Как запустить лабораторную
+Запустить файл main.py
+## Используемые технологии
+Библиотеки pandas, scikit-learn, их компоненты
+## Описание лабораторной (программы)
+Данный код берет данные из датасета о персонажах Dota 2, где описаны атрибуты персонажей, их роли, название, и как часто их пикают и какой у них винрейт на каждом звании в Доте, от реркута до титана.
+
+В моем случае была поставлена задача определить винрейт персонажа на ранге рекрут в зависимости от его атрибута, роли (я взяла 2 - саппорт или керри), и того, как часто его берут на рекрутах.
+
+Программа берет столбцы Herald Win Rate, Primary Attribute, Herald Picks и Roles, далее проводит фильтрацию столбца Roles и выбирает тех персонажей, у которых есть роль support или carry. Затем создает
+два новых столбца - isCarry и isSupport, так как в столбце Roles несколько значений и его нужно удалить.
+
+Затем данные делятся на обучающую и тестовую выборки и выясняется зависимость винрейта от остальных признаков.
+
+В конце программа выводит, насколько важны были выбранные признаки при определении винрейта и точность модели. 
+
+
+## Результат
+
+В результате получаем следующее:
+
+Feature Importances: [0.08035262 0.82893841 0.00453277 0.08617619]
+Score: 0.23055568233652535
+
+Вывод: самым значимым признаком при определении винрейта стал признак Primary Attribute. На фоне других признаков его значимость сильно выделяется, все остальные признаки уже играют очень маленькую роль.
+
+Точность модели вышла относительно низкой, но это легко объясняется тем, что в Доте невозможно точно предсказать винрейт персонажа, основываясь на подобных признаках. Винрейт предсказывается только лишь тем, какие персонажи сильны в данной мете, что зависит от их скиллов и изменений патча, не описанных в датасете (но и нет такого датасета, где они могли бы быть описаны).
+
+Тем не менее, данная программа дала понять, что на рекрутах на винрейт персонажа сильно влияет его главный атрибут.
+

belyaeva_ekaterina_lab_3/main.py

@@ -0,0 +1,47 @@
+import pandas as pd
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.model_selection import train_test_split
+
+# Загрузка данных
+data = pd.read_csv("Current_Pub_Meta.csv")
+
+# Отбор нужных столбцов
+selected_columns = ['Herald Win Rate', 'Primary Attribute', 'Herald Picks', 'Roles']
+data = data[selected_columns]
+
+# Фильтрация по ролям Carry и Support
+data = data[data['Roles'].apply(lambda x: 'Carry' in x or 'Support' in x)]
+
+# Создание столбцов для каждой роли и заполнение их значениями 1 или 0
+data['IsCarry'] = data['Roles'].apply(lambda x: 1 if 'Carry' in x else 0)
+data['IsSupport'] = data['Roles'].apply(lambda x: 1 if 'Support' in x else 0)
+
+# Удаление столбца Roles
+data.drop('Roles', axis=1, inplace=True)
+
+# Замена категориальных переменных на числовые
+data['Primary Attribute'] = data['Primary Attribute'].map({'str': 0, 'all': 1, 'int': 2, 'agi': 3})
+
+# Разделение данных на обучающую и тестовую выборки
+X = data.drop('Herald Win Rate', axis=1)
+y = data['Herald Win Rate']
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Обучение модели
+model = DecisionTreeRegressor()
+model.fit(X_train, y_train)
+
+# Прогноз на тестовой выборке
+y_pred = model.predict(X_test)
+
+# Вывод обработанных данных
+print("Обработанные данные:")
+print(data)
+
+# Оценка значимости признаков
+feature_importances = model.feature_importances_
+print("Feature Importances:", feature_importances)
+
+# Оценка score модели
+score = model.score(X_test, y_test)
+print("Score:", score)

belyaeva_ekaterina_lab_4/Current_Pub_Meta.csv

@@ -0,0 +1,125 @@
+,Name,Primary Attribute,Roles,Herald Picks,Herald Wins,Herald Win Rate,Guardian Picks,Guardian Wins,Guardian Win Rate,Crusader Picks,Crusader Wins,Crusader Win Rate,Archon Picks,Archon Wins,Archon Win Rate,Legend Picks,Legend Wins,Legend Win Rate,Ancient Picks,Ancient Wins,Ancient Win Rate,Divine Picks,Divine Wins,Divine Win Rate,Immortal Picks,Immortal Wins,Immortal Win Rate
+0,Abaddon,all,"Support, Carry, Durable",1111,575,51.76,6408,3309,51.64,13811,7050,51.05,16497,8530,51.71,11360,5877,51.73,5571,2893,51.93,2632,1345,51.1,991,497,50.15
+1,Alchemist,str,"Carry, Support, Durable, Disabler, Initiator, Nuker",1119,486,43.43,6370,2883,45.26,12238,5617,45.9,13028,6130,47.05,8455,4055,47.96,4120,1984,48.16,2021,1023,50.62,860,424,49.3
+2,Ancient Apparition,int,"Support, Disabler, Nuker",2146,1073,50.0,13697,7069,51.61,30673,16118,52.55,35145,18219,51.84,23114,12166,52.63,10688,5528,51.72,5035,2573,51.1,2134,1076,50.42
+3,Anti-Mage,agi,"Carry, Escape, Nuker",3765,1818,48.29,22050,10774,48.86,47371,23304,49.19,49115,24074,49.02,28599,13991,48.92,12303,5958,48.43,4866,2349,48.27,1502,751,50.0
+4,Arc Warden,agi,"Carry, Escape, Nuker",1448,704,48.62,8047,4162,51.72,14946,7982,53.41,14711,7875,53.53,9472,5167,54.55,4323,2309,53.41,2104,1148,54.56,789,435,55.13
+5,Axe,str,"Initiator, Durable, Disabler, Carry",5343,2880,53.9,32652,17719,54.27,71010,37736,53.14,77869,40559,52.09,49182,25079,50.99,22637,11353,50.15,10114,5000,49.44,3795,1837,48.41
+6,Bane,all,"Support, Disabler, Nuker, Durable",745,334,44.83,4983,2422,48.61,11332,5504,48.57,13633,6767,49.64,10132,5032,49.66,5596,2861,51.13,3028,1555,51.35,1958,1055,53.88
+7,Batrider,all,"Initiator, Disabler, Escape",349,136,38.97,1983,812,40.95,4053,1595,39.35,4725,1861,39.39,3173,1275,40.18,1678,731,43.56,802,362,45.14,497,227,45.67
+8,Beastmaster,all,"Initiator, Disabler, Durable, Nuker",402,174,43.28,2447,1060,43.32,5787,2569,44.39,6930,3092,44.62,5288,2389,45.18,2816,1274,45.24,1593,752,47.21,1176,539,45.83
+9,Bloodseeker,agi,"Carry, Disabler, Nuker, Initiator",2765,1382,49.98,12589,6270,49.81,21781,10683,49.05,20961,10420,49.71,13035,6430,49.33,6210,3006,48.41,2941,1475,50.15,1465,718,49.01
+10,Bounty Hunter,agi,"Escape, Nuker",3852,1868,48.49,19609,9535,48.63,36362,17600,48.4,37059,18314,49.42,22934,11518,50.22,10584,5276,49.85,5105,2594,50.81,2498,1325,53.04
+11,Brewmaster,all,"Carry, Initiator, Durable, Disabler, Nuker",545,280,51.38,3564,1745,48.96,8941,4388,49.08,12340,6111,49.52,11185,5623,50.27,7645,3906,51.09,4812,2478,51.5,3533,1820,51.51
+12,Bristleback,str,"Carry, Durable, Initiator, Nuker",5884,3262,55.44,27952,14587,52.19,48847,24379,49.91,46702,22927,49.09,27466,13319,48.49,12398,5969,48.14,5865,2915,49.7,2639,1304,49.41
+13,Broodmother,all,"Carry, Pusher, Escape, Nuker",456,173,37.94,2048,842,41.11,3444,1462,42.45,3392,1448,42.69,2193,1048,47.79,1203,602,50.04,795,422,53.08,453,230,50.77
+14,Centaur Warrunner,str,"Durable, Initiator, Disabler, Nuker, Escape",1721,911,52.93,11754,6266,53.31,28691,15201,52.98,35369,18741,52.99,25393,13468,53.04,12653,6607,52.22,6124,3181,51.94,2442,1243,50.9
+15,Chaos Knight,str,"Carry, Disabler, Durable, Pusher, Initiator",3032,1639,54.06,16762,8931,53.28,31892,17139,53.74,30697,16435,53.54,18217,9810,53.85,8572,4620,53.9,4230,2291,54.16,1750,943,53.89
+16,Chen,all,"Support, Pusher",284,125,44.01,1450,678,46.76,2969,1345,45.3,3258,1604,49.23,2641,1331,50.4,1488,767,51.55,970,512,52.78,770,448,58.18
+17,Clinkz,agi,"Carry, Escape, Pusher",3151,1608,51.03,13891,7141,51.41,25465,12938,50.81,27327,14066,51.47,18846,9726,51.61,9452,4890,51.74,4765,2475,51.94,2093,1052,50.26
+18,Clockwerk,all,"Initiator, Disabler, Durable, Nuker",816,397,48.65,5860,2837,48.41,14478,6929,47.86,18466,8843,47.89,13143,6301,47.94,6612,3169,47.93,3286,1581,48.11,1378,658,47.75
+19,Crystal Maiden,int,"Support, Disabler, Nuker",4821,2529,52.46,26584,13626,51.26,52168,26040,49.92,52258,25365,48.54,30690,14848,48.38,13295,6404,48.17,5602,2680,47.84,1638,771,47.07
+20,Dark Seer,all,"Initiator, Escape, Disabler",627,320,51.04,3675,1884,51.27,7881,3803,48.26,9589,4844,50.52,7186,3573,49.72,3902,1983,50.82,2145,1095,51.05,1217,593,48.73
+21,Dark Willow,all,"Support, Nuker, Disabler, Escape",2654,1293,48.72,13829,6657,48.14,28142,13480,47.9,32114,15785,49.15,23100,11331,49.05,12052,5909,49.03,6400,3182,49.72,3708,1915,51.65
+22,Dawnbreaker,str,"Carry, Durable",1746,875,50.11,12297,6105,49.65,32398,15921,49.14,44846,21936,48.91,35474,17441,49.17,19770,9832,49.73,10637,5263,49.48,6339,3173,50.06
+23,Dazzle,all,"Support, Nuker, Disabler",2827,1418,50.16,19852,9758,49.15,48236,23691,49.11,56417,27798,49.27,38159,18642,48.85,18695,9199,49.21,8530,4239,49.7,3382,1654,48.91
+24,Death Prophet,int,"Carry, Pusher, Nuker, Disabler",1372,659,48.03,6643,3145,47.34,11987,5729,47.79,12268,5856,47.73,7455,3606,48.37,3591,1698,47.28,1872,902,48.18,926,459,49.57
+25,Disruptor,int,"Support, Disabler, Nuker, Initiator",1541,757,49.12,11104,5331,48.01,27746,13542,48.81,33742,16310,48.34,23173,11096,47.88,10907,5201,47.68,4859,2255,46.41,1863,861,46.22
+26,Doom,str,"Carry, Disabler, Initiator, Durable, Nuker",1049,474,45.19,6112,2767,45.27,13700,6056,44.2,15454,6925,44.81,10727,4842,45.14,5444,2451,45.02,2979,1348,45.25,1545,731,47.31
+27,Dragon Knight,str,"Carry, Pusher, Durable, Disabler, Initiator, Nuker",1950,942,48.31,10643,5274,49.55,20451,9733,47.59,20326,9671,47.58,11674,5544,47.49,4979,2355,47.3,2024,973,48.07,725,341,47.03
+28,Drow Ranger,agi,"Carry, Disabler, Pusher",5737,2904,50.62,29675,14831,49.98,57655,28573,49.56,56682,27927,49.27,34310,16607,48.4,15050,7171,47.65,5947,2815,47.33,1768,788,44.57
+29,Earth Spirit,str,"Nuker, Escape, Disabler, Initiator, Durable",1038,465,44.8,7420,3276,44.15,20807,9432,45.33,30107,14166,47.05,25314,12148,47.99,14579,7041,48.3,7678,3802,49.52,4379,2169,49.53
+30,Earthshaker,str,"Support, Initiator, Disabler, Nuker",5012,2455,48.98,29784,14662,49.23,67050,33111,49.38,79963,39843,49.83,57108,28961,50.71,28650,14591,50.93,14186,7296,51.43,6151,3165,51.46
+31,Elder Titan,str,"Initiator, Disabler, Nuker, Durable",471,212,45.01,2551,1248,48.92,5213,2570,49.3,5572,2809,50.41,3847,1942,50.48,1964,998,50.81,1124,613,54.54,550,292,53.09
+32,Ember Spirit,agi,"Carry, Escape, Nuker, Disabler, Initiator",1514,635,41.94,9180,3836,41.79,20578,8738,42.46,25152,10844,43.11,17703,7814,44.14,8538,3793,44.42,4265,1892,44.36,2065,928,44.94
+33,Enchantress,int,"Support, Pusher, Durable, Disabler",1794,848,47.27,8050,3622,44.99,12921,5686,44.01,11673,4974,42.61,6863,2840,41.38,2948,1212,41.11,1434,654,45.61,806,318,39.45
+34,Enigma,all,"Disabler, Initiator, Pusher",1317,588,44.65,6937,3171,45.71,12908,5979,46.32,11687,5428,46.44,6194,2839,45.83,2493,1127,45.21,938,437,46.59,338,159,47.04
+35,Faceless Void,agi,"Carry, Initiator, Disabler, Escape, Durable",4323,2043,47.26,25618,11902,46.46,54581,25874,47.4,60671,28993,47.79,40137,19611,48.86,19376,9620,49.65,9579,4828,50.4,4439,2256,50.82
+36,Grimstroke,int,"Support, Nuker, Disabler, Escape",1455,694,47.7,9714,4789,49.3,24688,12430,50.35,32027,16094,50.25,23193,11795,50.86,12102,6100,50.4,6191,3047,49.22,3449,1666,48.3
+37,Gyrocopter,agi,"Carry, Nuker, Disabler",2560,1213,47.38,16589,7882,47.51,42072,20358,48.39,54200,26229,48.39,39414,19053,48.34,20164,9781,48.51,10164,4937,48.57,5241,2507,47.83
+38,Hoodwink,agi,"Support, Nuker, Escape, Disabler",2420,1126,46.53,14034,6800,48.45,31382,14964,47.68,35684,16966,47.55,22626,10651,47.07,9949,4690,47.14,4349,2089,48.03,1533,703,45.86
+39,Huskar,str,"Carry, Durable, Initiator",3501,1603,45.79,14234,6639,46.64,22794,10912,47.87,21801,10763,49.37,13811,6919,50.1,6769,3535,52.22,3556,1822,51.24,1936,993,51.29
+40,Invoker,all,"Carry, Nuker, Disabler, Escape, Pusher",4330,2042,47.16,27625,13176,47.7,69035,33863,49.05,86745,43479,50.12,61821,31510,50.97,31459,16321,51.88,15431,8195,53.11,7852,4148,52.83
+41,Io,all,"Support, Escape, Nuker",1274,615,48.27,6158,2999,48.7,12762,6247,48.95,14216,7024,49.41,9564,4843,50.64,5301,2685,50.65,2789,1463,52.46,1464,773,52.8
+42,Jakiro,int,"Support, Nuker, Pusher, Disabler",3147,1708,54.27,22718,12413,54.64,56736,30984,54.61,70038,37473,53.5,46389,24997,53.89,22084,11639,52.7,9838,5103,51.87,3282,1729,52.68
+43,Juggernaut,agi,"Carry, Pusher, Escape",5585,2711,48.54,30394,14800,48.69,62313,30581,49.08,65590,32344,49.31,39235,19326,49.26,16334,8012,49.05,6419,3066,47.76,1576,731,46.38
+44,Keeper of the Light,int,"Support, Nuker, Disabler",896,353,39.4,5051,2216,43.87,10452,4579,43.81,11614,5322,45.82,7870,3627,46.09,4268,2001,46.88,2147,1043,48.58,1333,588,44.11
+45,Kunkka,str,"Carry, Support, Disabler, Initiator, Durable, Nuker",2251,1124,49.93,13474,6828,50.68,31210,16196,51.89,39691,21293,53.65,30314,16458,54.29,15706,8793,55.98,7884,4339,55.04,3458,1898,54.89
+46,Legion Commander,str,"Carry, Disabler, Initiator, Durable, Nuker",6263,3264,52.12,37100,19157,51.64,81491,41557,51.0,91431,46558,50.92,59383,29917,50.38,27945,13917,49.8,13193,6587,49.93,5601,2745,49.01
+47,Leshrac,int,"Carry, Support, Nuker, Pusher, Disabler",674,316,46.88,3872,1799,46.46,7490,3433,45.83,7903,3604,45.6,5322,2526,47.46,2687,1298,48.31,1325,647,48.83,721,357,49.51
+48,Lich,int,"Support, Nuker",2700,1412,52.3,16646,8820,52.99,37785,19685,52.1,45471,23554,51.8,31203,16108,51.62,15530,7821,50.36,7243,3597,49.66,2520,1258,49.92
+49,Lifestealer,str,"Carry, Durable, Escape, Disabler",2515,1213,48.23,14131,6978,49.38,29724,14627,49.21,31211,15581,49.92,18970,9481,49.98,8689,4400,50.64,3630,1821,50.17,1229,617,50.2
+50,Lina,int,"Support, Carry, Nuker, Disabler",4512,2030,44.99,21927,10156,46.32,45301,21210,46.82,54229,25956,47.86,40016,19138,47.83,21072,10112,47.99,10481,5031,48.0,4369,2138,48.94
+51,Lion,int,"Support, Disabler, Nuker, Initiator",6204,2855,46.02,37869,17465,46.12,80124,36649,45.74,84390,38176,45.24,50720,22914,45.18,21698,9784,45.09,9308,4280,45.98,3220,1496,46.46
+52,Lone Druid,all,"Carry, Pusher, Durable",909,483,53.14,4714,2421,51.36,10987,5858,53.32,14580,7968,54.65,11810,6490,54.95,7241,3971,54.84,4024,2240,55.67,2303,1259,54.67
+53,Luna,agi,"Carry, Nuker, Pusher",1927,904,46.91,9091,4271,46.98,16571,7922,47.81,16035,7615,47.49,9728,4634,47.64,4463,2103,47.12,1912,911,47.65,719,322,44.78
+54,Lycan,all,"Carry, Pusher, Durable, Escape",374,174,46.52,1894,915,48.31,3691,1744,47.25,3824,1905,49.82,2694,1332,49.44,1460,753,51.58,827,411,49.7,532,289,54.32
+55,Magnus,all,"Initiator, Disabler, Nuker, Escape",770,339,44.03,5789,2651,45.79,17837,7954,44.59,26126,12058,46.15,20634,9592,46.49,10574,5056,47.82,4565,2073,45.41,1606,751,46.76
+56,Marci,all,"Support, Carry, Initiator, Disabler, Escape",1370,620,45.26,7092,3252,45.85,15199,7240,47.63,18485,8874,48.01,13308,6305,47.38,7176,3476,48.44,3689,1882,51.02,1746,883,50.57
+57,Mars,str,"Carry, Initiator, Disabler, Durable",862,375,43.5,5719,2529,44.22,15156,6756,44.58,20719,9369,45.22,16419,7387,44.99,9044,4052,44.8,4536,2093,46.14,1926,868,45.07
+58,Medusa,agi,"Carry, Disabler, Durable",1898,902,47.52,9289,4512,48.57,16504,7818,47.37,14796,6886,46.54,7488,3449,46.06,2775,1270,45.77,1073,482,44.92,394,184,46.7
+59,Meepo,agi,"Carry, Escape, Nuker, Disabler, Initiator, Pusher",1004,523,52.09,3970,1990,50.13,6904,3587,51.96,7166,3646,50.88,4906,2563,52.24,2383,1282,53.8,1139,588,51.62,585,300,51.28
+60,Mirana,all,"Carry, Support, Escape, Nuker, Disabler",2499,1193,47.74,16954,8135,47.98,39985,19097,47.76,45169,21554,47.72,28467,13456,47.27,12800,6047,47.24,5272,2500,47.42,1824,874,47.92
+61,Monkey King,agi,"Carry, Escape, Disabler, Initiator",3191,1384,43.37,17306,7544,43.59,35734,16113,45.09,40778,18322,44.93,27558,12630,45.83,14034,6433,45.84,6650,3152,47.4,3040,1440,47.37
+62,Morphling,agi,"Carry, Escape, Durable, Nuker, Disabler",1521,690,45.36,8620,4006,46.47,18075,8161,45.15,20414,9235,45.24,14395,6530,45.36,7697,3551,46.13,4432,2050,46.25,2560,1190,46.48
+63,Muerta,int,"Carry, Nuker, Disabler",2130,1089,51.13,10787,5740,53.21,22602,11898,52.64,27609,14495,52.5,20175,10465,51.87,10662,5518,51.75,5462,2759,50.51,2948,1517,51.46
+64,Naga Siren,agi,"Carry, Support, Pusher, Disabler, Initiator, Escape",1502,804,53.53,6495,3356,51.67,10423,5234,50.22,9830,4929,50.14,6057,2971,49.05,3216,1675,52.08,1855,933,50.3,1242,634,51.05
+65,Nature's Prophet,int,"Carry, Pusher, Escape, Nuker",5991,3029,50.56,36433,18143,49.8,83118,42095,50.64,100341,51268,51.09,69436,35870,51.66,34256,17858,52.13,16585,8745,52.73,7182,3755,52.28
+66,Necrophos,int,"Carry, Nuker, Durable, Disabler",4776,2702,56.57,28535,15771,55.27,62186,34285,55.13,70212,38163,54.35,46539,24708,53.09,21607,11302,52.31,9677,4994,51.61,3418,1733,50.7
+67,Night Stalker,str,"Carry, Initiator, Durable, Disabler, Nuker",1189,594,49.96,7868,3892,49.47,19446,10004,51.45,25524,13506,52.91,20138,10828,53.77,10767,5651,52.48,5499,2889,52.54,2415,1257,52.05
+68,Nyx Assassin,all,"Disabler, Nuker, Initiator, Escape",1718,867,50.47,10925,5525,50.57,27207,14073,51.73,34684,18059,52.07,25736,13572,52.74,13313,7093,53.28,6485,3444,53.11,2852,1468,51.47
+69,Ogre Magi,str,"Support, Nuker, Disabler, Durable, Initiator",5331,2845,53.37,31507,16299,51.73,62954,32248,51.22,61758,31373,50.8,33746,16988,50.34,13262,6654,50.17,4861,2420,49.78,1271,654,51.46
+70,Omniknight,str,"Support, Durable, Nuker",975,479,49.13,6426,3109,48.38,14641,7319,49.99,17258,8731,50.59,11695,5916,50.59,5746,2993,52.09,2870,1469,51.18,1333,656,49.21
+71,Oracle,int,"Support, Nuker, Disabler, Escape",796,384,48.24,4857,2417,49.76,13141,6645,50.57,18944,9853,52.01,15221,7964,52.32,8356,4458,53.35,4475,2380,53.18,1905,1018,53.44
+72,Outworld Destroyer,int,"Carry, Nuker, Disabler",2226,1118,50.22,13388,6864,51.27,33284,17362,52.16,43991,23377,53.14,32021,16994,53.07,16655,8724,52.38,8123,4218,51.93,3176,1649,51.92
+73,Pangolier,all,"Carry, Nuker, Disabler, Durable, Escape, Initiator",1156,534,46.19,7189,3209,44.64,17802,7937,44.58,25785,11677,45.29,21727,10144,46.69,13064,6351,48.61,7567,3737,49.39,5275,2734,51.83
+74,Phantom Assassin,agi,"Carry, Escape",8553,4426,51.75,48549,25553,52.63,104756,54881,52.39,119332,62511,52.38,79140,41143,51.99,37399,19325,51.67,17774,9077,51.07,7819,3856,49.32
+75,Phantom Lancer,agi,"Carry, Escape, Pusher, Nuker",3641,1960,53.83,19550,10374,53.06,38576,20633,53.49,41505,22310,53.75,26401,14268,54.04,12437,6590,52.99,5708,2985,52.3,2383,1243,52.16
+76,Phoenix,all,"Support, Nuker, Initiator, Escape, Disabler",743,315,42.4,5231,2471,47.24,13950,6633,47.55,18350,8864,48.31,13972,6715,48.06,7787,3761,48.3,4322,2132,49.33,2610,1325,50.77
+77,Primal Beast,str,"Initiator, Durable, Disabler",1455,701,48.18,9333,4448,47.66,22800,11058,48.5,30084,14643,48.67,24307,11993,49.34,13970,6991,50.04,7742,3890,50.25,4625,2407,52.04
+78,Puck,int,"Initiator, Disabler, Escape, Nuker",871,399,45.81,5773,2628,45.52,16596,7578,45.66,24480,11315,46.22,20070,9497,47.32,11023,5298,48.06,5656,2714,47.98,2555,1200,46.97
+79,Pudge,str,"Disabler, Initiator, Durable, Nuker",7677,3796,49.45,50891,24776,48.68,114784,56289,49.04,129604,63097,48.68,85800,41542,48.42,41730,20239,48.5,19823,9530,48.08,7112,3431,48.24
+80,Pugna,int,"Nuker, Pusher",2075,944,45.49,9998,4695,46.96,18962,8958,47.24,20240,9965,49.23,12807,6199,48.4,5825,2855,49.01,2758,1387,50.29,1195,592,49.54
+81,Queen of Pain,int,"Carry, Nuker, Escape",2287,1100,48.1,15119,7354,48.64,37137,18118,48.79,47706,23657,49.59,35500,18018,50.75,18405,9289,50.47,9243,4689,50.73,4227,2113,49.99
+82,Razor,agi,"Carry, Durable, Nuker, Pusher",2470,1231,49.84,12000,5964,49.7,24666,12142,49.23,30334,14844,48.94,21832,10558,48.36,11917,5679,47.65,6092,2912,47.8,3144,1551,49.33
+83,Riki,agi,"Carry, Escape, Disabler",3684,1929,52.36,19022,9891,52.0,35638,18582,52.14,33908,17415,51.36,20194,10312,51.06,8726,4377,50.16,3735,1855,49.67,1160,559,48.19
+84,Rubick,int,"Support, Disabler, Nuker",3090,1404,45.44,21639,9303,42.99,57417,24590,42.83,74874,32603,43.54,55186,24219,43.89,28206,12568,44.56,13732,6106,44.47,5764,2642,45.84
+85,Sand King,all,"Initiator, Disabler, Support, Nuker, Escape",2633,1513,57.46,13097,7323,55.91,25271,13807,54.64,26724,14323,53.6,17384,9144,52.6,7907,4104,51.9,3394,1719,50.65,1211,611,50.45
+86,Shadow Demon,int,"Support, Disabler, Initiator, Nuker",547,236,43.14,3252,1426,43.85,7920,3524,44.49,9752,4551,46.67,7404,3467,46.83,3956,1876,47.42,2076,1004,48.36,1054,497,47.15
+87,Shadow Fiend,agi,"Carry, Nuker",5051,2544,50.37,27255,14064,51.6,58589,29830,50.91,65429,33097,50.58,41810,21189,50.68,18766,9401,50.1,8232,4000,48.59,3016,1430,47.41
+88,Shadow Shaman,int,"Support, Pusher, Disabler, Nuker, Initiator",5323,2795,52.51,29733,15606,52.49,58894,31236,53.04,58765,30895,52.57,34475,18242,52.91,15166,7986,52.66,6377,3323,52.11,2413,1253,51.93
+89,Silencer,int,"Carry, Support, Disabler, Initiator, Nuker",4229,2324,54.95,27878,14960,53.66,61698,33081,53.62,65256,34458,52.8,38589,19853,51.45,16889,8653,51.23,6836,3416,49.97,2236,1105,49.42
+90,Skywrath Mage,int,"Support, Nuker, Disabler",4000,2030,50.75,22783,11675,51.24,46512,23624,50.79,51329,25706,50.08,34167,17364,50.82,16693,8415,50.41,8496,4208,49.53,4389,2069,47.14
+91,Slardar,str,"Carry, Durable, Initiator, Disabler, Escape",3935,2129,54.1,21523,11602,53.91,43947,23701,53.93,47721,25633,53.71,29887,16132,53.98,14233,7722,54.25,6530,3467,53.09,2322,1205,51.89
+92,Slark,agi,"Carry, Escape, Disabler, Nuker",4815,2521,52.36,29413,14762,50.19,64004,31771,49.64,70173,34411,49.04,44780,21926,48.96,20864,10270,49.22,9969,4962,49.77,4565,2394,52.44
+93,Snapfire,all,"Support, Nuker, Disabler, Escape",1524,682,44.75,10646,4576,42.98,27103,12120,44.72,34711,15412,44.4,24351,10786,44.29,11723,5131,43.77,5227,2294,43.89,1987,868,43.68
+94,Sniper,agi,"Carry, Nuker",8022,4079,50.85,44508,22727,51.06,88690,45223,50.99,87190,44086,50.56,47411,23648,49.88,18092,8924,49.33,6130,3040,49.59,1370,662,48.32
+95,Spectre,agi,"Carry, Durable, Escape",3454,2008,58.14,22097,12356,55.92,49157,26961,54.85,55914,30100,53.83,36321,19338,53.24,16946,8960,52.87,7921,4163,52.56,2568,1370,53.35
+96,Spirit Breaker,str,"Carry, Initiator, Disabler, Durable, Escape",4788,2423,50.61,26662,13530,50.75,56535,28908,51.13,63991,32249,50.4,42512,21357,50.24,20119,9926,49.34,9499,4814,50.68,3761,1884,50.09
+97,Storm Spirit,int,"Carry, Escape, Nuker, Initiator, Disabler",2202,1001,45.46,11656,5197,44.59,25644,11806,46.04,30968,14210,45.89,21680,10197,47.03,10810,5025,46.48,5278,2382,45.13,2363,1122,47.48
+98,Sven,str,"Carry, Disabler, Initiator, Durable, Nuker",3552,1761,49.58,19792,9744,49.23,41296,20478,49.59,48709,24228,49.74,35460,17828,50.28,19795,10065,50.85,11014,5655,51.34,6701,3387,50.54
+99,Techies,all,"Nuker, Disabler",2356,1131,48.01,13105,6245,47.65,27293,12893,47.24,29180,13507,46.29,18216,8407,46.15,8266,3771,45.62,3459,1644,47.53,1319,591,44.81
+100,Templar Assassin,agi,"Carry, Escape",2142,955,44.58,10932,4758,43.52,21211,9445,44.53,23928,10909,45.59,17399,8242,47.37,9567,4656,48.67,5525,2708,49.01,3524,1775,50.37
+101,Terrorblade,agi,"Carry, Pusher, Nuker",1115,484,43.41,5686,2430,42.74,10856,4638,42.72,11518,5041,43.77,8059,3540,43.93,4192,1827,43.58,2419,1082,44.73,1621,700,43.18
+102,Tidehunter,str,"Initiator, Durable, Disabler, Nuker, Carry",1835,855,46.59,11159,5369,48.11,26222,12699,48.43,30735,14879,48.41,20523,9727,47.4,9731,4740,48.71,4426,2079,46.97,1998,936,46.85
+103,Timbersaw,all,"Nuker, Durable, Escape",1050,448,42.67,5854,2584,44.14,12301,5391,43.83,14295,6097,42.65,9697,4217,43.49,4992,2163,43.33,2419,1021,42.21,1139,471,41.35
+104,Tinker,int,"Carry, Nuker, Pusher",2106,944,44.82,11058,5200,47.02,24263,11826,48.74,27531,13614,49.45,19017,9732,51.18,9416,4875,51.77,4700,2466,52.47,1951,1036,53.1
+105,Tiny,str,"Carry, Nuker, Pusher, Initiator, Durable, Disabler",1434,654,45.61,7742,3452,44.59,15936,6950,43.61,17139,7468,43.57,11269,4991,44.29,5485,2491,45.41,2599,1216,46.79,1058,519,49.05
+106,Treant Protector,str,"Support, Initiator, Durable, Disabler, Escape",1646,899,54.62,11430,5881,51.45,28752,15124,52.6,36093,19344,53.59,28762,15532,54.0,16751,9227,55.08,9870,5468,55.4,6801,3855,56.68
+107,Troll Warlord,agi,"Carry, Pusher, Disabler, Durable",3176,1720,54.16,14007,7445,53.15,24729,13022,52.66,25424,13228,52.03,17362,9030,52.01,9427,4913,52.12,4767,2499,52.42,2341,1242,53.05
+108,Tusk,str,"Initiator, Disabler, Nuker",1263,565,44.73,8338,3777,45.3,19642,8869,45.15,25308,11520,45.52,18927,8853,46.77,10100,4820,47.72,5220,2502,47.93,2350,1157,49.23
+109,Underlord,str,"Support, Nuker, Disabler, Durable, Escape",797,405,50.82,4583,2341,51.08,10067,5057,50.23,11650,5786,49.67,7224,3561,49.29,3310,1591,48.07,1368,673,49.2,395,190,48.1
+110,Undying,str,"Support, Durable, Disabler, Nuker",3170,1620,51.1,19403,10116,52.14,40582,21110,52.02,40850,21182,51.85,23985,12454,51.92,10395,5389,51.84,4541,2336,51.44,2064,1012,49.03
+111,Ursa,agi,"Carry, Durable, Disabler",2801,1273,45.45,15132,7038,46.51,33269,15478,46.52,40822,19264,47.19,29348,14011,47.74,15262,7375,48.32,7507,3622,48.25,3004,1473,49.03
+112,Vengeful Spirit,all,"Support, Initiator, Disabler, Nuker, Escape",2186,1108,50.69,15817,8285,52.38,41843,21809,52.12,57524,30476,52.98,45512,24120,53.0,25581,13382,52.31,13758,7121,51.76,8276,4303,51.99
+113,Venomancer,all,"Support, Nuker, Initiator, Pusher, Disabler",2309,1187,51.41,14669,7463,50.88,34787,18020,51.8,41797,21690,51.89,28706,15085,52.55,13974,7338,52.51,6538,3495,53.46,2794,1459,52.22
+114,Viper,agi,"Carry, Durable, Initiator, Disabler",4100,2057,50.17,18991,9510,50.08,33517,16923,50.49,32728,16677,50.96,18537,9427,50.86,7851,3928,50.03,3260,1652,50.67,1176,610,51.87
+115,Visage,all,"Support, Nuker, Durable, Disabler, Pusher",331,171,51.66,1638,813,49.63,3240,1577,48.67,3840,1986,51.72,3108,1609,51.77,1995,1055,52.88,1309,702,53.63,858,457,53.26
+116,Void Spirit,all,"Carry, Escape, Nuker, Disabler",1565,727,46.45,8672,4096,47.23,20010,9694,48.45,25213,12376,49.09,18817,9231,49.06,10026,4920,49.07,4788,2319,48.43,2006,964,48.06
+117,Warlock,int,"Support, Initiator, Disabler",2547,1369,53.75,18931,10331,54.57,49795,26999,54.22,66697,36220,54.31,48401,25668,53.03,24999,12942,51.77,12575,6356,50.54,6183,2934,47.45
+118,Weaver,agi,"Carry, Escape",2818,1389,49.29,13873,6770,48.8,23493,11571,49.25,21545,10694,49.64,12911,6427,49.78,5809,2928,50.4,2960,1455,49.16,1303,719,55.18
+119,Windranger,all,"Carry, Support, Disabler, Escape, Nuker",3861,1814,46.98,19934,9223,46.27,40644,18807,46.27,44476,20652,46.43,28952,13508,46.66,13418,6297,46.93,5898,2782,47.17,2374,1142,48.1
+120,Winter Wyvern,all,"Support, Disabler, Nuker",821,371,45.19,5168,2424,46.9,10544,5014,47.55,11184,5308,47.46,7426,3512,47.29,3730,1854,49.71,1862,934,50.16,944,464,49.15
+121,Witch Doctor,int,"Support, Nuker, Disabler",7504,4173,55.61,45501,25616,56.3,99664,54963,55.15,111382,60421,54.25,71830,37860,52.71,33164,17334,52.27,14610,7442,50.94,4196,2076,49.48
+122,Wraith King,str,"Carry, Support, Durable, Disabler, Initiator",4175,2266,54.28,26362,14516,55.06,58733,32403,55.17,66283,36503,55.07,42360,23083,54.49,19084,10251,53.72,8334,4315,51.78,2707,1376,50.83
+123,Zeus,int,"Nuker, Carry",4132,2106,50.97,23721,12487,52.64,51568,27475,53.28,58333,31078,53.28,37821,20047,53.0,17901,9504,53.09,8539,4459,52.22,3400,1791,52.68

