From d5cd684a9804227d63d0d6363e1d4056b38d19e5 Mon Sep 17 00:00:00 2001 From: a-shdv Date: Sat, 4 Nov 2023 19:10:52 +0400 Subject: [PATCH] Add lab4 --- shadaev_anton_lab_4/README.md | 33 + shadaev_anton_lab_4/main.py | 32 +- shadaev_anton_lab_4/myplot.png | Bin 0 -> 82764 bytes shadaev_anton_lab_4/stroke_prediction_ds.csv | 5111 ++++++++++++++++++ 4 files changed, 5175 insertions(+), 1 deletion(-) create mode 100644 shadaev_anton_lab_4/README.md create mode 100644 shadaev_anton_lab_4/myplot.png create mode 100644 shadaev_anton_lab_4/stroke_prediction_ds.csv diff --git a/shadaev_anton_lab_4/README.md b/shadaev_anton_lab_4/README.md new file mode 100644 index 0000000..1951e02 --- /dev/null +++ b/shadaev_anton_lab_4/README.md @@ -0,0 +1,33 @@ +# IIS_2023_1 +### Задание +Использовать метод кластеризации по 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. +4. DBSCAN + +### Способ запуска лабораторной работы +Выполнить скрипт `shadaev_anton_lab_4/main.py` + +### Стек технологий +* `Python`: v. 3.11 +* `Pandas` - библиотека, которая позволяет работать с двумерными и многомерными таблицами, строить сводные таблицы, выделять колонки, использовать фильтры по параметрам, выполнять группировку по параметрам, запускать функции (сложение, нахождение медианы, среднего, минимального, максимального значений), объединять таблицы и многое другое +* `Sklearn` - предоставляет ряд инструментов для моделирования данных, включая классификацию, регрессию, кластеризацию и уменьшение размерности. +* `Matplotlib` - это библиотека для визуализации данных в Python, предоставляющая инструменты для создания статических, анимированных и интерактивных графиков и диаграмм. + +### Описание кода +1. Загрузка данных из .csv-файла. +2. Предварительная обработка данных от null-значений. +3. Отбор признаков. +4. Нормализация признаков. +5. Применение алгоритма DBScan. +6. Визуализация данных. + +График: + +![myplot.png](myplot.png) + +### Вывод +Каждый цвет представляет собой отдельный кластер, и точки с одинаковым цветом принадлежат одному и тому же кластеру. +* Средний уровень сахара в крови варьируется (примерно) от 140 до 250. +Возраст варьируется (примерно) от 30 до 80. +* На графике видно очень много фиолетовых точек, что говорит о том, что на нашем графике очень много шума. Но по другим точкам (более светлым) уже можем отобрать какие-то данные. + +В целом, применение алгоритма DBScan к признакам (age, avg_glucose_level) из данного датасета не очень эффективно. Визуально можно оценить эффективность алгоритма на 5-10%. В моем случае этот алгоритм неэффективен, т.к. алгоритм лучше работает с данными с высокой плотностью, а разделить на группы среди признаков возраст и средний уровень сахара в крови может быть проблематично, т.к. получится выделить не так много характеристик среди групп с разным возрастом. По-сути все, что мы имеем, это низкий, средний, высокий уровень сахара в крови среди разных возрастных групп. \ No newline at end of file diff --git a/shadaev_anton_lab_4/main.py b/shadaev_anton_lab_4/main.py index e0369f7..b038594 100644 --- a/shadaev_anton_lab_4/main.py +++ b/shadaev_anton_lab_4/main.py @@ -1 +1,31 @@ -print('test') \ No newline at end of file +import pandas