belyaeva_ekaterina_lab_4/README.md

@@ -0,0 +1,31 @@
+## Задание 
+
+Использовать метод кластеризациипо варианту для данных из таблицы 1  по  варианту(таблица  9),самостоятельно  сформулировав  задачу. Интерпретировать результаты и оценить, насколько хорошо он подходит для решения сформулированной вами задачи  
+Вариант 6 - dendogram
+
+## Как запустить лабораторную
+Запустить файл main.py
+## Используемые технологии
+Библиотеки pandas, matplotlib, scipy, их компоненты
+## Описание лабораторной (программы)
+Данный код берет данные из датасета о персонажах Dota 2, где описаны атрибуты персонажей, их роли, название, и как часто их пикают и какой у них винрейт на каждом звании в Доте, от реркута до титана.
+
+В моем случае была поставлена задача сгруппировать персонажей по их винрейту и частоте их пиков на определенных рангах.
+
+Программа берет столбцы Name, Herald Win Rate, Herald Picks, создает матрицу для анализа и вычисляет матрицу связей, а затем выводит дендограмму, где персонажи объединены по тому, как часто их пикают и какой у них винрейт.
+
+## Результат
+
+В результате получаем дендограмму, где персонажи сгруппированы по частоте пиков и винрейту. Наглядное представление оказалось очень точным и такой способ решения поставленной задачи выполнил свою работу хорошо.
+
+Например, на диаграмме ниже можно обратить внимание на то, что на ранге рекрут персонажи Phantom Asassin, Witch Doctor, Sniper и Pudge стоят вместе в правом нижнем углу. Такое наблюдение говорит о том, что датасет очень приближен к реальным данным и составлен правильно, а так же о том, что программа работает верно и выдает правильный, приближенный к реальности, результат.
+
+![heraldInfo.png](heraldInfo.png)
+
+Если же посмотреть на результат по данным для ранга титан, можно увидеть других героев, объединенных друг с другом по тому же приципу.
+
+![ImmortalInfo.png](ImmortalInfo.png)
+
+Сначала я хотела объединить героев по их винрейту на всех рангах, но такая информация не несет в себе много смысла, поэтому задача, которую я описала выше, сформулирована правильно, несет в себе смысл и решается заданным способом. 
+
+Такую статистику можно посмотреть по любому из рангов, заменив в коде слово Herald на интересующий ранг.

belyaeva_ekaterina_lab_4/main.py

@@ -0,0 +1,29 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+from scipy.cluster.hierarchy import dendrogram, linkage
+
+# Загрузка данных
+data = pd.read_csv('Current_Pub_Meta.csv')
+
+# Выбор нужных столбцов
+selected_columns = ['Name', 'Herald Picks', 'Herald Win Rate']
+data = data[selected_columns]
+
+# Создание матрицы для анализа
+matrix = data.drop('Name', axis=1).values
+
+# Вычисление матрицы связей
+linked = linkage(matrix, 'ward')
+
+# Рисование дендрограммы
+plt.figure(figsize=(10, 6))
+dendrogram(linked,
+           orientation='top',
+           labels=data['Name'].tolist(),
+           distance_sort='descending',
+           show_leaf_counts=True)
+plt.title('Dendrogram of Hero Win Percentage')
+plt.xlabel('Heroes')
+plt.ylabel('Distance')
+plt.xticks(rotation=90)
+plt.show()

belyaeva_ekaterina_lab_5/Current_Pub_Meta.csv

@@ -0,0 +1,125 @@
+,Name,Primary Attribute,Roles,Herald Picks,Herald Wins,Herald Win Rate,Guardian Picks,Guardian Wins,Guardian Win Rate,Crusader Picks,Crusader Wins,Crusader Win Rate,Archon Picks,Archon Wins,Archon Win Rate,Legend Picks,Legend Wins,Legend Win Rate,Ancient Picks,Ancient Wins,Ancient Win Rate,Divine Picks,Divine Wins,Divine Win Rate,Immortal Picks,Immortal Wins,Immortal Win Rate
+0,Abaddon,all,"Support, Carry, Durable",1111,575,51.76,6408,3309,51.64,13811,7050,51.05,16497,8530,51.71,11360,5877,51.73,5571,2893,51.93,2632,1345,51.1,991,497,50.15
+1,Alchemist,str,"Carry, Support, Durable, Disabler, Initiator, Nuker",1119,486,43.43,6370,2883,45.26,12238,5617,45.9,13028,6130,47.05,8455,4055,47.96,4120,1984,48.16,2021,1023,50.62,860,424,49.3
+2,Ancient Apparition,int,"Support, Disabler, Nuker",2146,1073,50.0,13697,7069,51.61,30673,16118,52.55,35145,18219,51.84,23114,12166,52.63,10688,5528,51.72,5035,2573,51.1,2134,1076,50.42
+3,Anti-Mage,agi,"Carry, Escape, Nuker",3765,1818,48.29,22050,10774,48.86,47371,23304,49.19,49115,24074,49.02,28599,13991,48.92,12303,5958,48.43,4866,2349,48.27,1502,751,50.0
+4,Arc Warden,agi,"Carry, Escape, Nuker",1448,704,48.62,8047,4162,51.72,14946,7982,53.41,14711,7875,53.53,9472,5167,54.55,4323,2309,53.41,2104,1148,54.56,789,435,55.13
+5,Axe,str,"Initiator, Durable, Disabler, Carry",5343,2880,53.9,32652,17719,54.27,71010,37736,53.14,77869,40559,52.09,49182,25079,50.99,22637,11353,50.15,10114,5000,49.44,3795,1837,48.41
+6,Bane,all,"Support, Disabler, Nuker, Durable",745,334,44.83,4983,2422,48.61,11332,5504,48.57,13633,6767,49.64,10132,5032,49.66,5596,2861,51.13,3028,1555,51.35,1958,1055,53.88
+7,Batrider,all,"Initiator, Disabler, Escape",349,136,38.97,1983,812,40.95,4053,1595,39.35,4725,1861,39.39,3173,1275,40.18,1678,731,43.56,802,362,45.14,497,227,45.67
+8,Beastmaster,all,"Initiator, Disabler, Durable, Nuker",402,174,43.28,2447,1060,43.32,5787,2569,44.39,6930,3092,44.62,5288,2389,45.18,2816,1274,45.24,1593,752,47.21,1176,539,45.83
+9,Bloodseeker,agi,"Carry, Disabler, Nuker, Initiator",2765,1382,49.98,12589,6270,49.81,21781,10683,49.05,20961,10420,49.71,13035,6430,49.33,6210,3006,48.41,2941,1475,50.15,1465,718,49.01
+10,Bounty Hunter,agi,"Escape, Nuker",3852,1868,48.49,19609,9535,48.63,36362,17600,48.4,37059,18314,49.42,22934,11518,50.22,10584,5276,49.85,5105,2594,50.81,2498,1325,53.04
+11,Brewmaster,all,"Carry, Initiator, Durable, Disabler, Nuker",545,280,51.38,3564,1745,48.96,8941,4388,49.08,12340,6111,49.52,11185,5623,50.27,7645,3906,51.09,4812,2478,51.5,3533,1820,51.51
+12,Bristleback,str,"Carry, Durable, Initiator, Nuker",5884,3262,55.44,27952,14587,52.19,48847,24379,49.91,46702,22927,49.09,27466,13319,48.49,12398,5969,48.14,5865,2915,49.7,2639,1304,49.41
+13,Broodmother,all,"Carry, Pusher, Escape, Nuker",456,173,37.94,2048,842,41.11,3444,1462,42.45,3392,1448,42.69,2193,1048,47.79,1203,602,50.04,795,422,53.08,453,230,50.77
+14,Centaur Warrunner,str,"Durable, Initiator, Disabler, Nuker, Escape",1721,911,52.93,11754,6266,53.31,28691,15201,52.98,35369,18741,52.99,25393,13468,53.04,12653,6607,52.22,6124,3181,51.94,2442,1243,50.9
+15,Chaos Knight,str,"Carry, Disabler, Durable, Pusher, Initiator",3032,1639,54.06,16762,8931,53.28,31892,17139,53.74,30697,16435,53.54,18217,9810,53.85,8572,4620,53.9,4230,2291,54.16,1750,943,53.89
+16,Chen,all,"Support, Pusher",284,125,44.01,1450,678,46.76,2969,1345,45.3,3258,1604,49.23,2641,1331,50.4,1488,767,51.55,970,512,52.78,770,448,58.18
+17,Clinkz,agi,"Carry, Escape, Pusher",3151,1608,51.03,13891,7141,51.41,25465,12938,50.81,27327,14066,51.47,18846,9726,51.61,9452,4890,51.74,4765,2475,51.94,2093,1052,50.26
+18,Clockwerk,all,"Initiator, Disabler, Durable, Nuker",816,397,48.65,5860,2837,48.41,14478,6929,47.86,18466,8843,47.89,13143,6301,47.94,6612,3169,47.93,3286,1581,48.11,1378,658,47.75
+19,Crystal Maiden,int,"Support, Disabler, Nuker",4821,2529,52.46,26584,13626,51.26,52168,26040,49.92,52258,25365,48.54,30690,14848,48.38,13295,6404,48.17,5602,2680,47.84,1638,771,47.07
+20,Dark Seer,all,"Initiator, Escape, Disabler",627,320,51.04,3675,1884,51.27,7881,3803,48.26,9589,4844,50.52,7186,3573,49.72,3902,1983,50.82,2145,1095,51.05,1217,593,48.73
+21,Dark Willow,all,"Support, Nuker, Disabler, Escape",2654,1293,48.72,13829,6657,48.14,28142,13480,47.9,32114,15785,49.15,23100,11331,49.05,12052,5909,49.03,6400,3182,49.72,3708,1915,51.65
+22,Dawnbreaker,str,"Carry, Durable",1746,875,50.11,12297,6105,49.65,32398,15921,49.14,44846,21936,48.91,35474,17441,49.17,19770,9832,49.73,10637,5263,49.48,6339,3173,50.06
+23,Dazzle,all,"Support, Nuker, Disabler",2827,1418,50.16,19852,9758,49.15,48236,23691,49.11,56417,27798,49.27,38159,18642,48.85,18695,9199,49.21,8530,4239,49.7,3382,1654,48.91
+24,Death Prophet,int,"Carry, Pusher, Nuker, Disabler",1372,659,48.03,6643,3145,47.34,11987,5729,47.79,12268,5856,47.73,7455,3606,48.37,3591,1698,47.28,1872,902,48.18,926,459,49.57
+25,Disruptor,int,"Support, Disabler, Nuker, Initiator",1541,757,49.12,11104,5331,48.01,27746,13542,48.81,33742,16310,48.34,23173,11096,47.88,10907,5201,47.68,4859,2255,46.41,1863,861,46.22
+26,Doom,str,"Carry, Disabler, Initiator, Durable, Nuker",1049,474,45.19,6112,2767,45.27,13700,6056,44.2,15454,6925,44.81,10727,4842,45.14,5444,2451,45.02,2979,1348,45.25,1545,731,47.31
+27,Dragon Knight,str,"Carry, Pusher, Durable, Disabler, Initiator, Nuker",1950,942,48.31,10643,5274,49.55,20451,9733,47.59,20326,9671,47.58,11674,5544,47.49,4979,2355,47.3,2024,973,48.07,725,341,47.03
+28,Drow Ranger,agi,"Carry, Disabler, Pusher",5737,2904,50.62,29675,14831,49.98,57655,28573,49.56,56682,27927,49.27,34310,16607,48.4,15050,7171,47.65,5947,2815,47.33,1768,788,44.57
+29,Earth Spirit,str,"Nuker, Escape, Disabler, Initiator, Durable",1038,465,44.8,7420,3276,44.15,20807,9432,45.33,30107,14166,47.05,25314,12148,47.99,14579,7041,48.3,7678,3802,49.52,4379,2169,49.53
+30,Earthshaker,str,"Support, Initiator, Disabler, Nuker",5012,2455,48.98,29784,14662,49.23,67050,33111,49.38,79963,39843,49.83,57108,28961,50.71,28650,14591,50.93,14186,7296,51.43,6151,3165,51.46
+31,Elder Titan,str,"Initiator, Disabler, Nuker, Durable",471,212,45.01,2551,1248,48.92,5213,2570,49.3,5572,2809,50.41,3847,1942,50.48,1964,998,50.81,1124,613,54.54,550,292,53.09
+32,Ember Spirit,agi,"Carry, Escape, Nuker, Disabler, Initiator",1514,635,41.94,9180,3836,41.79,20578,8738,42.46,25152,10844,43.11,17703,7814,44.14,8538,3793,44.42,4265,1892,44.36,2065,928,44.94
+33,Enchantress,int,"Support, Pusher, Durable, Disabler",1794,848,47.27,8050,3622,44.99,12921,5686,44.01,11673,4974,42.61,6863,2840,41.38,2948,1212,41.11,1434,654,45.61,806,318,39.45
+34,Enigma,all,"Disabler, Initiator, Pusher",1317,588,44.65,6937,3171,45.71,12908,5979,46.32,11687,5428,46.44,6194,2839,45.83,2493,1127,45.21,938,437,46.59,338,159,47.04
+35,Faceless Void,agi,"Carry, Initiator, Disabler, Escape, Durable",4323,2043,47.26,25618,11902,46.46,54581,25874,47.4,60671,28993,47.79,40137,19611,48.86,19376,9620,49.65,9579,4828,50.4,4439,2256,50.82
+36,Grimstroke,int,"Support, Nuker, Disabler, Escape",1455,694,47.7,9714,4789,49.3,24688,12430,50.35,32027,16094,50.25,23193,11795,50.86,12102,6100,50.4,6191,3047,49.22,3449,1666,48.3
+37,Gyrocopter,agi,"Carry, Nuker, Disabler",2560,1213,47.38,16589,7882,47.51,42072,20358,48.39,54200,26229,48.39,39414,19053,48.34,20164,9781,48.51,10164,4937,48.57,5241,2507,47.83
+38,Hoodwink,agi,"Support, Nuker, Escape, Disabler",2420,1126,46.53,14034,6800,48.45,31382,14964,47.68,35684,16966,47.55,22626,10651,47.07,9949,4690,47.14,4349,2089,48.03,1533,703,45.86
+39,Huskar,str,"Carry, Durable, Initiator",3501,1603,45.79,14234,6639,46.64,22794,10912,47.87,21801,10763,49.37,13811,6919,50.1,6769,3535,52.22,3556,1822,51.24,1936,993,51.29
+40,Invoker,all,"Carry, Nuker, Disabler, Escape, Pusher",4330,2042,47.16,27625,13176,47.7,69035,33863,49.05,86745,43479,50.12,61821,31510,50.97,31459,16321,51.88,15431,8195,53.11,7852,4148,52.83
+41,Io,all,"Support, Escape, Nuker",1274,615,48.27,6158,2999,48.7,12762,6247,48.95,14216,7024,49.41,9564,4843,50.64,5301,2685,50.65,2789,1463,52.46,1464,773,52.8
+42,Jakiro,int,"Support, Nuker, Pusher, Disabler",3147,1708,54.27,22718,12413,54.64,56736,30984,54.61,70038,37473,53.5,46389,24997,53.89,22084,11639,52.7,9838,5103,51.87,3282,1729,52.68
+43,Juggernaut,agi,"Carry, Pusher, Escape",5585,2711,48.54,30394,14800,48.69,62313,30581,49.08,65590,32344,49.31,39235,19326,49.26,16334,8012,49.05,6419,3066,47.76,1576,731,46.38
+44,Keeper of the Light,int,"Support, Nuker, Disabler",896,353,39.4,5051,2216,43.87,10452,4579,43.81,11614,5322,45.82,7870,3627,46.09,4268,2001,46.88,2147,1043,48.58,1333,588,44.11
+45,Kunkka,str,"Carry, Support, Disabler, Initiator, Durable, Nuker",2251,1124,49.93,13474,6828,50.68,31210,16196,51.89,39691,21293,53.65,30314,16458,54.29,15706,8793,55.98,7884,4339,55.04,3458,1898,54.89
+46,Legion Commander,str,"Carry, Disabler, Initiator, Durable, Nuker",6263,3264,52.12,37100,19157,51.64,81491,41557,51.0,91431,46558,50.92,59383,29917,50.38,27945,13917,49.8,13193,6587,49.93,5601,2745,49.01
+47,Leshrac,int,"Carry, Support, Nuker, Pusher, Disabler",674,316,46.88,3872,1799,46.46,7490,3433,45.83,7903,3604,45.6,5322,2526,47.46,2687,1298,48.31,1325,647,48.83,721,357,49.51
+48,Lich,int,"Support, Nuker",2700,1412,52.3,16646,8820,52.99,37785,19685,52.1,45471,23554,51.8,31203,16108,51.62,15530,7821,50.36,7243,3597,49.66,2520,1258,49.92
+49,Lifestealer,str,"Carry, Durable, Escape, Disabler",2515,1213,48.23,14131,6978,49.38,29724,14627,49.21,31211,15581,49.92,18970,9481,49.98,8689,4400,50.64,3630,1821,50.17,1229,617,50.2
+50,Lina,int,"Support, Carry, Nuker, Disabler",4512,2030,44.99,21927,10156,46.32,45301,21210,46.82,54229,25956,47.86,40016,19138,47.83,21072,10112,47.99,10481,5031,48.0,4369,2138,48.94
+51,Lion,int,"Support, Disabler, Nuker, Initiator",6204,2855,46.02,37869,17465,46.12,80124,36649,45.74,84390,38176,45.24,50720,22914,45.18,21698,9784,45.09,9308,4280,45.98,3220,1496,46.46
+52,Lone Druid,all,"Carry, Pusher, Durable",909,483,53.14,4714,2421,51.36,10987,5858,53.32,14580,7968,54.65,11810,6490,54.95,7241,3971,54.84,4024,2240,55.67,2303,1259,54.67
+53,Luna,agi,"Carry, Nuker, Pusher",1927,904,46.91,9091,4271,46.98,16571,7922,47.81,16035,7615,47.49,9728,4634,47.64,4463,2103,47.12,1912,911,47.65,719,322,44.78
+54,Lycan,all,"Carry, Pusher, Durable, Escape",374,174,46.52,1894,915,48.31,3691,1744,47.25,3824,1905,49.82,2694,1332,49.44,1460,753,51.58,827,411,49.7,532,289,54.32
+55,Magnus,all,"Initiator, Disabler, Nuker, Escape",770,339,44.03,5789,2651,45.79,17837,7954,44.59,26126,12058,46.15,20634,9592,46.49,10574,5056,47.82,4565,2073,45.41,1606,751,46.76
+56,Marci,all,"Support, Carry, Initiator, Disabler, Escape",1370,620,45.26,7092,3252,45.85,15199,7240,47.63,18485,8874,48.01,13308,6305,47.38,7176,3476,48.44,3689,1882,51.02,1746,883,50.57
+57,Mars,str,"Carry, Initiator, Disabler, Durable",862,375,43.5,5719,2529,44.22,15156,6756,44.58,20719,9369,45.22,16419,7387,44.99,9044,4052,44.8,4536,2093,46.14,1926,868,45.07
+58,Medusa,agi,"Carry, Disabler, Durable",1898,902,47.52,9289,4512,48.57,16504,7818,47.37,14796,6886,46.54,7488,3449,46.06,2775,1270,45.77,1073,482,44.92,394,184,46.7
+59,Meepo,agi,"Carry, Escape, Nuker, Disabler, Initiator, Pusher",1004,523,52.09,3970,1990,50.13,6904,3587,51.96,7166,3646,50.88,4906,2563,52.24,2383,1282,53.8,1139,588,51.62,585,300,51.28
+60,Mirana,all,"Carry, Support, Escape, Nuker, Disabler",2499,1193,47.74,16954,8135,47.98,39985,19097,47.76,45169,21554,47.72,28467,13456,47.27,12800,6047,47.24,5272,2500,47.42,1824,874,47.92
+61,Monkey King,agi,"Carry, Escape, Disabler, Initiator",3191,1384,43.37,17306,7544,43.59,35734,16113,45.09,40778,18322,44.93,27558,12630,45.83,14034,6433,45.84,6650,3152,47.4,3040,1440,47.37
+62,Morphling,agi,"Carry, Escape, Durable, Nuker, Disabler",1521,690,45.36,8620,4006,46.47,18075,8161,45.15,20414,9235,45.24,14395,6530,45.36,7697,3551,46.13,4432,2050,46.25,2560,1190,46.48
+63,Muerta,int,"Carry, Nuker, Disabler",2130,1089,51.13,10787,5740,53.21,22602,11898,52.64,27609,14495,52.5,20175,10465,51.87,10662,5518,51.75,5462,2759,50.51,2948,1517,51.46
+64,Naga Siren,agi,"Carry, Support, Pusher, Disabler, Initiator, Escape",1502,804,53.53,6495,3356,51.67,10423,5234,50.22,9830,4929,50.14,6057,2971,49.05,3216,1675,52.08,1855,933,50.3,1242,634,51.05
+65,Nature's Prophet,int,"Carry, Pusher, Escape, Nuker",5991,3029,50.56,36433,18143,49.8,83118,42095,50.64,100341,51268,51.09,69436,35870,51.66,34256,17858,52.13,16585,8745,52.73,7182,3755,52.28
+66,Necrophos,int,"Carry, Nuker, Durable, Disabler",4776,2702,56.57,28535,15771,55.27,62186,34285,55.13,70212,38163,54.35,46539,24708,53.09,21607,11302,52.31,9677,4994,51.61,3418,1733,50.7
+67,Night Stalker,str,"Carry, Initiator, Durable, Disabler, Nuker",1189,594,49.96,7868,3892,49.47,19446,10004,51.45,25524,13506,52.91,20138,10828,53.77,10767,5651,52.48,5499,2889,52.54,2415,1257,52.05
+68,Nyx Assassin,all,"Disabler, Nuker, Initiator, Escape",1718,867,50.47,10925,5525,50.57,27207,14073,51.73,34684,18059,52.07,25736,13572,52.74,13313,7093,53.28,6485,3444,53.11,2852,1468,51.47
+69,Ogre Magi,str,"Support, Nuker, Disabler, Durable, Initiator",5331,2845,53.37,31507,16299,51.73,62954,32248,51.22,61758,31373,50.8,33746,16988,50.34,13262,6654,50.17,4861,2420,49.78,1271,654,51.46
+70,Omniknight,str,"Support, Durable, Nuker",975,479,49.13,6426,3109,48.38,14641,7319,49.99,17258,8731,50.59,11695,5916,50.59,5746,2993,52.09,2870,1469,51.18,1333,656,49.21
+71,Oracle,int,"Support, Nuker, Disabler, Escape",796,384,48.24,4857,2417,49.76,13141,6645,50.57,18944,9853,52.01,15221,7964,52.32,8356,4458,53.35,4475,2380,53.18,1905,1018,53.44
+72,Outworld Destroyer,int,"Carry, Nuker, Disabler",2226,1118,50.22,13388,6864,51.27,33284,17362,52.16,43991,23377,53.14,32021,16994,53.07,16655,8724,52.38,8123,4218,51.93,3176,1649,51.92
+73,Pangolier,all,"Carry, Nuker, Disabler, Durable, Escape, Initiator",1156,534,46.19,7189,3209,44.64,17802,7937,44.58,25785,11677,45.29,21727,10144,46.69,13064,6351,48.61,7567,3737,49.39,5275,2734,51.83
+74,Phantom Assassin,agi,"Carry, Escape",8553,4426,51.75,48549,25553,52.63,104756,54881,52.39,119332,62511,52.38,79140,41143,51.99,37399,19325,51.67,17774,9077,51.07,7819,3856,49.32
+75,Phantom Lancer,agi,"Carry, Escape, Pusher, Nuker",3641,1960,53.83,19550,10374,53.06,38576,20633,53.49,41505,22310,53.75,26401,14268,54.04,12437,6590,52.99,5708,2985,52.3,2383,1243,52.16
+76,Phoenix,all,"Support, Nuker, Initiator, Escape, Disabler",743,315,42.4,5231,2471,47.24,13950,6633,47.55,18350,8864,48.31,13972,6715,48.06,7787,3761,48.3,4322,2132,49.33,2610,1325,50.77
+77,Primal Beast,str,"Initiator, Durable, Disabler",1455,701,48.18,9333,4448,47.66,22800,11058,48.5,30084,14643,48.67,24307,11993,49.34,13970,6991,50.04,7742,3890,50.25,4625,2407,52.04
+78,Puck,int,"Initiator, Disabler, Escape, Nuker",871,399,45.81,5773,2628,45.52,16596,7578,45.66,24480,11315,46.22,20070,9497,47.32,11023,5298,48.06,5656,2714,47.98,2555,1200,46.97
+79,Pudge,str,"Disabler, Initiator, Durable, Nuker",7677,3796,49.45,50891,24776,48.68,114784,56289,49.04,129604,63097,48.68,85800,41542,48.42,41730,20239,48.5,19823,9530,48.08,7112,3431,48.24
+80,Pugna,int,"Nuker, Pusher",2075,944,45.49,9998,4695,46.96,18962,8958,47.24,20240,9965,49.23,12807,6199,48.4,5825,2855,49.01,2758,1387,50.29,1195,592,49.54
+81,Queen of Pain,int,"Carry, Nuker, Escape",2287,1100,48.1,15119,7354,48.64,37137,18118,48.79,47706,23657,49.59,35500,18018,50.75,18405,9289,50.47,9243,4689,50.73,4227,2113,49.99
+82,Razor,agi,"Carry, Durable, Nuker, Pusher",2470,1231,49.84,12000,5964,49.7,24666,12142,49.23,30334,14844,48.94,21832,10558,48.36,11917,5679,47.65,6092,2912,47.8,3144,1551,49.33
+83,Riki,agi,"Carry, Escape, Disabler",3684,1929,52.36,19022,9891,52.0,35638,18582,52.14,33908,17415,51.36,20194,10312,51.06,8726,4377,50.16,3735,1855,49.67,1160,559,48.19
+84,Rubick,int,"Support, Disabler, Nuker",3090,1404,45.44,21639,9303,42.99,57417,24590,42.83,74874,32603,43.54,55186,24219,43.89,28206,12568,44.56,13732,6106,44.47,5764,2642,45.84
+85,Sand King,all,"Initiator, Disabler, Support, Nuker, Escape",2633,1513,57.46,13097,7323,55.91,25271,13807,54.64,26724,14323,53.6,17384,9144,52.6,7907,4104,51.9,3394,1719,50.65,1211,611,50.45
+86,Shadow Demon,int,"Support, Disabler, Initiator, Nuker",547,236,43.14,3252,1426,43.85,7920,3524,44.49,9752,4551,46.67,7404,3467,46.83,3956,1876,47.42,2076,1004,48.36,1054,497,47.15
+87,Shadow Fiend,agi,"Carry, Nuker",5051,2544,50.37,27255,14064,51.6,58589,29830,50.91,65429,33097,50.58,41810,21189,50.68,18766,9401,50.1,8232,4000,48.59,3016,1430,47.41
+88,Shadow Shaman,int,"Support, Pusher, Disabler, Nuker, Initiator",5323,2795,52.51,29733,15606,52.49,58894,31236,53.04,58765,30895,52.57,34475,18242,52.91,15166,7986,52.66,6377,3323,52.11,2413,1253,51.93
+89,Silencer,int,"Carry, Support, Disabler, Initiator, Nuker",4229,2324,54.95,27878,14960,53.66,61698,33081,53.62,65256,34458,52.8,38589,19853,51.45,16889,8653,51.23,6836,3416,49.97,2236,1105,49.42
+90,Skywrath Mage,int,"Support, Nuker, Disabler",4000,2030,50.75,22783,11675,51.24,46512,23624,50.79,51329,25706,50.08,34167,17364,50.82,16693,8415,50.41,8496,4208,49.53,4389,2069,47.14
+91,Slardar,str,"Carry, Durable, Initiator, Disabler, Escape",3935,2129,54.1,21523,11602,53.91,43947,23701,53.93,47721,25633,53.71,29887,16132,53.98,14233,7722,54.25,6530,3467,53.09,2322,1205,51.89
+92,Slark,agi,"Carry, Escape, Disabler, Nuker",4815,2521,52.36,29413,14762,50.19,64004,31771,49.64,70173,34411,49.04,44780,21926,48.96,20864,10270,49.22,9969,4962,49.77,4565,2394,52.44
+93,Snapfire,all,"Support, Nuker, Disabler, Escape",1524,682,44.75,10646,4576,42.98,27103,12120,44.72,34711,15412,44.4,24351,10786,44.29,11723,5131,43.77,5227,2294,43.89,1987,868,43.68
+94,Sniper,agi,"Carry, Nuker",8022,4079,50.85,44508,22727,51.06,88690,45223,50.99,87190,44086,50.56,47411,23648,49.88,18092,8924,49.33,6130,3040,49.59,1370,662,48.32
+95,Spectre,agi,"Carry, Durable, Escape",3454,2008,58.14,22097,12356,55.92,49157,26961,54.85,55914,30100,53.83,36321,19338,53.24,16946,8960,52.87,7921,4163,52.56,2568,1370,53.35
+96,Spirit Breaker,str,"Carry, Initiator, Disabler, Durable, Escape",4788,2423,50.61,26662,13530,50.75,56535,28908,51.13,63991,32249,50.4,42512,21357,50.24,20119,9926,49.34,9499,4814,50.68,3761,1884,50.09
+97,Storm Spirit,int,"Carry, Escape, Nuker, Initiator, Disabler",2202,1001,45.46,11656,5197,44.59,25644,11806,46.04,30968,14210,45.89,21680,10197,47.03,10810,5025,46.48,5278,2382,45.13,2363,1122,47.48
+98,Sven,str,"Carry, Disabler, Initiator, Durable, Nuker",3552,1761,49.58,19792,9744,49.23,41296,20478,49.59,48709,24228,49.74,35460,17828,50.28,19795,10065,50.85,11014,5655,51.34,6701,3387,50.54
+99,Techies,all,"Nuker, Disabler",2356,1131,48.01,13105,6245,47.65,27293,12893,47.24,29180,13507,46.29,18216,8407,46.15,8266,3771,45.62,3459,1644,47.53,1319,591,44.81
+100,Templar Assassin,agi,"Carry, Escape",2142,955,44.58,10932,4758,43.52,21211,9445,44.53,23928,10909,45.59,17399,8242,47.37,9567,4656,48.67,5525,2708,49.01,3524,1775,50.37
+101,Terrorblade,agi,"Carry, Pusher, Nuker",1115,484,43.41,5686,2430,42.74,10856,4638,42.72,11518,5041,43.77,8059,3540,43.93,4192,1827,43.58,2419,1082,44.73,1621,700,43.18
+102,Tidehunter,str,"Initiator, Durable, Disabler, Nuker, Carry",1835,855,46.59,11159,5369,48.11,26222,12699,48.43,30735,14879,48.41,20523,9727,47.4,9731,4740,48.71,4426,2079,46.97,1998,936,46.85
+103,Timbersaw,all,"Nuker, Durable, Escape",1050,448,42.67,5854,2584,44.14,12301,5391,43.83,14295,6097,42.65,9697,4217,43.49,4992,2163,43.33,2419,1021,42.21,1139,471,41.35
+104,Tinker,int,"Carry, Nuker, Pusher",2106,944,44.82,11058,5200,47.02,24263,11826,48.74,27531,13614,49.45,19017,9732,51.18,9416,4875,51.77,4700,2466,52.47,1951,1036,53.1
+105,Tiny,str,"Carry, Nuker, Pusher, Initiator, Durable, Disabler",1434,654,45.61,7742,3452,44.59,15936,6950,43.61,17139,7468,43.57,11269,4991,44.29,5485,2491,45.41,2599,1216,46.79,1058,519,49.05
+106,Treant Protector,str,"Support, Initiator, Durable, Disabler, Escape",1646,899,54.62,11430,5881,51.45,28752,15124,52.6,36093,19344,53.59,28762,15532,54.0,16751,9227,55.08,9870,5468,55.4,6801,3855,56.68
+107,Troll Warlord,agi,"Carry, Pusher, Disabler, Durable",3176,1720,54.16,14007,7445,53.15,24729,13022,52.66,25424,13228,52.03,17362,9030,52.01,9427,4913,52.12,4767,2499,52.42,2341,1242,53.05
+108,Tusk,str,"Initiator, Disabler, Nuker",1263,565,44.73,8338,3777,45.3,19642,8869,45.15,25308,11520,45.52,18927,8853,46.77,10100,4820,47.72,5220,2502,47.93,2350,1157,49.23
+109,Underlord,str,"Support, Nuker, Disabler, Durable, Escape",797,405,50.82,4583,2341,51.08,10067,5057,50.23,11650,5786,49.67,7224,3561,49.29,3310,1591,48.07,1368,673,49.2,395,190,48.1
+110,Undying,str,"Support, Durable, Disabler, Nuker",3170,1620,51.1,19403,10116,52.14,40582,21110,52.02,40850,21182,51.85,23985,12454,51.92,10395,5389,51.84,4541,2336,51.44,2064,1012,49.03
+111,Ursa,agi,"Carry, Durable, Disabler",2801,1273,45.45,15132,7038,46.51,33269,15478,46.52,40822,19264,47.19,29348,14011,47.74,15262,7375,48.32,7507,3622,48.25,3004,1473,49.03
+112,Vengeful Spirit,all,"Support, Initiator, Disabler, Nuker, Escape",2186,1108,50.69,15817,8285,52.38,41843,21809,52.12,57524,30476,52.98,45512,24120,53.0,25581,13382,52.31,13758,7121,51.76,8276,4303,51.99
+113,Venomancer,all,"Support, Nuker, Initiator, Pusher, Disabler",2309,1187,51.41,14669,7463,50.88,34787,18020,51.8,41797,21690,51.89,28706,15085,52.55,13974,7338,52.51,6538,3495,53.46,2794,1459,52.22
+114,Viper,agi,"Carry, Durable, Initiator, Disabler",4100,2057,50.17,18991,9510,50.08,33517,16923,50.49,32728,16677,50.96,18537,9427,50.86,7851,3928,50.03,3260,1652,50.67,1176,610,51.87
+115,Visage,all,"Support, Nuker, Durable, Disabler, Pusher",331,171,51.66,1638,813,49.63,3240,1577,48.67,3840,1986,51.72,3108,1609,51.77,1995,1055,52.88,1309,702,53.63,858,457,53.26
+116,Void Spirit,all,"Carry, Escape, Nuker, Disabler",1565,727,46.45,8672,4096,47.23,20010,9694,48.45,25213,12376,49.09,18817,9231,49.06,10026,4920,49.07,4788,2319,48.43,2006,964,48.06
+117,Warlock,int,"Support, Initiator, Disabler",2547,1369,53.75,18931,10331,54.57,49795,26999,54.22,66697,36220,54.31,48401,25668,53.03,24999,12942,51.77,12575,6356,50.54,6183,2934,47.45
+118,Weaver,agi,"Carry, Escape",2818,1389,49.29,13873,6770,48.8,23493,11571,49.25,21545,10694,49.64,12911,6427,49.78,5809,2928,50.4,2960,1455,49.16,1303,719,55.18
+119,Windranger,all,"Carry, Support, Disabler, Escape, Nuker",3861,1814,46.98,19934,9223,46.27,40644,18807,46.27,44476,20652,46.43,28952,13508,46.66,13418,6297,46.93,5898,2782,47.17,2374,1142,48.1
+120,Winter Wyvern,all,"Support, Disabler, Nuker",821,371,45.19,5168,2424,46.9,10544,5014,47.55,11184,5308,47.46,7426,3512,47.29,3730,1854,49.71,1862,934,50.16,944,464,49.15
+121,Witch Doctor,int,"Support, Nuker, Disabler",7504,4173,55.61,45501,25616,56.3,99664,54963,55.15,111382,60421,54.25,71830,37860,52.71,33164,17334,52.27,14610,7442,50.94,4196,2076,49.48
+122,Wraith King,str,"Carry, Support, Durable, Disabler, Initiator",4175,2266,54.28,26362,14516,55.06,58733,32403,55.17,66283,36503,55.07,42360,23083,54.49,19084,10251,53.72,8334,4315,51.78,2707,1376,50.83
+123,Zeus,int,"Nuker, Carry",4132,2106,50.97,23721,12487,52.64,51568,27475,53.28,58333,31078,53.28,37821,20047,53.0,17901,9504,53.09,8539,4459,52.22,3400,1791,52.68

belyaeva_ekaterina_lab_5/README.md

@@ -0,0 +1,42 @@
+## Задание 
+
+Использовать  регрессию по  варианту для  данных  из  таблицы  1  по варианту(таблица  10),самостоятельно  сформулировав  задачу.  Оценить, насколько хорошо она подходит для решения сформулированной вами задачи  
+Вариант 6 - полиномиальная регрессия
+
+## Как запустить лабораторную
+Запустить файл main.py
+## Используемые технологии
+Библиотеки pandas, matplotlib, scikit-learn, их компоненты
+## Описание лабораторной (программы)
+Данный код берет данные из датасета о персонажах Dota 2, где описаны атрибуты персонажей, их роли, название, и как часто их пикают и какой у них винрейт на каждом звании в Доте, от реркута до титана.
+
+В моем случае была поставлена задача предсказать винрейт персонажа по тому, как часто его берут и по его винрейту на 
+смежных рангах (просто предсказать винрейт по тому, как часто его берут, нельзя, потому что винрейт зависит от текущей меты)
+
+Программа берет столбцы Name, Archon Picks, Archon Win Rate, Legend Picks, Legend Win Rate, Ancient Picks, Ancient Win Rate.
+Все столбцы, кроме Name и Legend Win Rate, нужны для того чтобы обучить модель. Legend Win Rate - 
+данные, которые нужно предсказать. Name - столбец для вывода результатов.
+
+Дальше все по дефолту - программа делит данные на обучающую и тестовые выборки, просиходит
+применение данных для обучения, затем обучаем модель. После этого происходит то же самое с тестовыми данными и затем выводится 
+оценка качества модели.
+
+В конце программа строит график, где показывает точки обучающей и тестовой выборки, но к тестовой выборки я решила добавить названия
+персонажей, чтобы график был более наглядным, но в то же время не перегруженным.
+
+## Результат
+
+В результате получаем график, который показывает результаты обучающей и тестовой выборок.
+![diagram.png](diagram.png)
+
+Помимо этого, программа вводит оценку качества модели:
+![R2Score.png](R2Score.png)
+
+Из чего можно сделать вывод, что модель работает очень хорошо и успешно решает поставленную задачу.
+
+Это объясняется тем, что модели было предоставлено достаточно большое количество признаков, по которым можно предсказать 
+интересующие нас данные. Кроме того, винрейт персонажей взят со смежных рангов. 
+
+Если взять винрейт персонажей на рангах, которые 
+находятся далеко от целевого, модель будет работать хуже, потому что чем больше разница в рангах, тем более разный винрейт у персонажей.
+Также, если бы было взято меньше признаков, оценка качества модели так же была бы ниже.

belyaeva_ekaterina_lab_5/main.py

@@ -0,0 +1,47 @@
+import pandas as pd
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
+
+# Загрузка данных
+data = pd.read_csv('Current_Pub_Meta.csv')
+
+X = data[['Archon Picks', 'Archon Win Rate', 'Legend Picks', 'Ancient Picks', 'Ancient Win Rate']]
+y = data['Legend Win Rate']
+names = data['Name']
+
+# Разбиваем данные на обучающую и тестовую выборки
+X_train, X_test, y_train, y_test, names_train, names_test = train_test_split(X, y, names, test_size=0.1, random_state=42)
+
+# Применяем полиномиальные признаки к обучающим данным
+poly_features = PolynomialFeatures(degree=2)
+X_train_poly = poly_features.fit_transform(X_train)
+
+# Создаем и обучаем модель полиномиальной регрессии
+poly_model = LinearRegression()
+poly_model.fit(X_train_poly, y_train)
+
+# Применяем полиномиальные признаки к тестовым данным и делаем предсказания
+X_test_poly = poly_features.transform(X_test)
+y_pred = poly_model.predict(X_test_poly)
+
+# Оценка качества модели на тестовых данных
+r2 = poly_model.score(X_test_poly, y_test)
+print(f"R-квадрат: {r2}")
+
+# Построение графика с именами персонажей
+plt.figure(figsize=(10, 6))
+plt.title('Корреляция между выбором персонажей и победами в ранге "Legend"')
+plt.grid(True)
+plt.scatter(X_train['Legend Picks'], y_train, color='blue', alpha=0.5, label='Обучающая выборка')
+plt.scatter(X_test['Legend Picks'], y_test, color='red', alpha=0.5, label='Тестовая выборка')
+
+# Добавляем имена персонажей на график
+for i, name in enumerate(names_test):
+    plt.annotate(name, (X_test['Legend Picks'].iloc[i], y_pred[i]), fontsize=8, alpha=0.7, color='black')
+
+plt.xlabel('Legend Picks')
+plt.ylabel('Legend Win Rate')
+plt.legend()
+plt.show()

belyaeva_ekaterina_lab_6/Current_Pub_Meta.csv

@@ -0,0 +1,125 @@
+,Name,Primary Attribute,Roles,Herald Picks,Herald Wins,Herald Win Rate,Guardian Picks,Guardian Wins,Guardian Win Rate,Crusader Picks,Crusader Wins,Crusader Win Rate,Archon Picks,Archon Wins,Archon Win Rate,Legend Picks,Legend Wins,Legend Win Rate,Ancient Picks,Ancient Wins,Ancient Win Rate,Divine Picks,Divine Wins,Divine Win Rate,Immortal Picks,Immortal Wins,Immortal Win Rate
+0,Abaddon,all,"Support, Carry, Durable",1111,575,51.76,6408,3309,51.64,13811,7050,51.05,16497,8530,51.71,11360,5877,51.73,5571,2893,51.93,2632,1345,51.1,991,497,50.15
+1,Alchemist,str,"Carry, Support, Durable, Disabler, Initiator, Nuker",1119,486,43.43,6370,2883,45.26,12238,5617,45.9,13028,6130,47.05,8455,4055,47.96,4120,1984,48.16,2021,1023,50.62,860,424,49.3
+2,Ancient Apparition,int,"Support, Disabler, Nuker",2146,1073,50.0,13697,7069,51.61,30673,16118,52.55,35145,18219,51.84,23114,12166,52.63,10688,5528,51.72,5035,2573,51.1,2134,1076,50.42
+3,Anti-Mage,agi,"Carry, Escape, Nuker",3765,1818,48.29,22050,10774,48.86,47371,23304,49.19,49115,24074,49.02,28599,13991,48.92,12303,5958,48.43,4866,2349,48.27,1502,751,50.0
+4,Arc Warden,agi,"Carry, Escape, Nuker",1448,704,48.62,8047,4162,51.72,14946,7982,53.41,14711,7875,53.53,9472,5167,54.55,4323,2309,53.41,2104,1148,54.56,789,435,55.13
+5,Axe,str,"Initiator, Durable, Disabler, Carry",5343,2880,53.9,32652,17719,54.27,71010,37736,53.14,77869,40559,52.09,49182,25079,50.99,22637,11353,50.15,10114,5000,49.44,3795,1837,48.41
+6,Bane,all,"Support, Disabler, Nuker, Durable",745,334,44.83,4983,2422,48.61,11332,5504,48.57,13633,6767,49.64,10132,5032,49.66,5596,2861,51.13,3028,1555,51.35,1958,1055,53.88
+7,Batrider,all,"Initiator, Disabler, Escape",349,136,38.97,1983,812,40.95,4053,1595,39.35,4725,1861,39.39,3173,1275,40.18,1678,731,43.56,802,362,45.14,497,227,45.67
+8,Beastmaster,all,"Initiator, Disabler, Durable, Nuker",402,174,43.28,2447,1060,43.32,5787,2569,44.39,6930,3092,44.62,5288,2389,45.18,2816,1274,45.24,1593,752,47.21,1176,539,45.83
+9,Bloodseeker,agi,"Carry, Disabler, Nuker, Initiator",2765,1382,49.98,12589,6270,49.81,21781,10683,49.05,20961,10420,49.71,13035,6430,49.33,6210,3006,48.41,2941,1475,50.15,1465,718,49.01
+10,Bounty Hunter,agi,"Escape, Nuker",3852,1868,48.49,19609,9535,48.63,36362,17600,48.4,37059,18314,49.42,22934,11518,50.22,10584,5276,49.85,5105,2594,50.81,2498,1325,53.04
+11,Brewmaster,all,"Carry, Initiator, Durable, Disabler, Nuker",545,280,51.38,3564,1745,48.96,8941,4388,49.08,12340,6111,49.52,11185,5623,50.27,7645,3906,51.09,4812,2478,51.5,3533,1820,51.51
+12,Bristleback,str,"Carry, Durable, Initiator, Nuker",5884,3262,55.44,27952,14587,52.19,48847,24379,49.91,46702,22927,49.09,27466,13319,48.49,12398,5969,48.14,5865,2915,49.7,2639,1304,49.41
+13,Broodmother,all,"Carry, Pusher, Escape, Nuker",456,173,37.94,2048,842,41.11,3444,1462,42.45,3392,1448,42.69,2193,1048,47.79,1203,602,50.04,795,422,53.08,453,230,50.77
+14,Centaur Warrunner,str,"Durable, Initiator, Disabler, Nuker, Escape",1721,911,52.93,11754,6266,53.31,28691,15201,52.98,35369,18741,52.99,25393,13468,53.04,12653,6607,52.22,6124,3181,51.94,2442,1243,50.9
+15,Chaos Knight,str,"Carry, Disabler, Durable, Pusher, Initiator",3032,1639,54.06,16762,8931,53.28,31892,17139,53.74,30697,16435,53.54,18217,9810,53.85,8572,4620,53.9,4230,2291,54.16,1750,943,53.89
+16,Chen,all,"Support, Pusher",284,125,44.01,1450,678,46.76,2969,1345,45.3,3258,1604,49.23,2641,1331,50.4,1488,767,51.55,970,512,52.78,770,448,58.18
+17,Clinkz,agi,"Carry, Escape, Pusher",3151,1608,51.03,13891,7141,51.41,25465,12938,50.81,27327,14066,51.47,18846,9726,51.61,9452,4890,51.74,4765,2475,51.94,2093,1052,50.26
+18,Clockwerk,all,"Initiator, Disabler, Durable, Nuker",816,397,48.65,5860,2837,48.41,14478,6929,47.86,18466,8843,47.89,13143,6301,47.94,6612,3169,47.93,3286,1581,48.11,1378,658,47.75
+19,Crystal Maiden,int,"Support, Disabler, Nuker",4821,2529,52.46,26584,13626,51.26,52168,26040,49.92,52258,25365,48.54,30690,14848,48.38,13295,6404,48.17,5602,2680,47.84,1638,771,47.07
+20,Dark Seer,all,"Initiator, Escape, Disabler",627,320,51.04,3675,1884,51.27,7881,3803,48.26,9589,4844,50.52,7186,3573,49.72,3902,1983,50.82,2145,1095,51.05,1217,593,48.73
+21,Dark Willow,all,"Support, Nuker, Disabler, Escape",2654,1293,48.72,13829,6657,48.14,28142,13480,47.9,32114,15785,49.15,23100,11331,49.05,12052,5909,49.03,6400,3182,49.72,3708,1915,51.65
+22,Dawnbreaker,str,"Carry, Durable",1746,875,50.11,12297,6105,49.65,32398,15921,49.14,44846,21936,48.91,35474,17441,49.17,19770,9832,49.73,10637,5263,49.48,6339,3173,50.06
+23,Dazzle,all,"Support, Nuker, Disabler",2827,1418,50.16,19852,9758,49.15,48236,23691,49.11,56417,27798,49.27,38159,18642,48.85,18695,9199,49.21,8530,4239,49.7,3382,1654,48.91
+24,Death Prophet,int,"Carry, Pusher, Nuker, Disabler",1372,659,48.03,6643,3145,47.34,11987,5729,47.79,12268,5856,47.73,7455,3606,48.37,3591,1698,47.28,1872,902,48.18,926,459,49.57
+25,Disruptor,int,"Support, Disabler, Nuker, Initiator",1541,757,49.12,11104,5331,48.01,27746,13542,48.81,33742,16310,48.34,23173,11096,47.88,10907,5201,47.68,4859,2255,46.41,1863,861,46.22
+26,Doom,str,"Carry, Disabler, Initiator, Durable, Nuker",1049,474,45.19,6112,2767,45.27,13700,6056,44.2,15454,6925,44.81,10727,4842,45.14,5444,2451,45.02,2979,1348,45.25,1545,731,47.31
+27,Dragon Knight,str,"Carry, Pusher, Durable, Disabler, Initiator, Nuker",1950,942,48.31,10643,5274,49.55,20451,9733,47.59,20326,9671,47.58,11674,5544,47.49,4979,2355,47.3,2024,973,48.07,725,341,47.03
+28,Drow Ranger,agi,"Carry, Disabler, Pusher",5737,2904,50.62,29675,14831,49.98,57655,28573,49.56,56682,27927,49.27,34310,16607,48.4,15050,7171,47.65,5947,2815,47.33,1768,788,44.57
+29,Earth Spirit,str,"Nuker, Escape, Disabler, Initiator, Durable",1038,465,44.8,7420,3276,44.15,20807,9432,45.33,30107,14166,47.05,25314,12148,47.99,14579,7041,48.3,7678,3802,49.52,4379,2169,49.53
+30,Earthshaker,str,"Support, Initiator, Disabler, Nuker",5012,2455,48.98,29784,14662,49.23,67050,33111,49.38,79963,39843,49.83,57108,28961,50.71,28650,14591,50.93,14186,7296,51.43,6151,3165,51.46
+31,Elder Titan,str,"Initiator, Disabler, Nuker, Durable",471,212,45.01,2551,1248,48.92,5213,2570,49.3,5572,2809,50.41,3847,1942,50.48,1964,998,50.81,1124,613,54.54,550,292,53.09
+32,Ember Spirit,agi,"Carry, Escape, Nuker, Disabler, Initiator",1514,635,41.94,9180,3836,41.79,20578,8738,42.46,25152,10844,43.11,17703,7814,44.14,8538,3793,44.42,4265,1892,44.36,2065,928,44.94
+33,Enchantress,int,"Support, Pusher, Durable, Disabler",1794,848,47.27,8050,3622,44.99,12921,5686,44.01,11673,4974,42.61,6863,2840,41.38,2948,1212,41.11,1434,654,45.61,806,318,39.45
+34,Enigma,all,"Disabler, Initiator, Pusher",1317,588,44.65,6937,3171,45.71,12908,5979,46.32,11687,5428,46.44,6194,2839,45.83,2493,1127,45.21,938,437,46.59,338,159,47.04
+35,Faceless Void,agi,"Carry, Initiator, Disabler, Escape, Durable",4323,2043,47.26,25618,11902,46.46,54581,25874,47.4,60671,28993,47.79,40137,19611,48.86,19376,9620,49.65,9579,4828,50.4,4439,2256,50.82
+36,Grimstroke,int,"Support, Nuker, Disabler, Escape",1455,694,47.7,9714,4789,49.3,24688,12430,50.35,32027,16094,50.25,23193,11795,50.86,12102,6100,50.4,6191,3047,49.22,3449,1666,48.3
+37,Gyrocopter,agi,"Carry, Nuker, Disabler",2560,1213,47.38,16589,7882,47.51,42072,20358,48.39,54200,26229,48.39,39414,19053,48.34,20164,9781,48.51,10164,4937,48.57,5241,2507,47.83
+38,Hoodwink,agi,"Support, Nuker, Escape, Disabler",2420,1126,46.53,14034,6800,48.45,31382,14964,47.68,35684,16966,47.55,22626,10651,47.07,9949,4690,47.14,4349,2089,48.03,1533,703,45.86
+39,Huskar,str,"Carry, Durable, Initiator",3501,1603,45.79,14234,6639,46.64,22794,10912,47.87,21801,10763,49.37,13811,6919,50.1,6769,3535,52.22,3556,1822,51.24,1936,993,51.29
+40,Invoker,all,"Carry, Nuker, Disabler, Escape, Pusher",4330,2042,47.16,27625,13176,47.7,69035,33863,49.05,86745,43479,50.12,61821,31510,50.97,31459,16321,51.88,15431,8195,53.11,7852,4148,52.83
+41,Io,all,"Support, Escape, Nuker",1274,615,48.27,6158,2999,48.7,12762,6247,48.95,14216,7024,49.41,9564,4843,50.64,5301,2685,50.65,2789,1463,52.46,1464,773,52.8
+42,Jakiro,int,"Support, Nuker, Pusher, Disabler",3147,1708,54.27,22718,12413,54.64,56736,30984,54.61,70038,37473,53.5,46389,24997,53.89,22084,11639,52.7,9838,5103,51.87,3282,1729,52.68
+43,Juggernaut,agi,"Carry, Pusher, Escape",5585,2711,48.54,30394,14800,48.69,62313,30581,49.08,65590,32344,49.31,39235,19326,49.26,16334,8012,49.05,6419,3066,47.76,1576,731,46.38
+44,Keeper of the Light,int,"Support, Nuker, Disabler",896,353,39.4,5051,2216,43.87,10452,4579,43.81,11614,5322,45.82,7870,3627,46.09,4268,2001,46.88,2147,1043,48.58,1333,588,44.11
+45,Kunkka,str,"Carry, Support, Disabler, Initiator, Durable, Nuker",2251,1124,49.93,13474,6828,50.68,31210,16196,51.89,39691,21293,53.65,30314,16458,54.29,15706,8793,55.98,7884,4339,55.04,3458,1898,54.89
+46,Legion Commander,str,"Carry, Disabler, Initiator, Durable, Nuker",6263,3264,52.12,37100,19157,51.64,81491,41557,51.0,91431,46558,50.92,59383,29917,50.38,27945,13917,49.8,13193,6587,49.93,5601,2745,49.01
+47,Leshrac,int,"Carry, Support, Nuker, Pusher, Disabler",674,316,46.88,3872,1799,46.46,7490,3433,45.83,7903,3604,45.6,5322,2526,47.46,2687,1298,48.31,1325,647,48.83,721,357,49.51
+48,Lich,int,"Support, Nuker",2700,1412,52.3,16646,8820,52.99,37785,19685,52.1,45471,23554,51.8,31203,16108,51.62,15530,7821,50.36,7243,3597,49.66,2520,1258,49.92
+49,Lifestealer,str,"Carry, Durable, Escape, Disabler",2515,1213,48.23,14131,6978,49.38,29724,14627,49.21,31211,15581,49.92,18970,9481,49.98,8689,4400,50.64,3630,1821,50.17,1229,617,50.2
+50,Lina,int,"Support, Carry, Nuker, Disabler",4512,2030,44.99,21927,10156,46.32,45301,21210,46.82,54229,25956,47.86,40016,19138,47.83,21072,10112,47.99,10481,5031,48.0,4369,2138,48.94
+51,Lion,int,"Support, Disabler, Nuker, Initiator",6204,2855,46.02,37869,17465,46.12,80124,36649,45.74,84390,38176,45.24,50720,22914,45.18,21698,9784,45.09,9308,4280,45.98,3220,1496,46.46
+52,Lone Druid,all,"Carry, Pusher, Durable",909,483,53.14,4714,2421,51.36,10987,5858,53.32,14580,7968,54.65,11810,6490,54.95,7241,3971,54.84,4024,2240,55.67,2303,1259,54.67
+53,Luna,agi,"Carry, Nuker, Pusher",1927,904,46.91,9091,4271,46.98,16571,7922,47.81,16035,7615,47.49,9728,4634,47.64,4463,2103,47.12,1912,911,47.65,719,322,44.78
+54,Lycan,all,"Carry, Pusher, Durable, Escape",374,174,46.52,1894,915,48.31,3691,1744,47.25,3824,1905,49.82,2694,1332,49.44,1460,753,51.58,827,411,49.7,532,289,54.32
+55,Magnus,all,"Initiator, Disabler, Nuker, Escape",770,339,44.03,5789,2651,45.79,17837,7954,44.59,26126,12058,46.15,20634,9592,46.49,10574,5056,47.82,4565,2073,45.41,1606,751,46.76
+56,Marci,all,"Support, Carry, Initiator, Disabler, Escape",1370,620,45.26,7092,3252,45.85,15199,7240,47.63,18485,8874,48.01,13308,6305,47.38,7176,3476,48.44,3689,1882,51.02,1746,883,50.57
+57,Mars,str,"Carry, Initiator, Disabler, Durable",862,375,43.5,5719,2529,44.22,15156,6756,44.58,20719,9369,45.22,16419,7387,44.99,9044,4052,44.8,4536,2093,46.14,1926,868,45.07
+58,Medusa,agi,"Carry, Disabler, Durable",1898,902,47.52,9289,4512,48.57,16504,7818,47.37,14796,6886,46.54,7488,3449,46.06,2775,1270,45.77,1073,482,44.92,394,184,46.7
+59,Meepo,agi,"Carry, Escape, Nuker, Disabler, Initiator, Pusher",1004,523,52.09,3970,1990,50.13,6904,3587,51.96,7166,3646,50.88,4906,2563,52.24,2383,1282,53.8,1139,588,51.62,585,300,51.28
+60,Mirana,all,"Carry, Support, Escape, Nuker, Disabler",2499,1193,47.74,16954,8135,47.98,39985,19097,47.76,45169,21554,47.72,28467,13456,47.27,12800,6047,47.24,5272,2500,47.42,1824,874,47.92
+61,Monkey King,agi,"Carry, Escape, Disabler, Initiator",3191,1384,43.37,17306,7544,43.59,35734,16113,45.09,40778,18322,44.93,27558,12630,45.83,14034,6433,45.84,6650,3152,47.4,3040,1440,47.37
+62,Morphling,agi,"Carry, Escape, Durable, Nuker, Disabler",1521,690,45.36,8620,4006,46.47,18075,8161,45.15,20414,9235,45.24,14395,6530,45.36,7697,3551,46.13,4432,2050,46.25,2560,1190,46.48
+63,Muerta,int,"Carry, Nuker, Disabler",2130,1089,51.13,10787,5740,53.21,22602,11898,52.64,27609,14495,52.5,20175,10465,51.87,10662,5518,51.75,5462,2759,50.51,2948,1517,51.46
+64,Naga Siren,agi,"Carry, Support, Pusher, Disabler, Initiator, Escape",1502,804,53.53,6495,3356,51.67,10423,5234,50.22,9830,4929,50.14,6057,2971,49.05,3216,1675,52.08,1855,933,50.3,1242,634,51.05
+65,Nature's Prophet,int,"Carry, Pusher, Escape, Nuker",5991,3029,50.56,36433,18143,49.8,83118,42095,50.64,100341,51268,51.09,69436,35870,51.66,34256,17858,52.13,16585,8745,52.73,7182,3755,52.28
+66,Necrophos,int,"Carry, Nuker, Durable, Disabler",4776,2702,56.57,28535,15771,55.27,62186,34285,55.13,70212,38163,54.35,46539,24708,53.09,21607,11302,52.31,9677,4994,51.61,3418,1733,50.7
+67,Night Stalker,str,"Carry, Initiator, Durable, Disabler, Nuker",1189,594,49.96,7868,3892,49.47,19446,10004,51.45,25524,13506,52.91,20138,10828,53.77,10767,5651,52.48,5499,2889,52.54,2415,1257,52.05
+68,Nyx Assassin,all,"Disabler, Nuker, Initiator, Escape",1718,867,50.47,10925,5525,50.57,27207,14073,51.73,34684,18059,52.07,25736,13572,52.74,13313,7093,53.28,6485,3444,53.11,2852,1468,51.47
+69,Ogre Magi,str,"Support, Nuker, Disabler, Durable, Initiator",5331,2845,53.37,31507,16299,51.73,62954,32248,51.22,61758,31373,50.8,33746,16988,50.34,13262,6654,50.17,4861,2420,49.78,1271,654,51.46
+70,Omniknight,str,"Support, Durable, Nuker",975,479,49.13,6426,3109,48.38,14641,7319,49.99,17258,8731,50.59,11695,5916,50.59,5746,2993,52.09,2870,1469,51.18,1333,656,49.21
+71,Oracle,int,"Support, Nuker, Disabler, Escape",796,384,48.24,4857,2417,49.76,13141,6645,50.57,18944,9853,52.01,15221,7964,52.32,8356,4458,53.35,4475,2380,53.18,1905,1018,53.44
+72,Outworld Destroyer,int,"Carry, Nuker, Disabler",2226,1118,50.22,13388,6864,51.27,33284,17362,52.16,43991,23377,53.14,32021,16994,53.07,16655,8724,52.38,8123,4218,51.93,3176,1649,51.92
+73,Pangolier,all,"Carry, Nuker, Disabler, Durable, Escape, Initiator",1156,534,46.19,7189,3209,44.64,17802,7937,44.58,25785,11677,45.29,21727,10144,46.69,13064,6351,48.61,7567,3737,49.39,5275,2734,51.83
+74,Phantom Assassin,agi,"Carry, Escape",8553,4426,51.75,48549,25553,52.63,104756,54881,52.39,119332,62511,52.38,79140,41143,51.99,37399,19325,51.67,17774,9077,51.07,7819,3856,49.32
+75,Phantom Lancer,agi,"Carry, Escape, Pusher, Nuker",3641,1960,53.83,19550,10374,53.06,38576,20633,53.49,41505,22310,53.75,26401,14268,54.04,12437,6590,52.99,5708,2985,52.3,2383,1243,52.16
+76,Phoenix,all,"Support, Nuker, Initiator, Escape, Disabler",743,315,42.4,5231,2471,47.24,13950,6633,47.55,18350,8864,48.31,13972,6715,48.06,7787,3761,48.3,4322,2132,49.33,2610,1325,50.77
+77,Primal Beast,str,"Initiator, Durable, Disabler",1455,701,48.18,9333,4448,47.66,22800,11058,48.5,30084,14643,48.67,24307,11993,49.34,13970,6991,50.04,7742,3890,50.25,4625,2407,52.04
+78,Puck,int,"Initiator, Disabler, Escape, Nuker",871,399,45.81,5773,2628,45.52,16596,7578,45.66,24480,11315,46.22,20070,9497,47.32,11023,5298,48.06,5656,2714,47.98,2555,1200,46.97
+79,Pudge,str,"Disabler, Initiator, Durable, Nuker",7677,3796,49.45,50891,24776,48.68,114784,56289,49.04,129604,63097,48.68,85800,41542,48.42,41730,20239,48.5,19823,9530,48.08,7112,3431,48.24
+80,Pugna,int,"Nuker, Pusher",2075,944,45.49,9998,4695,46.96,18962,8958,47.24,20240,9965,49.23,12807,6199,48.4,5825,2855,49.01,2758,1387,50.29,1195,592,49.54
+81,Queen of Pain,int,"Carry, Nuker, Escape",2287,1100,48.1,15119,7354,48.64,37137,18118,48.79,47706,23657,49.59,35500,18018,50.75,18405,9289,50.47,9243,4689,50.73,4227,2113,49.99
+82,Razor,agi,"Carry, Durable, Nuker, Pusher",2470,1231,49.84,12000,5964,49.7,24666,12142,49.23,30334,14844,48.94,21832,10558,48.36,11917,5679,47.65,6092,2912,47.8,3144,1551,49.33
+83,Riki,agi,"Carry, Escape, Disabler",3684,1929,52.36,19022,9891,52.0,35638,18582,52.14,33908,17415,51.36,20194,10312,51.06,8726,4377,50.16,3735,1855,49.67,1160,559,48.19
+84,Rubick,int,"Support, Disabler, Nuker",3090,1404,45.44,21639,9303,42.99,57417,24590,42.83,74874,32603,43.54,55186,24219,43.89,28206,12568,44.56,13732,6106,44.47,5764,2642,45.84
+85,Sand King,all,"Initiator, Disabler, Support, Nuker, Escape",2633,1513,57.46,13097,7323,55.91,25271,13807,54.64,26724,14323,53.6,17384,9144,52.6,7907,4104,51.9,3394,1719,50.65,1211,611,50.45
+86,Shadow Demon,int,"Support, Disabler, Initiator, Nuker",547,236,43.14,3252,1426,43.85,7920,3524,44.49,9752,4551,46.67,7404,3467,46.83,3956,1876,47.42,2076,1004,48.36,1054,497,47.15
+87,Shadow Fiend,agi,"Carry, Nuker",5051,2544,50.37,27255,14064,51.6,58589,29830,50.91,65429,33097,50.58,41810,21189,50.68,18766,9401,50.1,8232,4000,48.59,3016,1430,47.41
+88,Shadow Shaman,int,"Support, Pusher, Disabler, Nuker, Initiator",5323,2795,52.51,29733,15606,52.49,58894,31236,53.04,58765,30895,52.57,34475,18242,52.91,15166,7986,52.66,6377,3323,52.11,2413,1253,51.93
+89,Silencer,int,"Carry, Support, Disabler, Initiator, Nuker",4229,2324,54.95,27878,14960,53.66,61698,33081,53.62,65256,34458,52.8,38589,19853,51.45,16889,8653,51.23,6836,3416,49.97,2236,1105,49.42
+90,Skywrath Mage,int,"Support, Nuker, Disabler",4000,2030,50.75,22783,11675,51.24,46512,23624,50.79,51329,25706,50.08,34167,17364,50.82,16693,8415,50.41,8496,4208,49.53,4389,2069,47.14
+91,Slardar,str,"Carry, Durable, Initiator, Disabler, Escape",3935,2129,54.1,21523,11602,53.91,43947,23701,53.93,47721,25633,53.71,29887,16132,53.98,14233,7722,54.25,6530,3467,53.09,2322,1205,51.89
+92,Slark,agi,"Carry, Escape, Disabler, Nuker",4815,2521,52.36,29413,14762,50.19,64004,31771,49.64,70173,34411,49.04,44780,21926,48.96,20864,10270,49.22,9969,4962,49.77,4565,2394,52.44
+93,Snapfire,all,"Support, Nuker, Disabler, Escape",1524,682,44.75,10646,4576,42.98,27103,12120,44.72,34711,15412,44.4,24351,10786,44.29,11723,5131,43.77,5227,2294,43.89,1987,868,43.68
+94,Sniper,agi,"Carry, Nuker",8022,4079,50.85,44508,22727,51.06,88690,45223,50.99,87190,44086,50.56,47411,23648,49.88,18092,8924,49.33,6130,3040,49.59,1370,662,48.32
+95,Spectre,agi,"Carry, Durable, Escape",3454,2008,58.14,22097,12356,55.92,49157,26961,54.85,55914,30100,53.83,36321,19338,53.24,16946,8960,52.87,7921,4163,52.56,2568,1370,53.35
+96,Spirit Breaker,str,"Carry, Initiator, Disabler, Durable, Escape",4788,2423,50.61,26662,13530,50.75,56535,28908,51.13,63991,32249,50.4,42512,21357,50.24,20119,9926,49.34,9499,4814,50.68,3761,1884,50.09
+97,Storm Spirit,int,"Carry, Escape, Nuker, Initiator, Disabler",2202,1001,45.46,11656,5197,44.59,25644,11806,46.04,30968,14210,45.89,21680,10197,47.03,10810,5025,46.48,5278,2382,45.13,2363,1122,47.48
+98,Sven,str,"Carry, Disabler, Initiator, Durable, Nuker",3552,1761,49.58,19792,9744,49.23,41296,20478,49.59,48709,24228,49.74,35460,17828,50.28,19795,10065,50.85,11014,5655,51.34,6701,3387,50.54
+99,Techies,all,"Nuker, Disabler",2356,1131,48.01,13105,6245,47.65,27293,12893,47.24,29180,13507,46.29,18216,8407,46.15,8266,3771,45.62,3459,1644,47.53,1319,591,44.81
+100,Templar Assassin,agi,"Carry, Escape",2142,955,44.58,10932,4758,43.52,21211,9445,44.53,23928,10909,45.59,17399,8242,47.37,9567,4656,48.67,5525,2708,49.01,3524,1775,50.37
+101,Terrorblade,agi,"Carry, Pusher, Nuker",1115,484,43.41,5686,2430,42.74,10856,4638,42.72,11518,5041,43.77,8059,3540,43.93,4192,1827,43.58,2419,1082,44.73,1621,700,43.18
+102,Tidehunter,str,"Initiator, Durable, Disabler, Nuker, Carry",1835,855,46.59,11159,5369,48.11,26222,12699,48.43,30735,14879,48.41,20523,9727,47.4,9731,4740,48.71,4426,2079,46.97,1998,936,46.85
+103,Timbersaw,all,"Nuker, Durable, Escape",1050,448,42.67,5854,2584,44.14,12301,5391,43.83,14295,6097,42.65,9697,4217,43.49,4992,2163,43.33,2419,1021,42.21,1139,471,41.35
+104,Tinker,int,"Carry, Nuker, Pusher",2106,944,44.82,11058,5200,47.02,24263,11826,48.74,27531,13614,49.45,19017,9732,51.18,9416,4875,51.77,4700,2466,52.47,1951,1036,53.1
+105,Tiny,str,"Carry, Nuker, Pusher, Initiator, Durable, Disabler",1434,654,45.61,7742,3452,44.59,15936,6950,43.61,17139,7468,43.57,11269,4991,44.29,5485,2491,45.41,2599,1216,46.79,1058,519,49.05
+106,Treant Protector,str,"Support, Initiator, Durable, Disabler, Escape",1646,899,54.62,11430,5881,51.45,28752,15124,52.6,36093,19344,53.59,28762,15532,54.0,16751,9227,55.08,9870,5468,55.4,6801,3855,56.68
+107,Troll Warlord,agi,"Carry, Pusher, Disabler, Durable",3176,1720,54.16,14007,7445,53.15,24729,13022,52.66,25424,13228,52.03,17362,9030,52.01,9427,4913,52.12,4767,2499,52.42,2341,1242,53.05
+108,Tusk,str,"Initiator, Disabler, Nuker",1263,565,44.73,8338,3777,45.3,19642,8869,45.15,25308,11520,45.52,18927,8853,46.77,10100,4820,47.72,5220,2502,47.93,2350,1157,49.23
+109,Underlord,str,"Support, Nuker, Disabler, Durable, Escape",797,405,50.82,4583,2341,51.08,10067,5057,50.23,11650,5786,49.67,7224,3561,49.29,3310,1591,48.07,1368,673,49.2,395,190,48.1
+110,Undying,str,"Support, Durable, Disabler, Nuker",3170,1620,51.1,19403,10116,52.14,40582,21110,52.02,40850,21182,51.85,23985,12454,51.92,10395,5389,51.84,4541,2336,51.44,2064,1012,49.03
+111,Ursa,agi,"Carry, Durable, Disabler",2801,1273,45.45,15132,7038,46.51,33269,15478,46.52,40822,19264,47.19,29348,14011,47.74,15262,7375,48.32,7507,3622,48.25,3004,1473,49.03
+112,Vengeful Spirit,all,"Support, Initiator, Disabler, Nuker, Escape",2186,1108,50.69,15817,8285,52.38,41843,21809,52.12,57524,30476,52.98,45512,24120,53.0,25581,13382,52.31,13758,7121,51.76,8276,4303,51.99
+113,Venomancer,all,"Support, Nuker, Initiator, Pusher, Disabler",2309,1187,51.41,14669,7463,50.88,34787,18020,51.8,41797,21690,51.89,28706,15085,52.55,13974,7338,52.51,6538,3495,53.46,2794,1459,52.22
+114,Viper,agi,"Carry, Durable, Initiator, Disabler",4100,2057,50.17,18991,9510,50.08,33517,16923,50.49,32728,16677,50.96,18537,9427,50.86,7851,3928,50.03,3260,1652,50.67,1176,610,51.87
+115,Visage,all,"Support, Nuker, Durable, Disabler, Pusher",331,171,51.66,1638,813,49.63,3240,1577,48.67,3840,1986,51.72,3108,1609,51.77,1995,1055,52.88,1309,702,53.63,858,457,53.26
+116,Void Spirit,all,"Carry, Escape, Nuker, Disabler",1565,727,46.45,8672,4096,47.23,20010,9694,48.45,25213,12376,49.09,18817,9231,49.06,10026,4920,49.07,4788,2319,48.43,2006,964,48.06
+117,Warlock,int,"Support, Initiator, Disabler",2547,1369,53.75,18931,10331,54.57,49795,26999,54.22,66697,36220,54.31,48401,25668,53.03,24999,12942,51.77,12575,6356,50.54,6183,2934,47.45
+118,Weaver,agi,"Carry, Escape",2818,1389,49.29,13873,6770,48.8,23493,11571,49.25,21545,10694,49.64,12911,6427,49.78,5809,2928,50.4,2960,1455,49.16,1303,719,55.18
+119,Windranger,all,"Carry, Support, Disabler, Escape, Nuker",3861,1814,46.98,19934,9223,46.27,40644,18807,46.27,44476,20652,46.43,28952,13508,46.66,13418,6297,46.93,5898,2782,47.17,2374,1142,48.1
+120,Winter Wyvern,all,"Support, Disabler, Nuker",821,371,45.19,5168,2424,46.9,10544,5014,47.55,11184,5308,47.46,7426,3512,47.29,3730,1854,49.71,1862,934,50.16,944,464,49.15
+121,Witch Doctor,int,"Support, Nuker, Disabler",7504,4173,55.61,45501,25616,56.3,99664,54963,55.15,111382,60421,54.25,71830,37860,52.71,33164,17334,52.27,14610,7442,50.94,4196,2076,49.48
+122,Wraith King,str,"Carry, Support, Durable, Disabler, Initiator",4175,2266,54.28,26362,14516,55.06,58733,32403,55.17,66283,36503,55.07,42360,23083,54.49,19084,10251,53.72,8334,4315,51.78,2707,1376,50.83
+123,Zeus,int,"Nuker, Carry",4132,2106,50.97,23721,12487,52.64,51568,27475,53.28,58333,31078,53.28,37821,20047,53.0,17901,9504,53.09,8539,4459,52.22,3400,1791,52.68

belyaeva_ekaterina_lab_6/README.md

@@ -0,0 +1,92 @@
+## Задание 
+
+Использовать  нейронную  сеть MLPClassifier для  данных  из  таблицы  1  по  
+варианту, самостоятельно  сформулировав  задачу. Интерпретировать  результаты  и оценить,  насколько  хорошо  она подходит  для  решения сформулированной вами задачи
+
+## Как запустить лабораторную
+Запустить файл main.py
+## Используемые технологии
+Библиотеки pandas, scikit-learn, их компоненты
+## Описание лабораторной (программы)
+Данный код берет данные из датасета о персонажах Dota 2, где описаны атрибуты персонажей, их роли, название, и как часто их пикают и какой у них винрейт на каждом звании в Доте, от реркута до титана.
+
+В моем случае была поставлена задача понять, можно ли определить позицию персонажа (всего в игре есть 5 позиций - 
+carry, mid, offlane, support, full support), по его главному атрибуту и по тому, какие роли он выполняет в игре. Учитывая
+то, что Dota 2 имеет 124 персонажа, все они очень разные, поэтому была вероятность, что модель не установит зависимость и 
+не будет работать в принципе. Именно поэтому я посчитала данную задачу довольно интересной. В моем датасете присутствует информация о главном атрибуте персонажа и его ролях, но нет 
+информации о том, на каких позициях он играется. Поэтому для выяснения этого списка я обратилась к внешним ресурсам
+и занесла информацию об этом в программу вручную. Это можно увидеть в коде в месте, где определяются роли.
+
+![positions.png](positions.png)
+
+Программа берет столбцы Name, Roles, PrimaryAttribute из датасета. Так как в столбце Roles есть 9 значений, которые прописаны
+в разном количестве и разные у каждого персонажа, нужно было создать 9 дополнительных столбцов, где для каждого персонажа
+выставлялось 1, если такая роль присутствует в его описании и 0, если ее нет. 
+
+Пример:
+data['IsDurable'] = data['Roles'].apply(lambda x: 1 if 'Durable' in x else 0)
+
+Далее столбец Roles был удален.
+
+Так как PrimaryAttribute указан в строковом значении, он так же был переведен в числовое значение.
+
+После этого нужно было заполнить столбцы posCarry, posMid, posOfflane, posSupport, posFullSupport. Если персонаж есть в списке
+персонажей с этой позицией, там проставлялась 1, 0 - если нет.
+
+В итоге получился датасет, где есть имя персонажа, его главный атрибут в виде числа, его роли (1 - если есть, 0 - если нет)
+и то же самое с позициями.
+
+Далее датафрейм делится на признаки (все столбцы, кроме столбцов с позициями) и метки (столбцы с позициями). Метки переводятся в числовой формат с помощью LabelEncoder(), иначе программа не может с ними работать.
+Данные делятся на обучающую и тестовую выборку.  
+
+Модель создается таким образом потому, что если ставить меньшее число итераций или скрытых слоев, то она не успевала обучаться.
+model = MLPClassifier(hidden_layer_sizes=(128, 128, 128), activation='relu', max_iter=1000, random_state=42)
+
+Затем происходит предсказание позиций для тестовой выборки и оценка работы модели с помощью accuracy_score и classification_report
+
+## Результат
+
+В результате получаем следующее:
+
+![accuracy.png](accuracy.png)
+
+Оценка модели имеет относительно низкое значение. Однако, как было сказано ранее, она могла не работать в принципе, поэтому 
+я считаю это достаточно неплохим результатом и поставленная цель была выполнена - было выяснено, что позиция персонажа
+все-таки зависит от его атрибута и ролей, которые он выполняет по игре, хоть эта зависимость и не 100% явная. Если бы она
+была явная, например, все персонажи с атрибутом "сила" - это позиция offlane, тогда работа модели была бы значительно лучше.
+
+Далее мы получаем classification report:
+
+![classificationReport.png](classificationReport.png)
+
+В данном отчете представлены 5 классов, то есть позиции (0, 1, 2, 3, 4). Для каждого класса представлены значения точности, 
+полноты и F1-оценки, вычисленные с использованием соответствующих метрик. Также показана поддержка класса, которая 
+представляет собой количество образцов, принадлежащих этому классу.
+
+Precision (точность) - это метрика, которая оценивает долю правильно классифицированных объектов из всех объектов, которые модель отнесла к данному классу. Она измеряет, насколько точно модель предсказывает положительные классы. 
+
+Recall (полнота) - это метрика, которая оценивает долю правильно классифицированных объектов, отнесенных моделью к данному классу, относительно всех объектов, принадлежащих к данному классу. Она измеряет, насколько полно модель находит положительные классы.
+
+F1-мера (F1-score) - это гармоническое среднее между precision и recall. Она используется для объединения оценок точности и полноты в единую метрику. F1-мера принимает значение между 0 и 1, где 1 - это идеальное значение, означающее, что модель идеально находит и точно классифицирует объекты положительного класса
+
+micro avg - средневзвешенное значение точности, полноты и F1-оценки во всех классах, подсчитанное по общему количеству образцов.
+
+macro avg - среднее значение точности, полноты и F1-оценки по всем классам, без учета количества образцов.
+
+weighted avg - средневзвешенное значение точности, полноты и F1-оценки по всем классам, учитывая количество образцов.
+
+samples avg - средневзвешенное значение точности, полноты и F1-оценки по всем классам, учитывая количество образцов 
+класса (если образец может принадлежать нескольким классам).
+
+Из данного отчета можно сделать вывод о том, что по атрибутам и ролям в игре модель точно выявила персонажей для позиции 
+mid и offlane, но при этом, при работе с объектами, модель пропустила больше всего объектов, относящихся к этим классам,
+и занесла их в другие классы, из-за чего снизилась precision других классов. Мы сами должны выбирать, что важнее - точность или полнота,
+и в моем случае важнее точность, ведь изначально стоял вопрос о том, сможет ли модель определить, что к чему относится. Но низкие 
+значения полноты говорят о том, что низкое значение accuracy вполне оправдано, и хоть модель и может выявить, какие объекты к каким классам относятся, 
+делает она это не совсем "пОлно" и пропускает некоторые объекты.
+
+Что касается признаков micro avg, macro avg, weighted avg, samples avg - все они показывают неплохие результаты относительно 
+ожиданий по поводу работы модели. Я думаю, что для поставленной задачи значения этих показателей довольно высоки.
+
+Вывод: точность и показатели из отчета вышли достаточно хорошими относительно поставленной задачи, также был получен ответ на вопрос
+зависит ли позиция персонажа от его атрибута и роли. Следовательно, с задачей разработанная модель справилась.

belyaeva_ekaterina_lab_6/main.py

@@ -0,0 +1,76 @@
+import pandas as pd
+from sklearn.neural_network import MLPClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import LabelEncoder
+from sklearn.metrics import accuracy_score, classification_report
+
+# Чтение данных из файла Current_Pub_Meta.csv
+current_pub_meta = pd.read_csv('Current_Pub_Meta.csv')
+
+# Создаем пустой DataFrame для хранения данных
+data = pd.DataFrame(columns=['Name', 'Roles', 'Primary Attribute', 'IsDurable', 'IsSupport', 'IsCarry', 'IsDisabler',
+                             'IsInitiator', 'IsNuker', 'IsEscaper', 'IsPusher', 'posCarry', 'posMid',
+                           'posOfflane', 'posSupport', 'posHardSupport'])
+
+
+# Добавление новых столбцов из файла в датафрейм data
+data['Name'] = current_pub_meta['Name']
+data['Roles'] = current_pub_meta['Roles']
+data['Primary Attribute'] = current_pub_meta['Primary Attribute']
+data['Primary Attribute'] = data['Primary Attribute'].map({'str': 0, 'all': 1, 'int': 2, 'agi': 3})
+
+data['IsDurable'] = data['Roles'].apply(lambda x: 1 if 'Durable' in x else 0)
+data['IsCarry'] = data['Roles'].apply(lambda x: 1 if 'Carry' in x else 0)
+data['IsSupport'] = data['Roles'].apply(lambda x: 1 if 'Support' in x else 0)
+data['IsDisabler'] = data['Roles'].apply(lambda x: 1 if 'Disabler' in x else 0)
+data['IsInitiator'] = data['Roles'].apply(lambda x: 1 if 'Initiator' in x else 0)
+data['IsNuker'] = data['Roles'].apply(lambda x: 1 if 'Nuker' in x else 0)
+data['IsEscaper'] = data['Roles'].apply(lambda x: 1 if 'Escaper' in x else 0)
+data['IsPusher'] = data['Roles'].apply(lambda x: 1 if 'Pusher' in x else 0)
+
+#Удаление столбца Roles
+data.drop('Roles', axis=1, inplace=True)
+
+# Создаем список персонажей на каждую позицию
+roles = {
+    'posHardSupport': ['Undying', 'Pudge', 'Marci', 'Grimstroke', 'Elder Titan', 'Warlock', 'Dazzle', 'Witch Doctor', 'Vengeful Spirit', 'Ancient Apparition', 'Disruptor', 'Keeper of the Light', 'Rubick', 'Jakiro', 'Oracle', 'Visage', 'Silencer', 'Shadow Demon', 'Chen', 'Winter Wyvern', 'Bane', 'Treant Protector', 'Io', 'Enchantress', 'Naga Siren'],
+    'posSupport': ['Venomancer', 'Tusk', 'Tiny', 'Spirit Breaker', 'Techies', 'Snapfire', 'Pudge', 'Muerta', 'Marci', 'Hoodwink', 'Grimstroke', 'Earth Spirit', 'Bounty Hunter', 'Crystal Maiden', 'Lion', 'Shadow Shaman', 'Lich', 'Ogre Magi', 'Warlock', 'Dazzle', 'Witch Doctor', 'Vengeful Spirit', 'Ancient Apparition', 'Disruptor', 'Keeper of the Light', 'Rubick', 'Jakiro', 'Oracle', 'Visage', 'Silencer', 'Shadow Demon', 'Chen', 'Winter Wyvern', 'Bane', 'Treant Protector', 'Io', 'Enchantress', 'Naga Siren', 'Earthshaker', 'Skywrath Mage', 'Leshrac', 'Shadow Fiend', 'Nyx Assassin', 'Pugna', 'Lina', 'Zeus', "Nature's Prophet", 'Dark Willow'],
+    'posOfflane': ['Wraith King', 'Spirit Breaker', 'Snapfire', 'Pudge', 'Primal Beast', 'Marci', 'Dragon Knight', 'Tidehunter', 'Centaur Warrunner', 'Dark Seer', 'Beastmaster', 'Mars', 'Brewmaster', 'Timbersaw', 'Bristleback', 'Abaddon', 'Axe', 'Enigma', 'Sand King', 'Clockwerk', 'Doom', 'Underlord', 'Omniknight', 'Legion Commander', "Nature's Prophet", 'Slardar', 'Faceless Void', 'Earthshaker', 'Pangolier', 'Pugna', 'Mars', 'Batrider', 'Windranger', 'Mirana', 'Beastmaster', 'Brewmaster', 'Phoenix', 'Beastmaster', 'Dark Seer', 'Lone Druid', 'Timbersaw', 'Broodmother', "Nature's Prophet", 'Magnus', 'Necrophos', 'Bloodseeker', 'Lycan'],
+    'posMid': ['Void Spirit', 'Pudge', 'Primal Beast', 'Earth Spirit', 'Dragon Knight', 'Arc Warden', 'Invoker', 'Storm Spirit', 'Shadow Fiend', 'Templar Assassin', 'Queen of Pain', 'Puck', 'Zeus', 'Tinker', 'Lina', 'Ember Spirit', 'Outworld Destroyer', 'Morphling', 'Leshrac', 'Sniper', 'Mirana', 'Viper', 'Death Prophet', 'Razor', 'Pugna', 'Skywrath Mage', "Nature's Prophet", 'Windranger', 'Batrider', 'Lina', 'Shadow Fiend', 'Templar Assassin', 'Ember Spirit', 'Huskar', 'Kunkka', 'Puck', 'Queen of Pain', 'Invoker', 'Storm Spirit', 'Outworld Devourer', 'Death Prophet', 'Razor', 'Lina', 'Sniper', 'Medusa', 'Leshrac', 'Viper'],
+    'posCarry': ['Pudge', 'Muerta', 'Monkey King', 'Drow Ranger', 'Alchemist', 'Anti-Mage', 'Spectre', 'Juggernaut', 'Phantom Assassin', 'Faceless Void', 'Phantom Lancer', 'Lifestealer', 'Slark', 'Terrorblade', 'Medusa', 'Luna', 'Shadow Fiend', 'Morphling', 'Templar Assassin', 'Ember Spirit', 'Naga Siren', 'Troll Warlord', 'Gyrocopter', 'Lone Druid', 'Ursa', 'Riki', 'Sven', 'Phantom Lancer', 'Chaos Knight', 'Night Stalker', 'Wraith King', 'Meepo', 'Troll Warlord', 'Juggernaut', 'Lifestealer', 'Templar Assassin', 'Ursa', 'Clinkz', 'Weaver', 'Riki', 'Spectre', 'Phantom Assassin', 'Naga Siren', 'Luna', 'Gyrocopter', 'Meepo', 'Lone Druid', 'Slark', 'Morphling', 'Terrorblade', 'Medusa', 'Faceless Void']
+}
+
+# Перебираем каждого героя и добавляем значения в соответствующие столбцы
+for index, row in data.iterrows():
+    for role, characters in roles.items():
+        data.loc[index, role] = int(row['Name'] in characters)
+
+pd.set_option('display.max_columns', None)
+pd.set_option('display.max_rows', None)
+print(data)
+
+# Разделение датафрейма на признаки и метки
+X = data[['Primary Attribute', 'IsDurable', 'IsSupport', 'IsCarry', 'IsDisabler', 'IsInitiator', 'IsNuker', 'IsEscaper', 'IsPusher']]
+y = data[['posCarry', 'posMid', 'posOfflane', 'posSupport', 'posHardSupport']]
+
+# Преобразование меток в числовой формат
+label_encoder = LabelEncoder()
+y = y.apply(label_encoder.fit_transform)
+
+# Разделение выборки на обучающую и тестовую
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
+
+# Создание и обучение модели
+model = MLPClassifier(hidden_layer_sizes=(128, 128, 128), activation='relu', max_iter=1000, random_state=42)
+model.fit(X_train, y_train)
+
+# Предсказание позиций для тестовой выборки
+y_pred = model.predict(X_test)
+
+# Оценка точности модели
+accuracy = accuracy_score(y_test, y_pred)
+class_report = classification_report(y_test, y_pred)
+print("Accuracy:", accuracy)
+print('Classification Report:')
+print(class_report)

belyaeva_ekaterina_lab_7/README.md

@@ -0,0 +1,54 @@
+## Задание 
+
+Выбрать художественный текст (четные варианты – русскоязычный, нечетные – англоязычный) и обучить на нем рекуррентную 
+нейронную сеть для решения задачи генерации. Подобрать архитектуру и параметры так, чтобы приблизиться к максимально осмысленному результату.Далее разбиться на пары четный-нечетный вариант, обменяться разработанными сетями и проверить, как архитектура товарища справляется с вашим текстом.
+
+## Как запустить лабораторную
+Запустить файл main.py
+## Используемые технологии
+Библиотеки tensorflow, numpy, их компоненты
+## Описание лабораторной (программы)
+
+Данная лабораторная работа обучает модели для обработки русского и английского текста и решает задачу генерации.
+Ниже будет описан алгоритм работы одной из моделей (вторая работает аналогично):
+1. Читается текст из файла
+2. Создается экземпляр Tokenizer для токенизации текста
+3. С помощью метода fit_on_texts токенизатор анализирует текст и строит словарь уникальных слов
+4. rus_vocab_size - длина словаря
+5. C помощью метода text_to_sequences текст преобразуется в последовательность чисел
+6. Создаются последовательности для обучения модели
+7. Рассчитывается максимальная длина последовательности 
+8. Входные последовательности выравниваются до максимальной длины
+9. С помощью функции to_categorical последовательности преобразуются в one-hot представление
+10. Переменные x_rus_train, y_rus_train инициализируются соответствующими значениями
+11. Такая же обработка текста происходит и для текста на английском языке
+12. Происходит создание модели на русском языке: 
+ - создается экземпляр модели Sequential 
+ - добавляется слой Embedding, отображающий слова в векторы фиксированной длины
+ - добавляется слой LSTM с 512 нейронами
+ - добавляется слой Dense с функцией softmax для получения вероятности каждого слова в словаре
+ - модель компилируется
+13. Происходит обучение модели через model.fit()
+14. Все то же самое происходит для модели с английским языком
+15. Определяется функция generate_text для генерации текста на основе всех заданных параметров
+16. Выводятся результаты работы моделей и сгенерированные тексты
+
+## Результат
+
+Результат сгенерированного текста на русском языке: Помню просторный грязный двор и низкие домики обнесённые забором двор стоял у самой реки и по вёснам когда спадала полая вода он был усеян щепой и ракушками а иногда и другими куда более интересными вещами так однажды мы нашли туго набитую письмами сумку а потом вода принесла и осторожно положила на берег и самого почтальона он лежал на спине закинув руки как будто заслонясь от солнца ещё совсем молодой белокурый в форменной тужурке с блестящими пуговицами должно быть отправляясь в свой последний рейс почтальон начистил их мелом мелом мелом спадала щепой мелом мелом мелом мелом мелом спадала полая вода он ракушками а
+
+Результат сгенерированного текста на английском языке: The old man was thin and gaunt with deep wrinkles in the back of his neck the brown blotches of the benevolent skin cancer the sun brings from its reflection on the tropic sea were on his cheeks the blotches ran well down the sides of his face and his hands had the deep creased scars from handling heavy fish on the cords but none of these scars were fresh they were as old as erosions in a fishless desert fishless desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert fishless
+
+Результат потерь на тренировочных данных:
+
+![res.png](res.png)
+
+Вывод: можно заметить, что в сгенерированных текстах в конце слова повторяются. Это происходит потому, что в параметрах модели 
+указано сгенерировать 100 слов, хотя в тексте, по которому модель обучается, меньше слов. Поэтому сгенерированный текст сначала
+соответствует тексту для обучения, а затем начинает выдавать рандомные слова. Но нужно отметить, что это слова, а не просто 
+набор букв и пробелы, которые получались при иных настройках моделей.
+
+Так как у английской модели меньше потерь на тренировочных данных, чем у русской, то получается, что выполненная модель
+обрабатывает английский текст чуть лучше, чем русский, но в результате обе модели выдали осмысленный текст, что связано с большим
+числом нейронов и эпох, при помощи которых обучалась модель. Ведь когда было 20 эпох, а не 200, модель выдавала очень слабо осмысленный результат.
+

belyaeva_ekaterina_lab_7/eng.txt

@@ -0,0 +1,5 @@
+The old man was thin and gaunt with deep wrinkles in the back of his neck. The
+brown blotches of the benevolent skin cancer the sun brings from its reflection on the
+tropic sea were on his cheeks. The blotches ran well down the sides of his face and his
+hands had the deep-creased scars from handling heavy fish on the cords. But none of
+these scars were fresh. They were as old as erosions in a fishless desert.

belyaeva_ekaterina_lab_7/main.py

@@ -0,0 +1,97 @@
+import tensorflow as tf
+import numpy as np
+from keras.models import Sequential
+from keras.layers import LSTM, Dense, Embedding
+from keras.preprocessing.text import Tokenizer
+from keras.preprocessing.sequence import pad_sequences
+
+# Загрузка и предобработка данных на русском языке
+with open("rus.txt", "r", encoding="utf-8") as f:
+    rus_text = f.read()
+
+tokenizer_rus = Tokenizer()
+tokenizer_rus.fit_on_texts([rus_text])
+
+rus_vocab_size = len(tokenizer_rus.word_index) + 1
+rus_sequences = tokenizer_rus.texts_to_sequences([rus_text])[0]
+rus_input_sequences = []
+rus_output_sequences = []
+
+for i in range(1, len(rus_sequences)):
+    rus_input_sequences.append(rus_sequences[:i])
+    rus_output_sequences.append(rus_sequences[i])
+
+rus_max_sequence_len = max([len(seq) for seq in rus_input_sequences])
+rus_input_sequences = pad_sequences(rus_input_sequences, maxlen=rus_max_sequence_len)
+
+x_rus_train = rus_input_sequences
+y_rus_train = tf.keras.utils.to_categorical(rus_output_sequences, num_classes=rus_vocab_size)
+
+# Загрузка и предобработка данных на английском языке
+with open("eng.txt", "r", encoding="utf-8") as f:
+    eng_text = f.read()
+
+tokenizer_eng = Tokenizer()
+tokenizer_eng.fit_on_texts([eng_text])
+
+eng_vocab_size = len(tokenizer_eng.word_index) + 1
+eng_sequences = tokenizer_eng.texts_to_sequences([eng_text])[0]
+eng_input_sequences = []
+eng_output_sequences = []
+
+for i in range(1, len(eng_sequences)):
+    eng_input_sequences.append(eng_sequences[:i])
+    eng_output_sequences.append(eng_sequences[i])
+
+eng_max_sequence_len = max([len(seq) for seq in eng_input_sequences])
+eng_input_sequences = pad_sequences(eng_input_sequences, maxlen=eng_max_sequence_len)
+
+x_eng_train = eng_input_sequences
+y_eng_train = tf.keras.utils.to_categorical(eng_output_sequences, num_classes=eng_vocab_size)
+
+# Построение модели для русского языка
+rus_model = Sequential()
+rus_model.add(Embedding(rus_vocab_size, 256, input_length=rus_max_sequence_len))
+rus_model.add(LSTM(512))
+rus_model.add(Dense(rus_vocab_size, activation='softmax'))
+
+rus_model.compile(loss='categorical_crossentropy', optimizer='adam')
+
+# Обучение модели для русского языка
+rus_history = rus_model.fit(x_rus_train, y_rus_train, batch_size=128, epochs=200)
+
+# Построение модели для английского языка
+eng_model = Sequential()
+eng_model.add(Embedding(eng_vocab_size, 256, input_length=eng_max_sequence_len))
+eng_model.add(LSTM(512))
+eng_model.add(Dense(eng_vocab_size, activation='softmax'))
+
+eng_model.compile(loss='categorical_crossentropy', optimizer='adam')
+
+# Обучение модели для английского языка
+eng_history = eng_model.fit(x_eng_train, y_eng_train, batch_size=128, epochs=200)
+
+def generate_text(model, tokenizer, max_sequence_len, seed_text):
+    output_text = seed_text
+    for _ in range(100):  # Генерируем 100 слов
+        encoded_text = tokenizer.texts_to_sequences([output_text])[0]
+        pad_encoded = pad_sequences([encoded_text], maxlen=max_sequence_len, truncating='pre')
+        pred_word_index = np.argmax(model.predict(pad_encoded), axis=-1)
+        pred_word = tokenizer.index_word[pred_word_index[0]]
+        output_text += " " + pred_word
+    return output_text
+
+# Генерация текста для русской и английской моделей
+rus_output_text = generate_text(rus_model, tokenizer_rus, rus_max_sequence_len, "Помню просторный")
+eng_output_text = generate_text(eng_model, tokenizer_eng, eng_max_sequence_len, "The old man")
+
+# Вывод результатов
+print("Русская модель:")
+print("Потери на тренировочных данных:", rus_history.history['loss'][-1])
+print("Сгенерированный текст:")
+print(rus_output_text)
+
+print("Английская модель:")
+print("Потери на тренировочных данных:", eng_history.history['loss'][-1])
+print("Сгенерированный текст:")
+print(eng_output_text)

belyaeva_ekaterina_lab_7/rus.txt

@@ -0,0 +1 @@
+Помню просторный грязный двор и низкие домики, обнесённые забором. Двор стоял у самой реки, и по вёснам, когда спадала полая вода, он был усеян щепой и ракушками, а иногда и другими, куда более интересными вещами. Так, однажды мы нашли туго набитую письмами сумку, а потом вода принесла и осторожно положила на берег и самого почтальона. Он лежал на спине, закинув руки, как будто заслонясь от солнца, ещё совсем молодой, белокурый, в форменной тужурке с блестящими пуговицами: должно быть, отправляясь в свой последний рейс, почтальон начистил их мелом.

gusev_vladislav_lab_7/README.md

@@ -0,0 +1,41 @@
+### Вариант 9
+### Задание на лабораторную работу:
+Выбрать художественный текст (четные варианты – русскоязычный, нечетные – англоязычный) и 
+обучить на нем рекуррентную нейронную сеть для решения задачи генерации. 
+Подобрать архитектуру и параметры так, чтобы приблизиться к максимально осмысленному результату. 
+Далее разбиться на пары четный-нечетный вариант, обменяться разработанными сетями и проверить, 
+как архитектура товарища справляется с вашим текстом. 
+В завершении подобрать компромиссную архитектуру, справляющуюся достаточно хорошо с обоими видами 
+текстов.
+### Как запустить лабораторную работу:
+Выполняем файл gusev_vladislav_lab_7.py, решение будет в консоли.
+
+### Технологии
+Keras - это библиотека для Python, позволяющая легко и быстро создавать нейронные сети.
+NumPy - библиотека для работы с многомерными массивами.
+
+### По коду
+1) Читаем файл с текстом
+2) Создаем объект tokenizer для превращение текста в числа для нейронной сети.
+3) Создаем модель нейронной сети с следующими аргументами:
+
+- Embedding - это слой, который обычно используется для векторного представления категориальных данных, таких как слова или символы. Он позволяет нейронной сети изучать эмбеддинги, то есть отображение слов (или символов) в вектора низкой размерности. Это позволяет сети понимать семантические отношения между словами.
+- LSTM - это слой, представляющий собой рекуррентный нейрон, который способен учитывать зависимости в последовательных данных. Он хорошо подходит для обработки последовательных данных, таких как текст.
+- Dense - это полносвязный слой, который принимает входные данные и применяет весовые коэффициенты к ним. Этот слой часто используется в конце нейронных сетей для решения задачи классификации или регрессии.
+
+4) Обучаем модель на 100 эпохах (итерациях по данным) и генерируем текст.
+
+
+
+![img.png](img.png)
+Английский 100 эпох
+![img_1.png](img_1.png)
+
+![img_3.png](img_3.png)
+Русский 100 эпох
+![img_2.png](img_2.png)
+Русский 17 эпох
+![img_4.png](img_4.png)
+### По консоли
+- Английский текст генерировался на 100 эпохах, начало получилось осмысленным, но чем ближе к концу тем хуже.
+- Русский текст также генерировался на 100 эпохах, с многочисленными ошибками в словах. Русский текст,сгенерированный на 17 эпохах по ошибкам в словах оказался лучше, но всё равно не идеально.

gusev_vladislav_lab_7/gusev_vladislav_lab_7.py

@@ -0,0 +1,61 @@
+import numpy as np
+from keras.models import Sequential
+from keras.layers import Embedding, LSTM, Dense
+from keras.preprocessing.text import Tokenizer
+from keras.preprocessing.sequence import pad_sequences
+
+# Загрузка текста из файла
+with open('text_ru.txt', 'r', encoding='utf-8') as file:
+    text = file.read()
+
+# Создание экземпляра Tokenizer
+tokenizer = Tokenizer(char_level=True)
+tokenizer.fit_on_texts(text)
+
+# Преобразование текста в последовательность чисел
+sequences = tokenizer.texts_to_sequences(text)
+
+# Подготовка обучающих данных
+seq_length = 100
+dataX, dataY = [], []
+for i in range(0, len(sequences) - seq_length):
+    seq_in = sequences[i:i + seq_length]
+    seq_out = sequences[i + seq_length]
+    dataX.append(seq_in)
+    dataY.append(seq_out)
+
+dataX = np.array(dataX)
+dataY = np.array(dataY)
+
+# Создание модели
+vocab_size = len(tokenizer.word_index) + 1
+embedding_dim = 256
+rnn_units = 1024
+
+model = Sequential()
+model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=seq_length))
+model.add(LSTM(units=rnn_units))
+model.add(Dense(units=vocab_size, activation='softmax'))
+
+model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')
+
+# Обучение модели
+batch_size = 64
+model.fit(dataX, dataY, epochs=17, batch_size=batch_size)
+def generate_text(seed_text, gen_length):
+    generated_text = seed_text
+
+    for _ in range(gen_length):
+        sequence = tokenizer.texts_to_sequences([seed_text])[0]
+        sequence = pad_sequences([sequence], maxlen=seq_length)
+        prediction = model.predict(sequence)[0]
+        predicted_index = np.argmax(prediction)
+        predicted_char = tokenizer.index_word[predicted_index]
+        generated_text += predicted_char
+        seed_text += predicted_char
+        seed_text = seed_text[1:]
+
+    return generated_text
+# Пример использования
+generated_text = generate_text("Мультфильмы", 250)
+print(generated_text)

gusev_vladislav_lab_7/text_eng.txt

@@ -0,0 +1,21 @@
+Do you like watching cartoons? Probably you do! But how did they come to be? Who invented them?
+
+This is actually a very tough question. The first cartoons were created long before the TV.
+For example, shadow play was a very popular form of entertainment in ancient China. Such shows looked almost like modern cartoons!
+
+A toy called a flip book was made in the late 19th century. It was a small soft book with pictures.
+Each picture was drawn in a slightly different5 way. When you bend this book and release the pages one by one, the images start to move.
+Strictly speaking, they don’t, but our eyes see it like that anyway. The first real cartoons were made using this trick, too!
+
+In 1895 brothers Louis and Auguste Lumière created a cinematograph.
+It was a camera and a film projector in one device. Using this device, many aspiring film directors started to create their own cartoons.
+
+This developed into a full industry by 1910. Many cartoons of that era are forgotten now, but some are still with us.
+For example, Felix the Cat was created by Otto Messmer in 1919, and he’s still with us, more than a hundred years later.
+Currently the rights to the character are held by DreamWorks Animation.
+
+One of the pioneers in the industry was famous Walt Disney.
+He was not afraid to experiment to make a cartoon, and his Snow White film was among the firsts to use a multiplane camera.
+With its help the characters were able to move around the objects, creating an illusion of a 3D world.
+
+Today most of the cartoons are made with computer animation. The last traditional Disney cartoon to date was Winnie the Pooh (2011).

gusev_vladislav_lab_7/text_ru.txt

@@ -0,0 +1,21 @@
+Вам нравится смотреть мультфильмы? Вероятно, так оно и есть! Но как они появились на свет? Кто их изобрел?
+
+На самом деле это очень сложный вопрос. Первые мультфильмы были созданы задолго до появления телевидения.
+Например, игра с тенью была очень популярной формой развлечения в Древнем Китае. Такие шоу выглядели почти как современные мультфильмы!
+
+Игрушка под названием книжка-перевертыш была изготовлена в конце 19 века. Это была маленькая мягкая книжка с картинками.
+Каждая картинка была нарисована немного по-разному. Когда вы сгибаете эту книгу и отпускаете страницы одну за другой, изображения начинают двигаться.
+Строго говоря, это не так, но наши глаза все равно видят это именно так. Первые настоящие мультфильмы тоже были сделаны с использованием этого трюка!
+
+В 1895 году братья Луи и Огюст Люмьер создали кинематограф.
+Это была камера и кинопроектор в одном устройстве. Используя это устройство, многие начинающие режиссеры начали создавать свои собственные мультфильмы.
+
+К 1910 году это развилось в полноценную индустрию. Многие мультфильмы той эпохи сейчас забыты, но некоторые все еще с нами.
+Например, кот Феликс был создан Отто Мессмером в 1919 году, и он все еще с нами, более ста лет спустя.
+В настоящее время правами на персонажа владеет DreamWorks Animation.
+
+Одним из пионеров в этой отрасли был знаменитый Уолт Дисней.
+Он не боялся экспериментировать при создании мультфильма, и его фильм "Белоснежка" был одним из первых, в котором использовалась многоплановая камера.
+С его помощью персонажи смогли передвигаться по объектам, создавая иллюзию трехмерного мира.
+
+Сегодня большинство мультфильмов создано с использованием компьютерной анимации. Последним традиционным диснеевским мультфильмом на сегодняшний день был "Винни-Пух" (2011).

kurmyza_pavel_lab_5/README.md

@@ -0,0 +1,53 @@
+# Лабораторная работа №5
+
+## ПИбд-41, Курмыза Павел
+
+Датасет по варианту: https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand.
+
+Данный набор данных содержит информацию о бронировании городской и курортной гостиниц и включает в себя такие
+сведения, как время бронирования, продолжительность пребывания, количество взрослых, детей и/или младенцев, количество
+свободных парковочных мест и т.д.
+
+## Как запустить ЛР
+
+- Запустить файл main.py
+
+## Используемые технологии
+
+- Язык программирования Python
+- Библиотеки: sklearn, numpy, pandas
+
+## Что делает программа
+
+Программа решает задачу кластеризации на выбранном датасете: выделение наиболее прибыльных посетителей отелей на основе
+их времени прибывания и средней цены одной ночи пребывания в отели. Решение достигается в несколько этапов:
+
+- Предобработка данных
+- Стандартизация данных и приведение их к виду, удобном для работы с моделями ML
+- Использование модели кластеризации K-средних
+- Визуализация полученных результатов и вывод
+
+## Тестирование
+
+Теперь мы рассмотрели задачу кластеризации K-средних, и проанализируем результаты каждого
+кластера, чтобы определить наиболее прибыльных клиентов в нашем наборе данных на основе времени выполнения заказа и ADR.
+Первая проблема, с которой мы сталкиваемся, когда хотим использовать кластеризацию с помощью K-средних, - это
+определение оптимального количества кластеров, которые мы хотим получить в качестве результатов. Поэтому сначала для
+определения количества кластеров мы использовали метод локтя:
+
+![Кластеры](clusters.jpg)
+
+Для определения оптимального количества кластеров необходимо выбрать значение k, после которого искажение начинает
+линейно уменьшаться. Таким образом, мы пришли к выводу, что оптимальное количество кластеров для данных равно 4. Поэтому
+мы запустили алгоритм K-средних на основе lead_time и ADR с количеством кластеров, равным 4, и вывели центры кластеров:
+
+![Центры кластеров](centers.jpg)
+
+## Вывод
+
+Наиболее прибыльными считаются клиенты с наименьшим временем пребывания и наибольшим ADR, т.е. клиенты, попавшие в
+зеленый кластер. В то время как красная категория показывает самый низкий ADR и самое высокое (наименее выгодное) время
+пребывания. В нашем случае после визуализации графика мы можем задать такие вопросы, как: почему у
+одних клиентов время пребывания меньше, чем у других? и есть ли вероятность, что клиенты в определенных странах
+соответствуют этому профилю? и т.д. На все эти вопросы алгоритм кластеризации K-средних может и не ответить напрямую,
+но сведение данных в отдельные кластеры обеспечивает надежную основу для постановки подобных вопросов.

kurmyza_pavel_lab_5/main.py

@@ -0,0 +1,81 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import datetime as dt
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.preprocessing import LabelEncoder
+import sklearn.cluster as cluster
+
+# Чтение данных датасета
+df = pd.read_csv('hotel_bookings.csv')
+
+# Удаление строк, содержащих отсутствующие значения
+df = df[df['children'].notna()]
+df = df[df['country'].notna()]
+
+# Объединение столбцов 'arrival_date_year', 'arrival_date_month', 'arrival date day_of_month' в столбец
+# 'arrival_date', содержащий день, месяц и год приезда клиента в формате datetime
+df["arrival_date_month"] = pd.to_datetime(df['arrival_date_month'], format='%B').dt.month
+df["arrival_date"] = pd.to_datetime({"year": df["arrival_date_year"].values,
+                                     "month": df["arrival_date_month"].values,
+                                     "day": df["arrival_date_day_of_month"].values})
+df = df.drop(columns=['arrival_date_year', 'arrival_date_month', 'arrival_date_day_of_month'])
+
+# Преобразование типа столбца reservation_status_date в datetime
+df["reservation_status_date"] = pd.to_datetime(df["reservation_status_date"], format='%Y-%m-%d')
+
+# Заполнение нулевых значений в столбцах средним значением каждого столбца
+for column in ['agent', 'company', 'arrival_date']:
+    df[column] = df[column].fillna(df[column].mean())
+
+# Удаляем повторяющиеся значения
+df.drop_duplicates(inplace=True)
+
+# Преобразование категориальных переменных в числовые переменные для того, чтобы модель могла с ними работать
+categoricalV = ["hotel", "meal", "country", "market_segment", "distribution_channel", "reserved_room_type",
+                "assigned_room_type", "deposit_type", "customer_type"]
+df[categoricalV[1:11]] = df[categoricalV[1:11]].astype('category')
+
+df[categoricalV[1:11]] = df[categoricalV[1:11]].apply(lambda x: LabelEncoder().fit_transform(x))
+
+df['hotel_Num'] = LabelEncoder().fit_transform(df['hotel'])
+
+df['numerical_larrival_date'] = df['arrival_date'].map(dt.datetime.toordinal)
+df['numerical_reservation_status_date'] = df['reservation_status_date'].map(dt.datetime.toordinal)
+
+df["is_canceled"].replace({'not canceled': 0, 'canceled': 1}, inplace=True)
+df["reservation_status"].replace({'Canceled': 0, 'Check-Out': 1, 'No-Show': 2}, inplace=True)
+
+# Определение входных и выходных значений
+usefull_columns = df.columns.difference(['hotel', 'hotel_Num', 'arrival_date', 'reservation_status_date'])
+X = df[usefull_columns]
+Y = df["hotel_Num"].astype(int)
+
+# Деление данных на тестовую и обучающую выборки
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=150)
+
+# Определение оптимального количества кластеров
+df_Short = df[['lead_time', 'adr']]
+
+K = range(1, 12)
+wss = []
+for k in K:
+    kmeans = cluster.KMeans(n_clusters=k, init="k-means++")
+    kmeans = kmeans.fit(df_Short)
+    wss_iter = kmeans.inertia_
+    wss.append(wss_iter)
+
+mycenters = pd.DataFrame({'Clusters': K, 'WSS': wss})
+
+sns.scatterplot(x='Clusters', y='WSS', data=mycenters, marker="+")
+
+# Решение задачи кластеризации с использованием K-Means
+kmeans = cluster.KMeans(n_clusters=4, init="k-means++")
+kmeans = kmeans.fit(df[['lead_time', 'adr']])
+df['Clusters'] = kmeans.labels_
+
+# Визуализируем кластеры
+sns.lmplot(x="lead_time", y="adr", hue='Clusters', data=df)
+plt.ylim(0, 600)
+plt.xlim(0, 800)
+plt.show()

kurmyza_pavel_lab_6/README.md

@@ -0,0 +1,51 @@
+# Лабораторная работа №6
+
+## ПИбд-41, Курмыза Павел
+
+Датасет по варианту: https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand.
+
+Данный набор данных содержит информацию о бронировании городской и курортной гостиниц и включает в себя такие
+сведения, как время бронирования, продолжительность пребывания, количество взрослых, детей и/или младенцев, количество
+свободных парковочных мест и т.д.
+
+## Как запустить ЛР
+
+- Запустить файл main.py
+
+## Используемые технологии
+
+- Язык программирования Python
+- Библиотеки: sklearn, numpy, pandas, xgboost, matplotlib, seaborn
+
+## Что делает программа
+
+Программа решает задачу классификации на выбранном датасете: определение гостиничного класса отеля (городской отель или
+курортный отель). Решение достигается в несколько этапов:
+
+- Предобработка данных
+- Балансировка данных
+- Стандартизация данных и приведение их к виду, удобном для работы с моделью ML
+- Использование модели классификации MLPClassifier
+- Оценка точности и специфичности данной модели классификации
+
+## Тестирование
+
+Для решения задачи классификации были выбрана модель MLPClassifier.
+
+Оценка точности модели: 0.9778297119757453
+
+![Отчет классификации](classification_report.jpg)
+
+Оценка способности модели MLPClassifier предсказывать истинные положительные результаты (TP / (TP + FN)), также
+известные как коэффициент чувствительности, и истинные отрицательные результаты (TN / (TN + FP)), также известный как
+коэффициент специфичности через матрицу неточностей:
+
+![Матрица неточностей](confusion_matrix.jpg)
+
+Матрица неточностей подтверждает приведенную ранее оценку модели MLPClassifier. Кроме того, она указывает на
+то, что помимо высокой точности, модель также имеет высокую специфичность.
+
+## Вывод
+
+По итогу тестирования было выявлено, что модель MLPClassifier подходит для решения поставленной задачи, на что указывают
+высокая оценка точности (97%) и специфичности данной модели.

kurmyza_pavel_lab_6/main.py

@@ -0,0 +1,104 @@
+import pandas as pd
+from matplotlib import pyplot as plt
+from sklearn.preprocessing import LabelEncoder
+from sklearn.feature_selection import VarianceThreshold
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import confusion_matrix, classification_report
+import seaborn as sns
+from sklearn.neural_network import MLPClassifier
+
+# Считываем датасет
+ds = pd.read_csv('hotel_bookings.csv')
+
+# Удаляем из датасета строки с пропущенными значениями столбцов country, children.
+# Выбраны именно данные столбцы, так как, по информации из kaggle, только они могут содержать пропущеные значения
+ds.dropna(axis=0, subset=['country', 'children'], inplace=True)
+
+# Усредняем значения столбца agent, чтобы убрать его влияние на результат, так как столбец содержит неважные данные
+moa = ds['agent'].mean()
+ds['agent'].fillna(value=moa, axis=0, inplace=True)
+
+# Заполняем пропущенные значения ячеек, чтобы исключить незаполненные
+ds.fillna(method='pad', inplace=True)
+ds.dropna(inplace=True, subset=['company'])
+
+# Переводим столбцы, содержащие текстовые данные в числовое представление
+hotel = LabelEncoder()
+meal = LabelEncoder()
+country = LabelEncoder()
+market_segment = LabelEncoder()
+distribution_channel = LabelEncoder()
+reserved_room_type = LabelEncoder()
+assigned_room_type = LabelEncoder()
+deposit_type = LabelEncoder()
+customer_type = LabelEncoder()
+reservation_status = LabelEncoder()
+reservation_status_date = LabelEncoder()
+
+ds['hotel_n'] = hotel.fit_transform(ds['hotel'])
+ds['arrival_date_month_n'] = hotel.fit_transform(ds['arrival_date_month'])
+ds['meal_n'] = hotel.fit_transform(ds['meal'])
+ds['country_n'] = hotel.fit_transform(ds['country'])
+ds['market_segment_n'] = hotel.fit_transform(ds['market_segment'])
+ds['distribution_channel_n'] = hotel.fit_transform(ds['distribution_channel'])
+ds['reserved_room_type_n'] = hotel.fit_transform(ds['reserved_room_type'])
+ds['assigned_room_type_n'] = hotel.fit_transform(ds['assigned_room_type'])
+ds['deposit_type_n'] = hotel.fit_transform(ds['deposit_type'])
+ds['customer_type_n'] = hotel.fit_transform(ds['customer_type'])
+ds['reservation_status_n'] = hotel.fit_transform(ds['reservation_status'])
+ds['reservation_status_date_n'] = hotel.fit_transform(ds['reservation_status_date'])
+
+# Удаляем приведенные к числовым данным столбцы, они больше не нужны
+ds.drop(
+    ['hotel', 'arrival_date_month', 'meal', 'country', 'market_segment', 'distribution_channel', 'reserved_room_type',
+     'assigned_room_type', 'deposit_type', 'customer_type', 'reservation_status', 'reservation_status_date'], axis=1,
+    inplace=True)
+
+# Производим балансировку данных таким образом, чтобы было одинаковое количество отелей всех классов
+ds_0 = ds[ds['hotel_n'] == 0]
+ds_1 = ds[ds['hotel_n'] == 1]
+ds_0 = ds_0.sample(ds_1.shape[0])
+ds = ds_0._append(ds_1, ignore_index=True)
+
+# Полдготовка данных для выполнения модели
+x = ds.drop('hotel_n', axis=1)
+y = ds['hotel_n']
+
+threshold = VarianceThreshold()
+
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+x_train = threshold.fit_transform(x_train)
+x_test = threshold.transform(x_test)
+
+# Производим стандартизацию данных и приводим их к виду, с которым работают модель классификации MLPClassifier
+scaler = StandardScaler()
+
+x_train = scaler.fit_transform(x_train)
+x_test = scaler.fit_transform(x_test)
+
+y_train = y_train.to_numpy()
+y_test = y_test.to_numpy()
+
+# Инициализируем модель MLPClassifier и обучаем её
+
+mlp = MLPClassifier()
+mlp.fit(x_train, y_train)
+
+# Оценка точности моделей классификации
+
+mlp_accuracy = mlp.score(x_test, y_test)
+print(f"Оценка точности модели: {mlp_accuracy}")
+
+# Оценка коэффициента специфичности через матрицу неточностей
+
+y_pred = mlp.predict(x_test)
+
+cm = confusion_matrix(y_test, y_pred)
+plt.figure(figsize=(7, 5))
+sns.heatmap(cm, annot=True)
+plt.xlabel('Prediction')
+plt.ylabel('Actual')
+plt.show()
+
+print(classification_report(y_test, y_pred))

kutygin_andre_lab_3/README.md

@@ -0,0 +1,118 @@
+**Задание**
+***
+Решите с помощью библиотечной реализации дерева решений задачу из лабораторной работы «Веб-сервис «Дерево решений» по предмету «Методы искусственного интеллекта»на 99% ваших данных. Проверьте работу модели на оставшемся проценте, сделайте вывод
+
+**Как запустить лабораторную**
+***
+Запустить файл main.py
+
+**Используемые технологии**
+***
+Библиотеки pandas, scikit-learn, matplotlib, их компоненты
+
+**Описание лабораторной (программы)**
+***
+В данном коде мы создаем и обучаем модель дерева решений для прогнозирования инцидентов с НЛО на основе набора данных.
+
+1. В первой строке кода мы загружаем данные из CSV-файла 'ufo_data_nuforc.csv' с помощью функции pd.read_csv(). Эти данные содержат информацию о различных инцидентах с НЛО.
+2. Далее мы выбираем набор признаков, в данном случае, эти признаки - населенность и время, которые будут использоваться для обучения модели, и сохраняем их в переменную features.
+3. Затем преобразуем категориальные признаки в числовой вид при помощи функции pd.get_dummies(). Это необходимо, так как модель дерева решений работает только с числовыми данными.
+4. После этого мы разделяем данные на обучающую и тестовую выборки с помощью функции train_test_split(). Обучающая выборка будет использоваться для обучения модели, а тестовая - для проверки ее точности.
+5. Создаем модель дерева решений с помощью класса DecisionTreeClassifier() из библиотеки sklearn.tree.
+6. Обучаем модель на обучающей выборке с помощью метода fit(). В процессе обучения модель настраивает параметры дерева решений, чтобы лучше предсказывать целевой признак.
+7. После обучения модели, мы производим прогнозы на тестовых данных с помощью метода predict().
+8. Оцениваем точность модели на тестовой выборке с помощью метода accuracy_score() из библиотеки sklearn.metrics. Этот метод сравнивает фактические значения целевого признака с предсказанными и возвращает точность модели.
+9. Наконец, выводим точность модели на тестовой выборке, чтобы оценить, насколько хорошо модель предсказывает инциденты с НЛО.
+10. Также, код визуализирует данные в виде графика с помощью библиотеки matplotlib.pyplot, отображая фактические значения целевого признака и предсказания модели. Это помогает наглядно оценить, насколько близки предсказания модели к реальным значениям.
+**Результат**
+***
+Точность модели на тестовой выборке: 0.1377245508982036
+Прогнозы по оставшемуся проценту данных: 'cylinder' 'circle' 'sphere' 'disk' 'disk' 'fireball' 'disk' 'oval'
+ 'circle' 'disk' 'disk' 'other' 'light' 'light' 'oval' 'fireball' 'light'
+ 'rectangle' 'chevron' 'unknown' 'sphere' 'oval' 'light' 'circle'
+ 'unknown' 'unknown' 'disk' 'triangle' 'triangle' 'unknown' 'formation'
+ 'unknown' 'cigar' 'unknown' 'light' 'other' 'rectangle' 'light' 'other'
+ 'light' 'cylinder' 'delta' 'sphere' 'other' 'changing' 'fireball'
+ 'cylinder' 'cigar' 'circle' 'triangle' 'light' 'fireball' 'fireball'
+ 'sphere' 'circle' 'light' 'chevron' 'oval' 'oval' 'light' 'unknown'
+ 'triangle' 'other' 'rectangle' 'triangle' 'triangle' 'flash' 'unknown'
+ 'sphere' 'unknown' 'other' 'circle' 'oval' 'light' 'oval' 'formation'
+ 'sphere' 'triangle' 'changing' 'sphere' 'oval' 'unknown' 'circle'
+ 'circle' 'flash' 'light' 'light' 'sphere' 'other' 'other' 'egg' 'unknown'
+ 'other' 'light' 'light' 'disk' 'diamond' 'oval' 'unknown' 'light'
+ 'triangle' 'other' 'light' 'disk' 'unknown' 'light' 'changing' 'sphere'
+ 'triangle' 'circle' 'flash' 'sphere' 'light' 'unknown' 'oval' 'formation'
+ 'light' 'circle' 'unknown' 'other' 'triangle' 'other' 'light' 'disk'
+ 'formation' 'oval' 'triangle' 'triangle' 'light' 'formation' 'oval'
+ 'light' 'light' 'oval' 'disk' 'sphere' 'egg' 'unknown' 'unknown'
+ 'unknown' 'light' 'disk' 'changing' 'light' 'light' 'circle' 'circle'
+ 'formation' 'light' 'light' 'cigar' 'light' 'triangle' 'oval' 'fireball'
+ 'cylinder' 'other' 'circle' 'egg' 'changing' 'triangle' 'circle' 'other'
+ 'oval' 'disk' 'light' 'flash' 'fireball' 'circle' 'circle' 'circle'
+ 'circle' 'light' 'disk' 'fireball' 'other' 'sphere' 'light' 'changing'
+ 'cigar' 'light' 'cylinder' 'rectangle' 'chevron' 'light' 'light' 'light'
+ 'light' 'circle' 'circle' 'light' 'light' 'circle' 'sphere' 'triangle'
+ 'light' 'egg' 'circle' 'fireball' 'sphere' 'sphere' 'triangle' 'light'
+ 'other' 'cigar' 'sphere' 'sphere' 'fireball' 'light' 'light' 'disk'
+ 'oval' 'oval' 'other' 'cigar' 'triangle' 'light' 'light' 'light' 'disk'
+ 'light' 'light' 'light' 'light' 'other' 'light' 'teardrop' 'triangle'
+ 'teardrop' 'fireball' 'sphere' 'cylinder' 'fireball' 'circle' 'egg'
+ 'sphere' 'disk' 'chevron' 'triangle' 'light' 'other' 'light' 'circle'
+ 'rectangle' 'fireball' 'formation' 'light' 'light' 'circle' 'light'
+ 'light' 'formation' 'light' 'triangle' 'light' 'oval' 'light' 'unknown'
+ 'fireball' 'diamond' 'light' 'circle' 'light' 'triangle' 'oval' 'oval'
+ 'cylinder' 'circle' 'light' 'disk' 'light' 'sphere' 'circle' 'light'
+ 'triangle' 'light' 'fireball' 'triangle' 'light' 'flash' 'triangle' 'egg'
+ 'disk' 'oval' 'circle' 'flash' 'light' 'oval' 'sphere' 'light' 'triangle'
+ 'other' 'chevron' 'other' 'circle' 'unknown' 'unknown' 'sphere' 'light'
+ 'cigar' 'light' 'fireball' 'circle' 'diamond' 'fireball' 'triangle'
+ 'diamond' 'sphere' 'circle' 'chevron' 'cylinder' 'light' 'circle'
+ 'fireball' 'unknown' 'light' 'circle' 'fireball' 'light' 'fireball'
+ 'fireball' 'fireball' 'light' 'sphere' 'light' 'sphere' 'sphere'
+ 'formation' 'light' 'fireball' 'fireball' 'disk' 'disk' 'circle'
+ 'rectangle' 'unknown' 'disk' 'unknown' 'disk' 'triangle' 'other' 'sphere'
+ 'diamond' 'light' 'light' 'unknown' 'sphere' 'circle' 'disk' 'circle'
+ 'oval' 'changing' 'other' 'other' 'disk' 'unknown' 'unknown' 'disk'
+ 'rectangle' 'disk' 'light' 'oval' 'unknown' 'sphere' 'light' 'changing'
+ 'disk' 'disk' 'other' 'other' 'disk' 'cylinder' 'disk' 'rectangle'
+ 'light' 'disk' 'disk' 'light' 'fireball' 'formation' 'cigar' 'oval'
+ 'fireball' 'unknown' 'disk' 'light' 'light' 'triangle' 'triangle' 'light'
+ 'sphere' 'triangle' 'sphere' 'circle' 'light' 'oval' 'oval' 'circle'
+ 'oval' 'rectangle' 'disk' 'oval' 'light' 'light' 'other' 'cigar'
+ 'triangle' 'disk' 'cigar' 'other' 'triangle' 'egg' 'unknown' 'triangle'
+ 'light' 'triangle' 'disk' 'changing' 'triangle' 'disk' 'disk' 'rectangle'
+ 'other' 'triangle' 'triangle' 'formation' 'triangle' 'egg' 'sphere'
+ 'fireball' 'triangle' 'rectangle' 'light' 'triangle' 'triangle' 'other'
+ 'light' 'light' 'disk' 'fireball' 'light' 'disk' 'oval' 'triangle'
+ 'other' 'fireball' 'light' 'light' 'triangle' 'unknown' 'cigar' 'light'
+ 'unknown' 'chevron' 'formation' 'disk' 'cigar' 'light' 'sphere' 'cigar'
+ 'unknown' 'triangle' 'other' 'light' 'light' 'triangle' 'diamond' 'light'
+ 'triangle' 'oval' 'changing' 'light' 'flash' 'circle' 'oval' 'other'
+ 'sphere' 'circle' 'triangle' 'unknown' 'teardrop' 'unknown' 'fireball'
+ 'light' 'light' 'cigar' 'cigar' 'light' 'fireball' 'other' 'egg' 'light'
+ 'other' 'unknown' 'unknown' 'changing' 'circle' 'light' 'other' 'unknown'
+ 'unknown' 'light' 'other' 'light' 'unknown' 'cylinder' 'triangle'
+ 'circle' 'light' 'circle' 'circle' 'circle' 'light' 'light' 'changing'
+ 'changing' 'circle' 'circle' 'triangle' 'triangle' 'light' 'light'
+ 'light' 'light' 'other' 'changing' 'triangle' 'cylinder' 'light'
+ 'unknown' 'circle' 'disk' 'sphere' 'oval' 'formation' 'teardrop'
+ 'triangle' 'chevron' 'light' 'unknown' 'unknown' 'other' 'egg' 'circle'
+ 'oval' 'cigar' 'unknown' 'chevron' 'oval' 'cigar' 'fireball' 'circle'
+ 'unknown' 'light' 'sphere' 'fireball' 'changing' 'light' 'circle'
+ 'unknown' 'fireball' 'light' 'sphere' 'light' 'formation' 'circle'
+ 'fireball' 'formation' 'formation' 'formation' 'light' 'other' 'light'
+ 'light' 'circle' 'diamond' 'oval' 'circle' 'oval' 'triangle' 'light'
+ 'disk' 'light' 'other' 'triangle' 'triangle' 'cylinder' 'disk' 'cylinder'
+ 'light' 'oval' 'cigar' 'circle' 'disk' 'light' 'unknown' 'circle' 'other'
+ 'light' 'light' 'light' 'unknown' 'triangle' 'other' 'disk' 'cylinder'
+ 'triangle' 'oval' 'disk' 'light' 'triangle' 'circle' 'light' 'other'
+ 'light' 'other' 'circle' 'disk' 'other' 'triangle' 'oval' 'unknown'
+ 'light' 'triangle' 'unknown' 'circle' 'unknown' 'light' 'fireball'
+ 'fireball' 'rectangle' 'light' 'formation' 'unknown' 'light' 'light'
+ 'formation' 'fireball' 'light' 'light' 'other' 'unknown' 'light'
+ 'triangle' 'fireball' 'triangle' 'triangle' 'flash' 'circle' 'triangle'
+ 'disk' 'light' 'unknown' 'light' 'light' 'fireball' 'circle' 'unknown'
+ 'unknown' 'circle' 'disk' 'chevron' 'disk' 'disk' 'triangle' 'light'
+ 'light' 'disk'
+
+***Вывод:*** Наша модель дерева решений показала низкую точность предсказаний (Точность модели на тестовой выборке: 0.1377245508982036), что означает, что она не очень хорошо предсказывает форму НЛО на основе выбранных признаков (население и время). Из-за чего можно сделать вывод, что возможно, эти признаки недостаточно информативны или недостаточно связаны с формой НЛО.

kutygin_andre_lab_3/main.py

@@ -0,0 +1,39 @@
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+
+# Загрузка данных
+data = pd.read_csv('ufo_sighting_data.csv')
+
+# Выбор признаков
+features = [ 'length_of_encounter_seconds', 'latitude', 'longitude']
+target = 'UFO_shape'
+# Удаление строк содержащих NaN
+data.dropna(inplace=True)
+
+# Удаление столбцов содержащих NaN
+data.dropna(axis='columns', inplace=True)
+
+# Разделение данных на обучающую и тестовую выборки
+train_data, test_data, train_labels, test_labels = train_test_split(data[features], data[target], test_size=0.2, random_state=42)
+
+# Создание и обучение модели дерева решений
+model = DecisionTreeClassifier()
+model.fit(train_data, train_labels)
+
+# Прогнозирование на тестовой выборке
+predictions = model.predict(test_data)
+
+# Оценка точности модели
+accuracy = accuracy_score(test_labels, predictions)
+print('Точность модели на тестовой выборке:', accuracy)
+
+# Прогнозирование на оставшемся проценте данных
+remaining_data = data.drop(test_data.index)
+remaining_predictions = model.predict(remaining_data[features])
+
+# Вывод результатов
+print('Прогнозы по оставшемуся проценту данных:', remaining_predictions)
+
+# Сделайте необходимые выводы

lipatov_ilya_lab_5/README.md

@@ -0,0 +1,51 @@
+## Лабораторная работа №5
+
+### Регрессия
+
+## Выполнил студент группы ПИбд-41 Липатов Илья
+
+### Как запустить лабораторную работу:
+
+* установить python, numpy, matplotlib, sklearn
+* запустить проект (стартовая точка класс lab5)
+
+### Какие технологии использовались:
+
+* Язык программирования `Python`, библиотеки numpy, matplotlib, sklearn
+* Среда разработки `PyCharm`
+
+### Что делает лабораторная работа:
+
+* С помощью полиномиальной регрессии предсказывает среднюю стоимость домов в 1000 долларах [тыс. долларов] исходя из среднего количества комнат в жилом помещении, уровень преступности на душу населения в разбивке по городам и индекса доступности к радиальным магистралям.
+* Выводит размер ошибки, оценку модели и полученное предсказание 
+
+### Примеры работы:
+
+### Результаты:
+Были проведены тесты с различными параметрами степени (от 1 до 6). По итогу степень ошибки большая, меньше всего она при степени равной 2 или 4 (при этом и оценка модели 0.68 и 0.55 соответственно).
+
+#### Тесты
+
+#### degree = 1
+* Оценка качества: 0.4252542186083391
+* Ошибка: 0.22653604605972913
+
+#### degree = 2
+* Оценка качества: 0.6835376807930289
+* Ошибка: 0.1625504540569756
+
+#### degree = 3
+* Оценка качества: 0.5267438865953347
+* Ошибка: 0.195302452251188
+
+#### degree = 4
+* Оценка качества: 0.5481932964142193
+* Ошибка: 0.17852746450144702
+
+#### degree = 5
+* Оценка качества: -3.372087305867348
+* Ошибка: 0.4163026401028063
+
+#### degree = 6
+* Оценка качества: -69.05174526020205
+* Ошибка: 1.3125236408458876

lipatov_ilya_lab_5/boston.csv

@@ -0,0 +1,507 @@
+CRIM,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,B,LSTAT,MEDV
+0.00632,18.00,2.310,0,0.5380,6.5750,65.20,4.0900,1,296.0,15.30,396.90,4.98,24.00
+0.02731,0.00,7.070,0,0.4690,6.4210,78.90,4.9671,2,242.0,17.80,396.90,9.14,21.60
+0.02729,0.00,7.070,0,0.4690,7.1850,61.10,4.9671,2,242.0,17.80,392.83,4.03,34.70
+0.03237,0.00,2.180,0,0.4580,6.9980,45.80,6.0622,3,222.0,18.70,394.63,2.94,33.40
+0.06905,0.00,2.180,0,0.4580,7.1470,54.20,6.0622,3,222.0,18.70,396.90,5.33,36.20
+0.02985,0.00,2.180,0,0.4580,6.4300,58.70,6.0622,3,222.0,18.70,394.12,5.21,28.70
+0.08829,12.50,7.870,0,0.5240,6.0120,66.60,5.5605,5,311.0,15.20,395.60,12.43,22.90
+0.14455,12.50,7.870,0,0.5240,6.1720,96.10,5.9505,5,311.0,15.20,396.90,19.15,27.10
+0.21124,12.50,7.870,0,0.5240,5.6310,100.00,6.0821,5,311.0,15.20,386.63,29.93,16.50
+0.17004,12.50,7.870,0,0.5240,6.0040,85.90,6.5921,5,311.0,15.20,386.71,17.10,18.90
+0.22489,12.50,7.870,0,0.5240,6.3770,94.30,6.3467,5,311.0,15.20,392.52,20.45,15.00
+0.11747,12.50,7.870,0,0.5240,6.0090,82.90,6.2267,5,311.0,15.20,396.90,13.27,18.90
+0.09378,12.50,7.870,0,0.5240,5.8890,39.00,5.4509,5,311.0,15.20,390.50,15.71,21.70
+0.62976,0.00,8.140,0,0.5380,5.9490,61.80,4.7075,4,307.0,21.00,396.90,8.26,20.40
+0.63796,0.00,8.140,0,0.5380,6.0960,84.50,4.4619,4,307.0,21.00,380.02,10.26,18.20
+0.62739,0.00,8.140,0,0.5380,5.8340,56.50,4.4986,4,307.0,21.00,395.62,8.47,19.90
+1.05393,0.00,8.140,0,0.5380,5.9350,29.30,4.4986,4,307.0,21.00,386.85,6.58,23.10
+0.78420,0.00,8.140,0,0.5380,5.9900,81.70,4.2579,4,307.0,21.00,386.75,14.67,17.50
+0.80271,0.00,8.140,0,0.5380,5.4560,36.60,3.7965,4,307.0,21.00,288.99,11.69,20.20
+0.72580,0.00,8.140,0,0.5380,5.7270,69.50,3.7965,4,307.0,21.00,390.95,11.28,18.20
+1.25179,0.00,8.140,0,0.5380,5.5700,98.10,3.7979,4,307.0,21.00,376.57,21.02,13.60
+0.85204,0.00,8.140,0,0.5380,5.9650,89.20,4.0123,4,307.0,21.00,392.53,13.83,19.60
+1.23247,0.00,8.140,0,0.5380,6.1420,91.70,3.9769,4,307.0,21.00,396.90,18.72,15.20
+0.98843,0.00,8.140,0,0.5380,5.8130,100.00,4.0952,4,307.0,21.00,394.54,19.88,14.50
+0.75026,0.00,8.140,0,0.5380,5.9240,94.10,4.3996,4,307.0,21.00,394.33,16.30,15.60
+0.84054,0.00,8.140,0,0.5380,5.5990,85.70,4.4546,4,307.0,21.00,303.42,16.51,13.90
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lipatov_ilya_lab_5/lab5.py

@@ -0,0 +1,20 @@
+from sklearn.metrics import mean_absolute_percentage_error
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.linear_model import LinearRegression
+from sklearn.pipeline import Pipeline
+import pandas as pd
+
+data = pd.read_csv('boston.csv')
+X = (data[['CRIM', 'RM', 'RAD']])
+y = data['MEDV']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+lin = LinearRegression()
+polynomial_features = PolynomialFeatures(degree=1)
+pipeline = Pipeline([("Linear", polynomial_features), ("linear_regression", lin)])
+pipeline.fit(X_train, y_train)
+y_predict = lin.predict(polynomial_features.fit_transform(X_test))
+print('Предсказание: ', y_predict)
+print('Оценка качества:', pipeline.score(X_test, y_test))
+print('Ошибка:', mean_absolute_percentage_error(y_test, y_predict))

madyshev_egor_lab_5/main.py

@@ -0,0 +1,57 @@
+import numpy as np
+import pandas as pb
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression, Perceptron, LogisticRegression, Lasso, Ridge
+from sklearn.neural_network import MLPClassifier, MLPRegressor
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder, MinMaxScaler
+from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier
+from sklearn.preprocessing import PolynomialFeatures
+
+df = pb.read_csv("StudentsPerformance.csv", sep=",", encoding="windows-1251")
+df1 = df
+print("Данные без подготовки:")
+with pb.option_context('display.max_rows', None, 'display.max_columns', None, 'display.width', 1000):
+    print(df[:5])
+
+def prepareStringData(columnName):
+    uniq = df[columnName].unique()
+    mp = {}
+    for i in uniq:
+        mp[i] = len(mp)
+    df[columnName] = df[columnName].map(mp)
+
+
+print()
+print("Данные после подготовки:")
+prepareStringData("gender")
+prepareStringData("race/ethnicity")
+prepareStringData("parental level of education")
+prepareStringData("lunch")
+prepareStringData("test preparation course")
+with pb.option_context('display.max_rows', None, 'display.max_columns', None, 'display.width', 1000):
+    print(df[:5])
+
+
+
+X = df[["gender", "race/ethnicity", "lunch", "test preparation course", "parental level of education", "reading score", "writing score"]]
+y = df["math score"]
+X_train, X_Test, y_train, y_test = train_test_split(X, y, test_size=0.26, random_state=42)
+lnr = LinearRegression()
+lnr = lnr.fit(X_train,y_train)
+
+poly_regression = make_pipeline(PolynomialFeatures(degree=4), LinearRegression())
+poly_regression.fit(X_train, y_train)
+
+lasso = Lasso()
+lasso.fit(X_train, y_train)
+
+ridge = Ridge()
+ridge.fit(X_train, y_train)
+
+
+print("Линейная регрессия: ", lnr.score(X_Test,y_test))
+print("Полиномиальная регрессия: ", poly_regression.score(X_Test,y_test))
+print("Лассо-регрессия: ", lasso.score(X_Test,y_test))
+print("Гребневая регрессия: ", ridge.score(X_Test,y_test))

madyshev_egor_lab_5/readme.md

@@ -0,0 +1,41 @@
+# Задание
+Использовать регрессию по варианту для данных из таблицы 1 по варианту (таблица 10), самостоятельно сформулировав задачу. Оценить, насколько хорошо она подходит для решения сформулированной вами задачи.    
+## Задание по варианту
+Полиномиальная регрессия
+## Решение
+### Запуск программы
+Для запуска программы необходимо запустить файл main.py, содержащий код программы  
+### Используемые технологии
+Программа использует следующие библиотеки:  
+- numpy - библиотека для работы с массивами и матрицами.
+- matplotlib - библиотека для создания графиков и визуализации данных.
+- sklearn - библиотека для машинного обучения и анализа данных.
+### Что делает программа
+Программа читает данные из csv файла. Подготавливает их для работы модели, приводя текстовые параметры к числам.  И пытается научиться предсказывать оценку по математике на основании остальных данных с помощью различных моделей.
+### Тесты
+Данные без подготовки:  
+   gender race/ethnicity parental level of education         lunch test preparation course  math score  reading score  writing score  
+0  female        group B           bachelor's degree      standard                    none          72             72             74  
+1  female        group C                some college      standard               completed          69             90             88  
+2  female        group B             master's degree      standard                    none          90             95             93  
+3    male        group A          associate's degree  free/reduced                    none          47             57             44  
+4    male        group C                some college      standard                    none          76             78             75  
+  
+Данные после подготовки:  
+   gender  race/ethnicity  parental level of education  lunch  test preparation course  math score  reading score  writing score  
+0       0               0                            0      0                        0          72             72             74  
+1       0               1                            1      0                        1          69             90             88  
+2       0               0                            2      0                        0          90             95             93  
+3       1               2                            3      1                        0          47             57             44  
+4       1               1                            1      0                        0          76             78             75  
+  
+Линейная регрессия:  0.8769480272687482  
+Полиномиальная регрессия:  0.736490555768213  
+Лассо-регрессия:  0.8299946331354273  
+Гребневая регрессия:  0.8768384994076267  
+
+Логическая регрессия не подошла так как требует чтобы переменная ответа была двоичной.
+Из результатов четырех моделей видно, что для решения задачи предсказания оценки по математике неплохо подходит модель Линейной регрессии.  
+Модель гребневой регрессии имеет схожие результаты. Далее идет лассо, и хуже всех полиномиальная регрессия.  
+  
+Вывод: Для решения задачи предсказания результатов экзамена по математике неплохо подходят линейные модели, а именно линейная регрессия и гребневая регрессия

madyshev_egor_lab_6/main.py

@@ -0,0 +1,49 @@
+import numpy as np
+import pandas as pb
+import matplotlib.pyplot as plt
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression, Perceptron, LogisticRegression, Lasso, Ridge
+from sklearn.neural_network import MLPClassifier, MLPRegressor
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder, MinMaxScaler
+from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier
+from sklearn.preprocessing import PolynomialFeatures
+
+df = pb.read_csv("StudentsPerformance.csv", sep=",", encoding="windows-1251")
+df1 = df
+print("Данные без подготовки:")
+with pb.option_context('display.max_rows', None, 'display.max_columns', None, 'display.width', 1000):
+    print(df[:5])
+
+def prepareStringData(columnName):
+    uniq = df[columnName].unique()
+    mp = {}
+    for i in uniq:
+        mp[i] = len(mp)
+    df[columnName] = df[columnName].map(mp)
+
+
+print()
+print("Данные после подготовки:")
+prepareStringData("gender")
+prepareStringData("race/ethnicity")
+prepareStringData("parental level of education")
+prepareStringData("lunch")
+prepareStringData("test preparation course")
+with pb.option_context('display.max_rows', None, 'display.max_columns', None, 'display.width', 1000):
+    print(df[:5])
+
+
+
+X = df[["gender", "race/ethnicity", "lunch", "parental level of education", "reading score", "writing score", "math score"]]
+y = df["test preparation course"]
+X_train, X_Test, y_train, y_test = train_test_split(X, y, test_size=0.26, random_state=42)
+
+mlpr = MLPRegressor()
+mlpc = MLPClassifier()
+mlpr.fit(X_train, y_train)
+mlpc.fit(X_train, y_train)
+
+print("MLPRegressor:", mlpr.score(X_Test, y_test))
+print("MLPClassifier:", mlpc.score(X_Test, y_test))

madyshev_egor_lab_6/readme.md

@@ -0,0 +1,38 @@
+# Задание
+Использовать нейронную сеть (четные варианты – MLPRegressor, нечетные – MLPClassifier) для данных из таблицы 1 по варианту, самостоятельно сформулировав задачу. Интерпретировать результаты и оценить, насколько хорошо она подходит для решения сформулированной вами задачи.    
+## Задание по варианту
+MLPRegressor
+## Решение
+### Запуск программы
+Для запуска программы необходимо запустить файл main.py, содержащий код программы  
+### Используемые технологии
+Программа использует следующие библиотеки:  
+- numpy - библиотека для работы с массивами и матрицами.
+- matplotlib - библиотека для создания графиков и визуализации данных.
+- sklearn - библиотека для машинного обучения и анализа данных.
+### Что делает программа
+Программа читает данные из csv файла. Подготавливает их для работы модели, приводя текстовые параметры к числам.  И пытается научиться предсказывать прохождение подготовительных курсов с помощью моделей нейронных сетей.
+### Тесты
+Данные без подготовки:  
+   gender race/ethnicity parental level of education         lunch test preparation course  math score  reading score  writing score  
+0  female        group B           bachelor's degree      standard                    none          72             72             74  
+1  female        group C                some college      standard               completed          69             90             88  
+2  female        group B             master's degree      standard                    none          90             95             93  
+3    male        group A          associate's degree  free/reduced                    none          47             57             44  
+4    male        group C                some college      standard                    none          76             78             75  
+  
+Данные после подготовки:  
+   gender  race/ethnicity  parental level of education  lunch  test preparation course  math score  reading score  writing score  
+0       0               0                            0      0                        0          72             72             74  
+1       0               1                            1      0                        1          69             90             88  
+2       0               0                            2      0                        0          90             95             93  
+3       1               2                            3      1                        0          47             57             44  
+4       1               1                            1      0                        0          76             78             75  
+  
+MLPRegressor: 0.1347847602324338  
+MLPClassifier: 0.65  
+
+Модель регрессии показала себя хуже чем модель классификации. Хотя модель классификации показала себя чуть лучше, результаты её работы всё равно не очень высоки.  
+Итоговый результат лежит в границах между 0 и 1, и в тестовых результатах является целым. Это значит, что угадывая произвольно модель в любом случае может достигнуть точности близкой к 0.5  
+
+Вывод:  Модели нейронных сетей MLPRegressor и MLPClassifier не подходят для решения поставленной задачи, предсказания прохождения курсов по остальным данным. Или на практике не существует соответствующей зависимости в данных.

madyshev_egor_lab_7/main.py

@@ -0,0 +1,65 @@
+import numpy as np
+from keras.preprocessing.text import Tokenizer
+from keras.preprocessing.sequence import pad_sequences
+from keras.models import Sequential
+from keras.layers import LSTM, Dense, Embedding
+
+# Чтение текста из файла
+with open('mumu.txt', 'r', encoding='utf-8') as file:
+    text = file.read()
+
+# Параметры модели
+seq_length = 50  # Длина входных последовательностей
+num_epochs = 50
+gen_length = 200  # Длина генерируемого текста
+seed_text = "Начнем с этого"  # Начальная фраза для генерации
+
+# Создание экземпляра Tokenizer и обучение на тексте
+tokenizer = Tokenizer()
+tokenizer.fit_on_texts([text])
+vocab_size = len(tokenizer.word_index) + 1  # Размер словаря
+
+# Преобразование текста в последовательности чисел
+sequences = tokenizer.texts_to_sequences([text])[0]
+
+# Создание входных и выходных последовательностей
+X_data = []
+y_data = []
+for i in range(seq_length, len(sequences)):
+    sequence = sequences[i - seq_length:i]
+    target = sequences[i]
+    X_data.append(sequence)
+    y_data.append(target)
+
+X = np.array(X_data)
+y = np.array(y_data)
+
+# Создание модели RNN
+model = Sequential()
+model.add(Embedding(input_dim=vocab_size, output_dim=128, input_length=seq_length))
+model.add(LSTM(256, return_sequences=True))
+model.add(LSTM(256))
+model.add(Dense(vocab_size, activation='softmax'))
+
+# Компиляция модели
+model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+# Обучение модели
+model.fit(X, y, epochs=num_epochs, batch_size=64, verbose=1)
+
+# Функция для генерации текста
+def generate_text(seed_text, gen_length):
+    generated_text = seed_text
+    for _ in range(gen_length):
+        sequence = tokenizer.texts_to_sequences([seed_text])[0]
+        sequence = pad_sequences([sequence], maxlen=seq_length)
+        prediction = model.predict(sequence, verbose=0)
+        predicted_index = np.argmax(prediction)
+        predicted_word = [word for word, index in tokenizer.word_index.items() if index == predicted_index][0]
+        generated_text += " " + predicted_word
+        seed_text += " " + predicted_word
+    return generated_text
+
+# Генерация текста
+generated_text = generate_text(seed_text, gen_length)
+print(generated_text)

madyshev_egor_lab_7/mumu.txt

@@ -0,0 +1,13 @@
+Артем поежился, представляя себе туннель за семисотым метром. Страшно было даже помыслить о том, чтобы показаться там. За семисотый метр на север не отваживался ходить никто. Патрули доезжали до пятисотого и, осветив пограничный столб прожектором с дрезины, убедившись, что никакая дрянь не переползла за него, торопливо возвращались. Разведчики, здоровые мужики, бывшие морские пехотинцы, и те останавливались на шестьсот восьмидесятом, прятали горящие сигареты в ладонях и замирали, прильнув к приборам ночного видения. А потом медленно, тихо отходили назад, не спуская глаз с туннеля и ни в коем случае не поворачиваясь к нему спиной.
+
+Дозор, в котором они сейчас стояли, находился на четыреста пятидесятом, в пятидесяти метрах от пограничного столба. Но граница проверялась раз в день, и осмотр закончился уже несколько часов назад. Теперь их пост был крайним, а за часы, прошедшие со времени последней проверки, твари, которых патруль мог спугнуть, наверняка снова начали подползать. Тянуло их на огонек, поближе к людям…
+
+Артем уселся на свое место и спросил:
+
+– А что там с Полежаевской случилось?
+
+И хотя он уже знал эту леденящую кровь историю – рассказывали челноки на станции, его тянуло послушать ее еще раз, как неудержимо тянет детей на страшные байки о безголовых мутантах и упырях, похищающих младенцев.
+– С Полежаевской? А ты не слышал? Странная история с ними вышла. Странная и страшная. Сначала у них разведчики стали пропадать. Уходили в туннели и не возвращались. У них, правда, салаги разведчики, не то что наши, но у них ведь и станция поменьше, и народу там не столько живет… Жило. Так вот, стали, значит, у них пропадать разведчики. Один отряд ушел – и нет его. Сначала думали, задержало его что-то, у них там еще туннель петляет, совсем как у нас, – Артему стало не по себе при этих словах, – и ни дозорам, ни тем более со станции ничего не видно, сколько ни свети. Нет их и нет, полчаса нет, час нет, два нет. Казалось бы, где там пропасть – всего ведь на километр уходили, им запретили дальше идти, да они и сами не дураки… В общем, так и не дождались, послали усиленный дозор, те искали, искали, кричали, кричали – все зря. Нету. Пропали разведчики. И ладно еще, что никто не видел, что с ними случилось. Плохо, что слышно ничего не было… Ни звука. И следов никаких.
+
+Артем уже начал жалеть, что попросил Петра Андреевича рассказать о Полежаевской. Тот был то ли лучше осведомлен, то ли сам что-то додумывал, только рассказывал он такие подробности, какие и не снились челнокам, уж на что те были мастера и любители рассказать байку. От подробностей этих мороз шел по коже и неуютно становилось даже у костра, а любые, пусть и совсем безобидные шорохи из туннеля будоражили воображение.
+– Ну, так вот. Стрельбы слышно не было, те и решили, что разведчики, наверное, ушли от них – недовольны, может, чем-то были и сбежали. Ну, и шут с ними. Хотят легкой жизни, хотят со всяким отребьем мотаться, с анархистами всякими, пусть себе мотаются. Так проще было думать. Спокойнее. А через неделю еще одна разведгруппа пропала. Те вообще не должны были дальше полукилометра от станции отходить. И опять та же история. Ни звука, ни следа. Как в воду канули. Тут на станции забеспокоились. Это уже непорядок, когда за неделю два отряда исчезают. С этим уже надо что-то делать. Меры, значит, принимать. Ну, они выставили на трехсотом кордон. Мешков с песком натаскали, пулемет установили, прожектор – по всем правилам фортификации. Послали на Беговую гонца – у них с Беговой и с Улицей 1905 года конфедерация. Раньше Октябрьское Поле тоже было с ними, но потом там что-то случилось, никто не знает точно что, авария какая-то: жить там стало нельзя, и оттуда все разбежались, ну, да это неважно. Послали они на Беговую гонца – предупредить, мол, творится что-то неладное, и о помощи попросить в случае чего. Не успел первый гонец до Беговой добраться, дня не прошло – те еще ответ обдумывали, – прибегает второй, весь в мыле, и рассказывает, что их усиленный кордон погиб поголовно, не сделав ни единого выстрела. Всех перерезали. И словно во сне зарезали – вот что страшно-то! А ведь они и не смогли бы заснуть после пережитого страха, не говоря уж о приказах и инструкциях. Тут на Беговой поняли, что, если ничего не сделать, скоро та же петрушка и у них начнется. Снарядили ударный отряд из ветеранов – около сотни человек, пулеметы, гранатометы… Времени, конечно, это заняло порядком, дня полтора, но все же отправили группу на помощь. А когда та вошла на Полежаевскую, там уже ни одной живой души не было. И тел не было – только кровь повсюду. Вот так вот. И черт знает, кто это сделал. Я вот не верю, что люди вообще на такое способны.

madyshev_egor_lab_7/readme.md

@@ -0,0 +1,17 @@
+# Задание
+Выбрать художественный текст (четные варианты – русскоязычный, нечетные – англоязычный) и обучить на нем рекуррентную нейронную сеть для решения задачи генерации. Подобрать архитектуру и параметры так, чтобы приблизиться к максимально осмысленному результату.    
+## Задание по варианту
+Русский язык
+## Решение
+### Запуск программы
+Для запуска программы необходимо запустить файл main.py, содержащий код программы  
+### Используемые технологии
+Программа использует следующие библиотеки:  
+- NumPy: Используется для работы с массивами и матрицами, особенно для обработки данных и их подготовки для обучения моделей глубокого обучения.
+- Keras: Используется для создания и обучения нейронных сетей. В коде представлены классы Tokenizer для обработки текста, Sequential для создания модели и различные слои, такие как LSTM, Dense и Embedding.
+### Что делает программа
+Программа читает текст из файла, обучается на нем, и генерирует новый текст.
+### Тесты
+Получившийся сгенерированный текст:
+   Начнем с этого прильнув к к приборам ночного видения а потом потом тихо отходили не спуская глаз и туннеля и ни в коем случае не поворачиваясь к нему спиной дозор в котором они сейчас стояли находился на четыреста пятидесятом 
+Тест на тексте на английском языке, показал что параметры модели не подходят. Сгенерированный текст представлял собой набор букв.

malkova_anastasia_lab_1/README.md

@@ -0,0 +1,44 @@
+# Лабораторная работа №1
+
+> Работа с типовыми наборами данных и различными моделями
+
+# Задание
+
+Сгенерировать определённый тип данных, сравнить на нём разные модели и отобразить качество на графиках.
+
+Данные: make_classification (n_samples=500, n_features=2, n_redundant=0, n_informative=2, random_state=rs, n_clusters_per_class=1) 
+Модели:
+* Линейную регрессию 
+* Персептрон 
+* Гребневую полиномиальную регрессию (со степенью 3, alpha= 1.0)
+
+### Как запустить лабораторную работу
+
+1. Установить python, numpy, sklearn, matplotlib
+2. Запустить команду `python main.py` в корне проекта
+
+### Использованные технологии
+
+* Язык программирования `python`
+* Библиотеки `numpy, sklearn, matplotlib`
+* Среда разработки `PyCharm`
+
+### Что делает программа?
+
+Генерирует набор данных для классификации с помощью make_classification.
+Обучает на них 3 модели:
+
+- Линейную регрессию
+- Персептрон
+- Гребневую полиномиальную регрессию (со степенью 3, alpha = 1.0)
+
+Собирает итоговые оценки моделей:
+
+- Линейная регрессия - коэффициент детерминации R2
+- Персептрон - средняя точность по заданным тестовым данным
+- Гребневая полиномиальная регрессия - Перекрёстная проверка
+
+![plots screen](plots.jpg)
+
+Лучший результат показала модель персептрона
+

malkova_anastasia_lab_1/dataset.py

@@ -0,0 +1,16 @@
+import numpy as np
+from sklearn.datasets import make_classification
+from sklearn.model_selection import train_test_split
+
+
+def generate_dataset():
+    x, y = make_classification(n_samples=500, n_features=2, n_redundant=0,
+                               n_informative=2, random_state=0, n_clusters_per_class=1)
+    random = np.random.RandomState(2)
+    x += 2.5 * random.uniform(size=x.shape)
+    return x, y
+
+
+def split_dataset(x, y):
+    return train_test_split(
+        x, y, test_size=.05, random_state=42)

malkova_anastasia_lab_1/main.py

@@ -0,0 +1,19 @@
+from dataset import generate_dataset, split_dataset
+from models import launch_linear_regression, launch_perceptron, launch_ridge_poly_regression
+from plots import show_plot
+
+x, y = generate_dataset()
+
+x_train, x_test, y_train, y_test = split_dataset(x, y)
+
+my_linear_model, linear_model_score = launch_linear_regression(
+    x_train, x_test, y_train, y_test)
+my_perceptron_model, perceptron_model_score = launch_perceptron(
+    x_train, x_test, y_train, y_test)
+my_polynomial_model, polynomial_model_score = launch_ridge_poly_regression(
+    x_train, x_test, y_train, y_test)
+
+show_plot(x, x_train, x_test, y_train, y_test,
+          my_linear_model, linear_model_score,
+          my_perceptron_model, perceptron_model_score,
+          my_polynomial_model, polynomial_model_score)

malkova_anastasia_lab_1/models.py

@@ -0,0 +1,37 @@
+from sklearn.linear_model import LinearRegression, Perceptron, Ridge
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.model_selection import cross_val_score
+from sklearn.pipeline import Pipeline
+
+
+def launch_linear_regression(x_train, x_test, y_train, y_test):
+    my_linear_model = LinearRegression()
+    my_linear_model.fit(x_train, y_train)
+    linear_model_score = my_linear_model.score(
+        x_test, y_test)
+    print('linear_model_score: ', linear_model_score)
+    return my_linear_model, linear_model_score
+
+
+# Perceptron
+def launch_perceptron(x_train, x_test, y_train, y_test):
+    my_perceptron_model = Perceptron()
+    my_perceptron_model.fit(x_train, y_train)
+    perceptron_model_score = my_perceptron_model.score(
+        x_test, y_test)
+    print('perceptron_model_score: ', perceptron_model_score)
+    return my_perceptron_model, perceptron_model_score
+
+
+# RidgePolyRegression
+def launch_ridge_poly_regression(x_train, x_test, y_train, y_test):
+    my_polynomial_model = PolynomialFeatures(degree=3, include_bias=False)
+    ridge = Ridge(alpha=1)
+    pipeline = Pipeline(
+        [("polynomial_features", my_polynomial_model), ("ridge_regression", ridge)])
+    pipeline.fit(x_train, y_train)
+    scores = cross_val_score(pipeline, x_test, y_test,
+                             scoring="neg_mean_squared_error", cv=5)
+    polynomial_model_score = -scores.mean()
+    print('mean polynomial_model_score: ', polynomial_model_score)
+    return my_polynomial_model, polynomial_model_score

malkova_anastasia_lab_1/plots.py

@@ -0,0 +1,71 @@
+import numpy as np
+from matplotlib.colors import ListedColormap
+from matplotlib.axes import Axes
+from matplotlib import pyplot as plt
+
+TRAIN_DATA_ROW_LENGTH = 3
+TEST_DATA_ROW_LENGTH = 6
+LINEAR_REGRESSION_PLOT_INDEX = 6
+PERCEPTRON_REGRESSION_PLOT_INDEX = 7
+RIDGE_POLY_REGRESSION_REGRESSION_PLOT_INDEX = 8
+
+
+def show_plot(x, x_train, x_test, y_train, y_test, my_linear_model, linear_model_score, my_perceptron_model, perceptron_model_score, pipeline, polynomial_model_score):
+    h = .02  # шаг регулярной сетки
+    x0_min, x0_max = x[:, 0].min() - .5, x[:, 0].max() + .5
+    x1_min, x1_max = x[:, 1].min() - .5, x[:, 1].max() + .5
+    xx0, xx1 = np.meshgrid(np.arange(x0_min, x0_max, h),
+                           np.arange(x1_min, x1_max, h))
+    cm = plt.cm.RdBu
+
+    cm_bright = ListedColormap(['#FF0000', '#0000FF'])
+
+    for i in range(9):
+        current_subplot = plt.subplot(3, 3, i+1)
+        if i < TRAIN_DATA_ROW_LENGTH:
+            current_subplot.scatter(
+                x_train[:, 0], x_train[:, 1], c=y_train, cmap=cm_bright)
+        elif i < TEST_DATA_ROW_LENGTH:
+            current_subplot.scatter(
+                x_test[:, 0], x_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
+        else:
+            if i == LINEAR_REGRESSION_PLOT_INDEX:
+                show_gradient(my_linear_model, current_subplot=current_subplot,
+                              title='LinearRegression', score=linear_model_score, xx0=xx0, xx1=xx1, cm=cm)
+
+            elif i == PERCEPTRON_REGRESSION_PLOT_INDEX:
+                show_gradient(my_perceptron_model, current_subplot=current_subplot,
+                              title='Perceptron', score=perceptron_model_score, xx0=xx0, xx1=xx1, cm=cm)
+
+            elif i == RIDGE_POLY_REGRESSION_REGRESSION_PLOT_INDEX:
+                current_subplot.set_title('RidgePolyRegression')
+                show_gradient(pipeline, current_subplot=current_subplot,
+                              title='RidgePolyRegression', score=polynomial_model_score, xx0=xx0, xx1=xx1, cm=cm)
+
+            current_subplot.scatter(
+                x_train[:, 0], x_train[:, 1], c=y_train, cmap=cm_bright)
+            current_subplot.scatter(
+                x_test[:, 0], x_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
+
+    plt.show()
+
+
+def show_gradient(model, current_subplot: Axes, title: str, score: float, xx0, xx1, cm):
+    current_subplot.set_title(title)
+    if hasattr(model, "decision_function"):
+        Z = model.decision_function(np.c_[xx0.ravel(), xx1.ravel()])
+    elif hasattr(model, "predict_proba"):
+        Z = model.predict_proba(np.c_[xx0.ravel(), xx1.ravel()])[:, 1]
+    elif hasattr(model, "predict"):
+        Z = model.predict(np.c_[xx0.ravel(), xx1.ravel()])
+    else:
+        return
+
+    Z = Z.reshape(xx0.shape)
+    current_subplot.contourf(xx0, xx1, Z, cmap=cm, alpha=.8)
+    current_subplot.set_xlim(xx0.min(), xx0.max())
+    current_subplot.set_ylim(xx0.min(), xx1.max())
+    current_subplot.set_xticks(())
+    current_subplot.set_yticks(())
+    current_subplot.text(xx0.max() - .3, xx1.min() + .3, ('%.2f' % score),
+                         size=15, horizontalalignment='left')

orlov_artem_lab_7/app.py

@@ -0,0 +1,84 @@
+import numpy as np
+import tensorflow as tf
+from keras.models import Sequential
+from keras.layers import Embedding, LSTM, Dense
+from flask import Flask, request, jsonify, render_template
+
+# Загрузка и предобработка данных
+with open('your_text_file.txt', 'r', encoding='utf-8') as file:
+    text = file.read()
+
+# Создание словаря символов
+chars = sorted(list(set(text)))
+char_to_idx = {char: idx for idx, char in enumerate(chars)}
+idx_to_char = {idx: char for idx, char in enumerate(chars)}
+
+# Подготовка данных для обучения
+seq_length = 100
+step = 3
+sequences = []
+next_chars = []
+
+for i in range(0, len(text) - seq_length, step):
+    seq = text[i:i+seq_length]
+    target = text[i+seq_length]
+    sequences.append(seq)
+    next_chars.append(target)
+
+# Преобразование данных в числовой формат
+X = np.zeros((len(sequences), seq_length, len(chars)), dtype=bool)
+y = np.zeros((len(sequences), len(chars)), dtype=bool)
+
+for i, seq in enumerate(sequences):
+    for t, char in enumerate(seq):
+        X[i, t, char_to_idx[char]] = 1
+    y[i, char_to_idx[next_chars[i]]] = 1
+
+# Построение более сложной модели
+model = Sequential([
+    LSTM(256, input_shape=(seq_length, len(chars)), return_sequences=True),
+    LSTM(256),
+    Dense(len(chars), activation='softmax')
+])
+
+model.compile(optimizer='adam', loss='categorical_crossentropy')
+
+# Увеличение количества эпох обучения
+model.fit(X, y, epochs=100, batch_size=128)
+
+# Функция для генерации текста с параметром температуры
+def generate_text(seed_text, model, length=100, temperature=1.0):
+    generated_text = seed_text
+    for _ in range(length):
+        x = np.zeros((1, seq_length, len(chars)))
+        for t, char in enumerate(seed_text):
+            x[0, t, char_to_idx[char]] = 1
+        preds = model.predict(x, verbose=0)[0]
+        preds = np.log(preds) / temperature
+        exp_preds = np.exp(preds)
+        preds = exp_preds / np.sum(exp_preds)
+        next_index = np.random.choice(len(chars), p=preds)
+        next_char = idx_to_char[next_index]
+        generated_text += next_char
+        seed_text = seed_text[1:] + next_char
+    return generated_text
+
+# Создание Flask-приложения
+app = Flask(__name__)
+
+# Эндпоинт для генерации текста
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+# Эндпоинт для генерации текста
+@app.route('/generate_text', methods=['POST'])
+def generate_text_endpoint():
+    data = request.get_json()
+    seed_text = data.get('seed_text', '')
+    generated_text = generate_text(seed_text, model)
+    return jsonify({'generated_text': generated_text})
+
+# Запуск Flask-сервера
+if __name__ == '__main__':
+    app.run(port=5000)

orlov_artem_lab_7/readme.md

@@ -0,0 +1,29 @@
+Общее задание:
+Выбрать художественный текст (четные варианты – русскоязычный, нечетные – англоязычный) и обучить на нем рекуррентную нейронную сеть
+для решения задачи генерации. Подобрать архитектуру и параметры так,чтобы приблизиться к максимально осмысленному результату. Далее
+разбиться на пары четный-нечетный вариант, обменяться разработанными сетями и проверить, как архитектура товарища справляется с вашим текстом. В завершении подобрать компромиссную архитектуру, справляющуюся достаточно хорошо с обоими видами текстов. 
+
+Задание по вариантам:
+нечетный вариант, художественным текстом был выбран фрагмент произведения "The Adventures of Tom Sawyer"
+
+Запуск приложения: запуск файла app.py
+
+Использованные технологии:
+
+Python: Язык программирования, на котором написан код.
+TensorFlow и Keras: Фреймворки для создания и обучения нейронных сетей.
+Flask: Фреймворк для создания веб-сервисов на языке Python.
+
+Описание работы программы:
+
+Программа обучает рекуррентную нейронную сеть (RNN) на художественном англоязычный тексте из файла.
+Веб-сервис Flask создается для обработки запросов на генерацию текста на основе введенного начального текста.
+Пользователь вводит начальный текст в веб-интерфейсе, нажимает кнопку "Generate Text".
+Введенный текст передается через HTTP POST-запрос на сервер Flask.
+Сервер использует обученную RNN-модель для генерации продолжения текста и возвращает результат обратно в веб-интерфейс.
+
+Пример входных данных:
+Adventures
+
+Пример выходных данных:
+Adventures ti;ir ocsvvvy eslv;:ev, n nshe gv'ekvpr'g ey'vlat.tr-v:l eivee s ekgg d seovnve., ts kln

orlov_artem_lab_7/templates/index.html

@@ -0,0 +1,46 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>RNN Text Generation</title>
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            text-align: center;
+            margin: 50px;
+        }
+        #output {
+            margin-top: 20px;
+        }
+    </style>
+</head>
+<body>
+    <h1>RNN Text Generation</h1>
+    <label for="seed_text">Seed Text:</label>
+    <input type="text" id="seed_text" placeholder="Enter your seed text">
+    <button onclick="generateText()">Generate Text</button>
+    <div id="output"></div>
+
+    <script>
+        function generateText() {
+            const seedText = document.getElementById("seed_text").value;
+
+            fetch('http://127.0.0.1:5000/generate_text', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                },
+                body: JSON.stringify({ seed_text: seedText }),
+            })
+            .then(response => response.json())
+            .then(data => {
+                document.getElementById("output").innerText = data.generated_text;
+            })
+            .catch(error => {
+                console.error('Error:', error);
+            });
+        }
+    </script>
+</body>
+</html>

orlov_artem_lab_7/your_text_file.txt

@@ -0,0 +1,646 @@
+The Adventures of
+Tom Sawyer
+MARK TWAIN
+Level 1
+Retold by Jacqueline Kehl
+Series Editors: Andy Hopkins and Jocelyn Potter
+Pearson Education Limited
+Edinburgh Gate, Harlow,
+Essex CM20 2JE, England
+and Associated Companies throughout the world.
+ISBN 0 582 41923 9
+First published 1876
+Published by Puffin Books 1950
+This edition first published 2000
+Copyright © Penguin Books 2000
+Typeset by Digital Type, London
+Set in 12/14ptBembo
+Printed in Spain by Mateu Cromo, S. A. Pinto (Madrid)
+All rights reserved; no part of this publication may be reproduced, stored
+in a retrieval system, or transmitted in any form or by any means,
+electronic, mechanical, photocopying, recording or otherwise, without the
+prior written permission of the Publishers.
+Published by Pearson Education Limited in association with Penguin Books Ltd, both
+companies being subsidiaries of Pearson Plc
+For a complete list of the titles available in the Penguin Readers series please write to your
+local Pearson Education office or to: Marketing Department, Penguin Longman Publishing,
+5 Bentinck Street, London W1M 5R.N.
+Introduction
+One Saturday afternoon Tom wanted to have an adventure
+because he didn’t want to think about Injun Joe. He went
+to Huck and said, “I’m going to look for treasure. Do you
+want to come with me?”
+Tom Sawyer loves adventures. He has a lot of adventures
+at home, at school, and with his friends. He has one
+adventure in a cave. But why is he there? What does he
+see in the cave? And why is he afraid?
+Mark Twain (1835-1910) is a famous American writer.
+His name was Samuel Clemens. Young Samuel lived in
+Hannibal, Missouri, a small town on the Mississippi River.
+He loved the river and he liked watching the big boats
+on it.
+Samuel loved adventures. He worked on boats on the
+Mississippi River for two years. Then he went to Nevada.
+He looked for treasure, but he didn’t find it. He worked for
+a newspaper there. His stories were in the name of Mark
+Twain, and people loved them.
+Later, Samuel lived in New York. His book The
+Adventures of Tom Sawyer (1876) is about a young boy in
+a small town in the 1800s. Huck Finn is his friend. The
+Adventures of Huckleberry Finn (1884) is about Huck’s
+adventures. These two books are very famous.
+Today, many people visit Hannibal because they want to
+see Mark Twain’s home and the Mark Twain Cave, the
+cave in The Adventures of Tom Sawyer.
+iii
+Chapter 1 The Fence
+Tom Sawyer lived with his aunt because his mother and
+father were dead. Tom didn’t like going to school, and he
+didn’t like working. He liked playing and having
+adventures. One Friday, he didn’t go to school—he went
+to the river.
+Aunt Polly was angry. “You’re a bad boy!” she said.
+“Tomorrow you can’t play with your friends because you
+didn’t go to school today. Tomorrow you’re going to work
+for me. You can paint the fence.”
+Saturday morning, Tom was not happy, but he started to
+paint the fence. His friend Jim was in the street.
+Tom asked him, “Do you want to paint?”
+Jim said, “No, I can’t. I’m going to get water.”
+Then Ben came to Tom’s house. He watched Tom and
+said, “I’m going to swim today. You can’t swim because
+you’re working.”
+Tom said, “This isn’t work. I like painting.”
+“Can I paint, too?” Ben asked.
+“No, you can’t,” Tom answered. “Aunt Polly asked me
+because I’m a very good painter.”
+Ben said, “I’m a good painter, too. Please, can I paint? I
+have some fruit. Do you want it?”
+OK,” Tom said. “Give me the fruit. Then you can paint.”
+Ben started to paint the fence. Later, many boys came to
+Tom’s house. They watched Ben, and they wanted to
+paint, too.
+Tom said, “Give me some food and you can paint.”
+1
+Tom stayed in the yard, and the boys painted.
+Tom stayed in the yard, and the boys painted. They
+painted the fence three times. It was beautiful and white.
+Tom went into the house. “Aunt Polly, can I play now?”
+he asked.
+Aunt Polly was surprised. “Did you paint the fence?”
+she asked.
+“Yes, I did,” Tom answered.
+Aunt Polly went to the yard and looked at the fence. She
+was very surprised and very happy. “It’s beautiful!” she
+said. “Yes, you can play now.”
+Tom walked to his friend Joe Harper’s house and played
+with his friends there. Then he walked home again. There
+was a new girl in one yard. She had yellow hair and blue
+eyes. She was beautiful. Tom wanted to talk to her, but she
+didn’t see him. She went into her house. Tom waited, but
+she didn’t come out again.
+Chapter 2 In the Graveyard
+One morning before school, Tom’s friend Huck Finn
+waited for him in the street. Huck didn’t have a home, and
+he never went to school. People in the town didn’t like
+him. But Tom liked Huck.
+Huck said, “Let’s have an adventure.”
+“What can we do on our adventure?” Tom asked.
+“Let’s go to the graveyard at night—at twelve o’clock!”
+Huck answered.
+‘That’s a good adventure,” Tom said. “Let’s meet at
+eleven o’clock.”
+Then Tom went to school, but he was late. The teacher
+was angry. He asked, “Why are you late again?”
+3
+“I’m late because I talked to Huck Finn,” Tom said.
+Then the teacher was very angry. “Sit with the girls,” he
+said to Tom.
+Tom sat near the beautiful new girl. He was happy. He
+looked at her.
+“What’s your name?” he asked.
+“Becky,” she answered.
+Tom smiled and said, “My name’s Tom.”
+The teacher was angry again. “Tom Sawyer, stop
+talking! Go to your place now,” he said. Tom went to his
+place.
+At twelve o’clock Tom and Becky didn’t go home. They
+stayed in the school yard and talked. Tom said, “I love
+you. Do you love me?”
+“Yes,” Becky answered.
+“Good,” Tom said. “Then you’re going to walk to
+school with me every day. Amy always walked with me.”
+“Amy!” Becky said angrily. “Do you love her?”
+“No,” Tom answered. “I love you now. Do you want to
+walk with me?”
+But Becky was angry with Tom. She walked away and
+didn’t answer. Tom was unhappy. He didn’t go to school
+in the afternoon.
+That night Tom went to bed at nine o’clock, but he
+didn’t sleep. At eleven o’clock he went out his bedroom
+window to the yard. Huck was there. They walked to the
+graveyard. They stopped behind some big trees and talked
+quietly.
+Suddenly, there was a noise. Three men came into the
+graveyard—the doctor, Muff Potter, and Injun Joe. Injun
+Joe and the doctor talked angrily. Then Injun Joe 
+Then Injun Joe killed the doctor with a knife.
+killed the doctor with a knife. Tom and Huck watched.
+Then they went away quickly because they were afraid.
+They went to Tom’s yard. Huck said, “We can’t talk
+about this. Injun Joe can find us and kill us, too.”
+“That’s right,” Tom said. “We can’t talk about it.”
+Tom went in his bedroom window. He went to bed, but
+he didn’t sleep well. Tom and Huck didn’t talk to their
+friends or Aunt Polly about that night because they were
+afraid of Injun Joe.
+Later, some men went to Muff Potter and said, “You’re
+a bad man. You killed the doctor.”
+Chapter 3 A Bad Day
+Becky was sick and didn’t go to school for many days.
+Tom was very sad. One morning, he said to Aunt Polly,
+“I’m very sick, and I want to stay home from school.”
+Aunt Polly said, “Here’s some medicine. Take this and
+you can get well quickly.”
+But Tom didn’t like the medicine. Peter, the cat, came
+into the room and looked at Tom.
+“Peter!” Tom said. ”Have some medicine!”
+Peter had some medicine. He didn’t like it! He went
+quickly out the open window and into the yard.
+Aunt Polly watched Peter. “Why did you do that, Tom?”
+she asked angrily. “You’re a very bad boy! Go to school
+now.”
+Tom arrived at school early and he waited for Becky at
+the school fence. She arrived early, too, but she didn’t
+6
+Peter had some medicine. He didn’t like it!
+look at Tom. She went into school. Tom walked away. He
+didn’t want to go to school now. He was very sad.
+Joe Harper was near the school. He was sad, too,
+because his mother was angry with him. The two boys
+walked and talked.
+Tom said, “Let’s run away.”
+“Yes, let’s!” Joe said.
+The two boys went to the river. Huck Finn was there.
+Tom and Joe said, “We’re going to run away. Do you
+want to come with us?”
+“Yes,” Huck answered. “Let’s go across the river. We
+can have a good adventure there.”
+The boys went home because they wanted to get food
+for their adventure.
+Chapter 4 Across the River
+Tom, Joe, and Huck went to the river. There was a small
+boat there. The boys went across the river in the small
+boat. They said, “This is a good place because we can play
+all day. There’s no school here.”
+They played and then went to sleep.
+In the morning, the boys were happy again. They said,
+“Let’s stay here for a long time.”
+In the afternoon, they played near the river again.
+Suddenly, there was a noise from a big boat on the river.
+The boys stopped playing and watched the boat.
+“Listen,” Tom said. “The men on the boat are talking
+about us.”
+8
+The boys stopped playing and watched the boat.
+The boys listened quietly. A man said, “The boys are in
+the river. They’re dead.”
+Tom said, “Those men are looking for us in the river.
+We’re here, but they don’t know that.”
+That night, the boys were sad. Huck and Joe went to
+sleep, but Tom didn’t sleep. He went home in the small
+boat. He quietly went in his bedroom window. Then he
+went under his bed and stayed there.
+Aunt Polly and her friends came into his room. Aunt
+Polly said to her friends, “Tom was a good boy, and I
+loved him. Now he’s dead, and I’m very sad.”
+Tom wanted to say, “I’m not dead.” But he stayed quiet.
+Aunt Polly went to sleep. Tom went out the window
+very quietly and went back across the river.
+In the morning, Joe and Huck said, “We’re not happy
+here now. We want to go home.”
+Tom said, “Let’s go home on Sunday. We can go to
+church. People are going to be very surprised!”
+Sunday morning, many children were at church. They
+talked about the three boys. They were sad because their
+friends were dead. Becky was sad, too.
+Suddenly, the three boys walked into the church. People
+were very surprised, but they were very happy, too.
+Chapter 5 At School
+Monday morning, Tom went to school. The children
+wanted to hear about his adventure, and Tom liked
+10
+talking about it. Becky wanted to talk to Tom, but he
+didn’t look at her.
+Then Tom talked to Amy. Becky watched him and she
+was angry. She said to her friends, “I’m going to have an
+adventure day. You can come on my adventure.” But she
+didn’t ask Tom.
+Later in the morning, Tom talked to Amy again. Becky
+talked to her friend Alfred and looked at a picture-book
+with him. Tom watched them and he was angry with
+Becky.
+In the afternoon, Tom waited for Becky at the school
+fence. He said, “I’m sorry.”
+But Becky didn’t listen to him. She walked into the
+school room. The teacher’s new book was on his table.
+This book wasn’t for children, but Becky wanted to look
+at it. She opened the book quietly and looked at the
+pictures.
+Suddenly, Tom came into the room. Becky was
+surprised. She closed the book quickly, and it tore. Becky
+was angry with Tom and quickly went out of the room.
+Then the children and the teacher came into the room
+and went to their places. The teacher looked at his book.
+“Who did this? Who tore my book?” he asked angrily.
+The room was very quiet. The teacher started to ask
+every child, “Did you do this?”
+They answered, “No, I didn’t.”
+Then he looked at Becky “Becky, did you do this?”
+11
+“I did it. I tore your book.”
+Tom wanted to help her. Suddenly he said, “I did it. I
+tore your book.”
+“Tom Sawyer, you’re a very bad boy. Stay here after
+school!” the teacher said angrily.
+At five o’clock Tom started to walk home. Becky waited
+for him at the school fence. “You’re a very good friend,”
+she said.
+Tom smiled at her and they walked home.
+Chapter 6 The Trial
+Summer vacation started, and Becky went away with her
+family. Tom was unhappy.
+Then Muff Potter’s trial started. Tom and Huck
+remembered the night in the graveyard. They were afraid
+of Injun Joe again.
+“Did you talk about the night in the graveyard?”
+Tom asked Huck.
+“No, I didn’t,” Huck answered. “Did you?”
+“No,” Tom answered. “But. I’m sorry about Muff Potter.
+He’s always friendly to us. He didn’t kill the doctor. I
+want to help him.”
+“Let’s take some food to him,” Huck said.
+The boys visited Muff Potter. “Here’s some food,” they
+said.
+Muff Potter said, “Thank you. You’re good boys.”
+Tom and Huck went to the trial and listened for two
+days. Tom didn’t sleep well at night because he wanted to
+help Muff Potter.
+On day three of the trial Tom talked.
+13
+A man asked him, “Where were you on the night of
+June 17th?”
+“I was in the graveyard,” Tom answered.
+“Did you see any people there?” the man asked:
+“Yes. Injun Joe, the doctor, and Muff Potter were there.
+They didn’t see me because I was behind some big trees.”
+“What did you see?” the man asked.
+“Injun Joe and the doctor talked angrily,” Tom
+answered. “Then Injun Joe killed the doctor with his knife.
+Muff Potter didn’t do it.”
+The people at the trial were surprised. Injun Joe quickly
+went out of the building.
+Tom and Huck were very afraid. Tom said, “Now Injun
+Joe knows about us. He can kill us, too.”
+Many people wanted to hear about the boys’ adventure
+in the graveyard. Tom liked talking about it. He was
+happy, too, because he helped Muff Potter. But he didn’t
+sleep well because he was afraid of Injun Joe.
+Chapter 7 Injun Joe’s Treasure
+One Saturday afternoon, Tom wanted to have an adventure
+because he didn’t want to think about Injun Joe. He went
+to Huck and said, “I’m going to look for treasure. Do you
+want to come with me?”
+ Huck always liked an adventure. “Oh, yes,” he said.
+“Where can we look?”
+
+14
+“Let’s start looking in the old house near Mrs. Douglas’s
+house. Old houses are good places for treasure,” Tom
+answered.
+The boys went to the old house. They wanted to look at
+every room. First they went into the kitchen, and then they
+went into the bedroom.
+Suddenly, two men came into the kitchen—Injun Joe
+and his friend. The boys were afraid and stayed in the
+bedroom very quietly.
+Injun Joe walked across the kitchen. “We can put our
+money here,” he said to his friend.
+He started to dig under the floor with his knife.
+“What’s this?” Injun Joe said. “I’m going to get it out.”
+There was a big box under the floor. He opened it with
+his knife. There was a lot of money in the box.
+“Look at that money!” his friend said. “Let’s go now.
+We can come back and get it tomorrow.”
+“No,” Injun Joe said. “We’re going to take it with us
+now. We can take it to that place. You know—the place
+under the cross.”
+Then the men went out of the house. Injun Joe talked
+quietly to his friend. The boys listened and were afraid.
+Tom said, “Did you hear that? He wants to kill us.”
+They went out of the house quietly and went home.
+The boys were afraid of Injun Joe, but they wanted to
+find his treasure. They watched his house every night, but
+they didn’t see Injun Joe or his treasure.
+15
+There was a lot of money in the box.
+Chapter 8 Becky’s Adventure Day
+In August Becky’s family came back from their vacation.
+Tom was very happy and he didn’t think about Injun Joe’s
+treasure.
+Becky’s adventure day was Saturday. Her mother said,
+“You can sleep at Susy Harper’s house after your
+adventure.’
+“Good,” Becky said.
+Becky and her friends went on the river on a big boat.
+The boat went down the river and across it. Then it
+stopped. The children went out of the boat and played
+games near the river. In the afternoon one boy asked,
+“Who wants to go to the big cave?”
+The children went to the cave. It was dark and cold
+there, but they played games. In the evening they went
+back to the boat and went home.
+Sunday morning, Becky’s mother and Aunt Polly talked
+to Mrs. Harper at church. Becky’s mother asked, “Where’s
+my Becky? Did she sleep at your house?”
+“No, she didn’t,” Mrs. Harper answered. “I didn’t see
+her.”
+Aunt Polly said, “My Tom didn’t come home. Did he
+stay at your house?”
+“No, he didn’t,” Mrs. Harper answered.
+Then Aunt Polly and Becky’s mother asked the children,
+“Did Tom and Becky come home? Did you see them on
+the boat?”
+The children answered, “No, we didn’t see them, but it
+was dark.”
+Then a boy said, “Maybe they’re in the cave!”
+17
+Two hundred men looked for Tom and Becky in the
+cave. They looked for three days, but they didn’t find
+them. People in the town were very sad.
+Chapter 9 Huck’s Adventure
+Huck didn’t go on Becky’s adventure. He stayed home
+and watched Injun Joe’s house that night. At eleven
+o’clock Injun Joe and his friend came out and walked
+down the street. There was a box in his friend’s hands.
+Huck said quietly, “Maybe that’s the treasure box.” He
+went after the two men.
+They walked to Mrs. Douglas’s house and stopped in her
+yard. Huck stayed behind some small trees. The men
+talked, and Huck listened to them.
+Injun Joe was angry. “I want to kill her,” he said to his
+friend. “Mr. Douglas was bad to me. He’s dead now, but I
+remember.”
+“’There are a lot of lights in the house. Maybe her
+friends are visiting,” Injun Joe’s friend said. “We can
+come back tomorrow.”
+“No,” Injun Joe said. “Let’s wait now.”
+Huck liked Mrs. Douglas because she was always good
+to him. He wanted to help her. He quietly walked away
+and then he started to run to Mr. Jones’s house.
+Mr. Jones opened the door. “What do you want?” he
+asked Huck.
+“Injun Joe and his friend are in Mrs. Douglas’s yard,”
+Huck said. “They want to kill her. Can you go there and
+help Mrs. Douglas?”
+18
+The men talked, and Huck listened to them.
+“Yes. My sons and I can go there,” Mr. Jones
+answered. “You can go home.”
+In the morning, Huck went back to Mr. Jones’s house.
+“How’s Mrs. Douglas?” he asked.
+“She’s OK,” Mr. Jones answered. “The men went away
+because we arrived.”
+“Good,” Huck said. But he was afraid of Injun Joe.
+“Please don’t say my name to Mrs. Douglas.”
+Mr. Jones looked at him, and then he said, “You aren’t
+well. Go and sleep in my bedroom.”
+Later, Mrs. Douglas visited Mr. Jones.
+“You helped me yesterday night. Thank you,” she said.
+“You’re a good man.”
+Mr. Jones said, “We didn’t know about the men in your
+yard. A boy was there and he wanted to help you. He came
+here, but I can’t say his name.”
+Mr. Jones and Mrs. Douglas went to church. People
+there talked about Tom and Becky. Mr. Jones and his sons
+went to the cave with the men, but on Monday morning
+they went home. Huck was in bed and was very sick. The
+men went back to the cave, but Mrs. Douglas stayed with
+Huck.
+Chapter 10 In the Cave
+Saturday, Tom and Becky walked and played in the cave.
+Then they stopped near some water.
+“What time is it?” Becky asked.
+“I don’t know,” Tom said. “Let’s go back now”
+
+20
+The two children walked and walked. But they didn’t
+find the door to the cave. Becky was afraid. She wanted to
+sit down and eat. “Maybe they’re looking for us now,” she
+said.
+“Here’s some food,” Tom said. “Eat this and wait here.
+I’m going to look for the door.”
+Tom walked and walked. But he didn’t find the cave
+door. Suddenly, there was a man near him. Tom was
+afraid, but he stayed quiet. He looked at the man. It was
+Injun Joe!
+Tom was very afraid and he made a noise. Injun Joe
+went away quickly. Tom went back to Becky, but he
+didn’t talk to her about Injun Joe.
+They were in the cave for three days. Tuesday, Becky
+didn’t want to walk. Again Tom said, “Stay here. I’m
+going to look for the door.”
+This time he went to a new place. There was light there.
+He went to the light. It came from a small door in the
+cave.
+Tom went out of the cave. Then he went back to Becky.
+“Come with me,” he said. “We can go out of the cave
+now.”
+Tom and Becky went out of the cave. They were very
+happy. They went to the river and waited there. Some men
+in a small boat came to them.
+Tom said, “We want to go home. Can you help us?”
+The men answered, “Yes. We can take you home.”
+Tom and Becky went in the boat with the men. They
+arrived home very late Tuesday night, and people in the
+town were very happy. Tom talked all night about their
+adventure in the cave.
+21
+Tom went out of the cave.
+Chapter 11 In the Cave Again
+Tom and Becky stayed home for many days. Then, two
+weeks after their adventure, Tom visited Becky and talked
+to her father.
+Mr. Thatcher said, “You’re a very good boy, Tom. You
+helped Becky in the cave. Thank you. People can’t go into
+it now because it has a new big door.”
+“But Injun Joe’s living in the cave!” Tom said.
+Some men went down the river to the cave. Tom went
+with them. They opened the new door. Injun Joe was
+there, but he was dead.
+Tom wanted to talk to Huck. Later in the week he went
+to Mr. Jones’s house. The two boys talked about their
+adventures.
+“The money isn’t in Injun Joe’s house,” Tom said. “It’s
+in the cave! I know, because Injun Joe was there. Let’s get
+it!”
+Huck was afraid. “But maybe we can’t find it.”
+“I can find it again,” Tom said. “I know about a small
+door at the back of the cave. Becky and I came out there.
+We can go in that door, and I can find Injun Joe’s treasure.”
+“OK,” Huck said. “Let’s go today.”
+That afternoon the boys went in a small boat to the back
+of the cave. Tom walked first, and Huck went after him.
+They walked and walked.
+Then Tom said, “This is the right place! Injun Joe was
+here.”
+The boys looked for a good place for treasure.
+Suddenly, Tom said, “Look! There’s a cross! Injun Joe
+said, ’under the cross.’ Let’s look there!” 
+“Look! It’s the treasure box!”
+The boys went to the place with the cross. Tom said,
+“I’m going to dig here with my knife ... Look! It’s the
+treasure box! Let’s get it out now. The treasure’s ours!”
+“This box is very heavy,” Huck said. “We can’t take it
+with us.”
+“I have some small bags,” Tom said. “We can put the
+money in them and take it home.”
+The boys went out of the cave with the money.
+Chapter 12 At Mrs. Douglas’s House
+Tom said, “Let’s take the money to the old house near
+Mrs. Douglas’s house. That’s a good place for it.”
+They started to walk to the old house. Mr. Jones was in
+Mrs. Douglas’s yard. He called to the boys.
+“A lot of people are waiting for you. Come with me,” he
+said. They went into Mrs. Douglas’s house.
+“Hello, boys,” Mrs. Douglas said. “Come with me.”
+Tom and Huck went with her to a bedroom. There were
+new shirts and jeans on the bed.
+“Wash your hands and faces and put on these shirts and
+jeans,” Mrs. Douglas said. “Then come to the big room.”
+The boys went to the room. A lot of people were there.
+Mrs. Douglas said, “First I want to say ‘thank you’ to
+Mr. Jones and his sons. They helped me. They’re very
+good people.”
+“Huck helped, too,” Mr. Jones said.
+ ‘Thank you, too, Huck,” Mrs. Douglas said. “You’re a
+good boy, and I like you. I want to give you a home and
+some money.”
+
+25
+“But Huck has a lot of money!” Tom said.
+He went to the bedroom and came back with the bags of
+money. “We have this money from the cave There’s a lot
+of money in them, and it’s ours now.”
+There was $12,000 in the bags. The people were very
+surprised. They asked about the boys’ adventure.
+Chapter 13 Huck’s New Home
+Huck lived in the big house with Mrs. Douglas. He was a
+new person. He washed every day, and he went to school
+and church. But he wasn’t happy. He stayed there for three
+weeks, and then he ran away.
+Tom went to Huck. “Why did you run away?”
+Huck answered, “Mrs. Douglas is a good woman. I like
+her, but I can’t live with her. I don’t like washing every
+day, and I don’t like going to school and church. I don’t
+want to have a lot of money. But I want to be your friend.
+OK?”
+“No,” Tom said, “I can’t be your friend, because the
+boys at school don’t want to play with you. We’re
+thinking about a lot of new adventures. Please live with
+Mrs. Douglas and come to school. Then the boys at school
+can play with you.”
+“I want to be your friend,” Huck said, “and I want to
+have adventures with you and the boys at school. Maybe I
+can live with Mrs. Douglas. I don’t know, but I’m going to
+try it again for a month.”
+“Good,” Tom said. “The boys are meeting later, at
+twelve o’clock at night. You can come, too.”
+“Good!” Huck said.
+ACTIVITIES
+Chapters 1-6
+Before you read
+1 Find the words in italics in your dictionary. They are all in the
+story.
+a Answer the questions.
+ What adventures are on TV? What adventures do you
+have?
+What are you afraid of?
+Do you like cats?
+What makes you sac/?
+ When are you surprised?
+b Put a word on the left with a word on the right.
+aunt dead
+church family
+fence sick
+graveyard police
+medicine picture
+paint yard
+trial Sunday
+c Put these words in the sentences.
+help kill tore (to tear)
+The man is going to..... the animal with his knife.
+She’s unhappy because she.......her new dress.
+He likes to..... his mother with her work.
+After you read
+2 Why
+a is Aunt Polly angry with Tom?
+b do Tom and Joe want to run away?
+c doesn’t Becky talk to Tom?
+27
+d do Tom and Huck want to help Muff Potter?
+e are Tom and Huck afraid of Injun Joe?
+Chapters 7-13
+Before you read
+3 Are Tom and Huck going to see Injun Joe again?
+4 Find the words in italics in your dictionary. Answer the questions.
+ a What can you buy in a box?
+b Is it light or dark in a cave?
+c The teacher put a cross on your answer. Is the answer
+ right or wrong?
+d What can you dig with? A ticket, a book or a knife?
+e What are your treasures?
+After you read
+5 Answer the questions.
+a How many nights are Tom and Becky in the cave?
+b Why is there a new big door on the cave?
+c How is Huck a new person?
+6 Where are they?
+a Injun Joe finds the treasure box.
+b Aunt Polly and Becky’s mother talk to the children.
+c Tom and Huck find the treasure box.
+d There is a surprise for Huck.
+Writing
+7 Is Tom a good boy or a bad boy? Why? Write about it.
+8 You are Tom or Huck. What are you going to do with your money
+ from the treasure box? Write about it.
+
+Answers for the Activities in this book are published in our free resource packs for teachers,
+the Penguin Readers Factsheets, or available on a separate sheet. Please write to your local
+Pearson Education office or to: Marketing Department, Penguin Longman Publishing,
+ 5 Bentinck Street, London W1M 5RN.
+
+VISIT www.penguinreaders.com
+The best place on the Internet to find
+information about graded Readers
+On our website you can:
+• Find the best selection of graded Readers in
+the world
+• Read about your favourite writers and
+film stars
+• Download free teacher's resources
+• Buy Penguin Readers online
+• Do much more ... Website
+BUY ONLINE AT w w w . p e n g u i n r e a d e r s . c o m
+Tom Sawyer loves adventures. He has them at home,
+at school, and with his friends—Huck Finn and Joe
+Harper. Tom has one adventure in a graveyard, and one
+in a cave. Who does he see there, and why is he afraid?
+Penguin Readers are simplified texts designed in association with Longman,
+the world famous educational publisher, to provide a step-by-step
+approach to this joys of reading for pleasure. Each book has an
+introduction and extensive activity material. They are published
+at seven levels from Easystarts (200 words) to Advanced (3000 words).
+Series Editors: Andy Hopkins and Jocelyn Potter
+www.penguinreaders.com
+Cover photograph © Frank Hopkinson 2000

podkorytova_yulia_lab_3/README.md

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+# Лабораторная работа 3. Деревья решений
+### Задание на лабораторную:
+Часть 1. По данным о пассажирах Титаника решите задачу классификации (с  помощью  дерева  решений),  в  которой  по  различным  характеристикам пассажиров требуется найти у выживших пассажиров два наиболее важных признака  из  трех  рассматриваемых  (по  варианту).
+
+**Вариант 20.**
+Pclass, Parch, Fare
+
+Часть 2. Решите с помощью библиотечной реализации дерева решений задачу из лабораторной работы «Веб-сервис «Дерево решений» по предмету «Методы  искусственного  интеллекта» на  99%  ваших  данных.  Проверьте работу модели на оставшемся проценте, сделайте вывод.
+***
+### Как запустить лабораторную работу:
+Для запуска лабораторной работы необходимо открыть файл `lr3.py`, нажать на ПКМ и в выпадающем списке выбрать опцию "Run".
+***
+### Технологии:
+**NumPy (Numerical Python)** - это библиотека для научных вычислений в Python, которая обеспечивает эффективные вычисления и манипуляции с данными.
+
+**Pandas** - это библиотека на языке Python, которая предоставляет удобные и эффективные инструменты для обработки и анализа данных. Она предоставляет высокоуровневые структуры данных, такие как DataFrame, которые позволяют легко и гибко работать с табличными данными.
+
+**Scikit-learn (Sklearn)** - это библиотека для языка программирования Python, которая предоставляет инструменты для разработки и применения различных алгоритмов машинного обучения, включая классификацию, регрессию, кластеризацию, снижение размерности и многое другое. Scikit-learn также предлагает функции для предобработки данных, оценки моделей и выбора наилучших параметров.
+***
+### Что делает лабораторная работа:
+В первой части лабораторной работе загружается выборка из файла `titanic.csv` с помощью пакета *Pandas*, пустые значения убираются из выборки.
+Далее в выборку отбираются 3 признака *(Pclass, Parch, Fare)* и определяется целевая переменная *(Survived)*.
+После обучается решающее дерево классификации с параметром *random_state=241* и остальными параметрами по умолчанию.
+Результатом первой части лабораторной работы являются определение двух наиболее важных признаков у выживших пассажиров.
+
+Во второй части лабораторной работе загружается выборка из файла `dataset.csv` с помощью пакета *Pandas*, тип устройства и уровень гибкости приводятся в числовому виду.
+Далее в выборку отбираются 2 признака *(Age и Device)* и определяется целевая переменная *(Flexibility Level)*.
+После данные разделяются на обучающие и тестовые выборки, создается и обучается дерево регрессии с параметрами по умолчанию.
+Результатом второй части лабораторной работы являются определение зависимости уровня гибкости от возраста и типа устройства и оценка точности модели.
+***
+### Пример выходных данных:
+***Часть 1:*** 
+выводятся первые 5 записей таблицы со столбцами по варианту, важности признаков и 2 наиболее важных признака из трех.
+![](result1.JPG)
+
+***Часть 2:***
+выводятся первые 5 записей таблицы со столбцами по варианту, важности признаков, 2 наиболее важных признака из трех и средняя квадратичная ошибка.
+![](result2.JPG)
+***
+**Вывод**: результаты первой части лабораторной работы показали, что у выживших пассажиров наиболее важными признаками являются *Fare* и *Parch*, причем *Fare* оказался самым важным признаком.
+
+По результатам второй части лабораторной можно сказать, что уровень гибкости праткически одинаково зависит как от типа устройства, с которого человек работает, так и от возраста учащегося.
+*Device* оказался более важным признаком, чем *Age*, но стоит сделать замечание, тип устройства и уровень гибкости были преобразованы к числовому виду, характер данных был искажен, 
+так как ранее объекты столбцов не могли быть математически сравнимы между собой, а после преобразований эта характеристика у них появилась.
+Посчитанная среднеквадратичная ошибка находится ближе к 0, чем к 1, это говорит о высоком качестве модели.

podkorytova_yulia_lab_3/lr3.py

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+import pandas as pd
+from sklearn.metrics import mean_squared_error
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
+import numpy as np
+
+# 1 часть лабораторной работы
+# Вариант 20. Pclass, Parch, Fare
+def part_one():
+    data = pd.read_csv('titanic.csv', index_col='Passengerid')
+    # выгрузка непустых данных
+    data = data.loc[(np.isnan(data['Pclass']) == False) & (np.isnan(data['Fare']) == False) & (np.isnan(data['Parch']) == False) & (np.isnan(data['Survived']) == False)]
+    # отбор нужных столбцов
+    corr = data[['Pclass', 'Parch', 'Fare']]
+    # респечатка первых 5 строк данных
+    print(corr.head())
+    # определение целевой переменной
+    y = data['Survived']
+    # создание и обучение дерева решений
+    clf = DecisionTreeClassifier(random_state=241)
+    clf.fit(corr, y)
+    # получение и распечатка важностей признаков
+    importances = clf.feature_importances_
+    print(importances)
+    top_importances = importances.argsort()[-2:][::-1]
+    print("Наиболее важные признаки:", corr.columns[top_importances][0], "и", corr.columns[top_importances][1])
+
+# функция для приведения типа мобильного устройства к числу
+def device_to_bool(device):
+    if device == "Computer":
+        return 0
+    elif device == "Mobile":
+        return 1
+    elif device == "Tab":
+        return 2
+
+# функция для приведения уровня гибкости к числу
+def flexibility_level_to_bool(flexibility_level):
+    if flexibility_level == "Low":
+        return 0
+    elif flexibility_level == "Moderate":
+        return 1
+    elif flexibility_level == "High":
+        return 2
+
+# 2 часть лабораторной работы
+# Вариант 20. Зависимость уровня гибкости от возраста и устройства, с которого человек работает
+def part_two():
+    data = pd.read_csv('dataset.csv')
+    # приведение типа мобильного устройства к числу
+    data['Device'] = data['Device'].apply(device_to_bool)
+    # приведение уровня гибкости к числу
+    data['Flexibility Level'] = data['Flexibility Level'].apply(flexibility_level_to_bool)
+    # отбор нужных столбцов
+    X = data[['Age', 'Device']]
+    # респечатка первых 5 строк данных
+    print(X.head())
+    # определение целевой переменной
+    y = data['Flexibility Level']
+    # разделение данных на обучающую и тестовую выборки
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.01, random_state=42)
+    # создание и обучение дерева регрессии
+    tree_reg = DecisionTreeRegressor()
+    tree_reg.fit(X_train, y_train)
+    # получение и распечатка важностей признаков
+    importances = tree_reg.feature_importances_
+    print(importances)
+    top_importances = importances.argsort()[-2:][::-1]
+    print("Наиболее важные признаки:", X.columns[top_importances][0], "и", X.columns[top_importances][1])
+    # предсказание на тестовых данных
+    y_pred = tree_reg.predict(X_test)
+    # оценка точности модели
+    mse = mean_squared_error(y_test, y_pred)
+    print("Средняя квадратичная ошибка:", mse)
+
+print("---ПЕРВАЯ ЧАСТЬ ЛАБОРАТОРНОЙ РАБОТЫ---")
+part_one()
+print("\n---ВТОРАЯ ЧАСТЬ ЛАБОРАТОРНОЙ РАБОТЫ---")
+part_two()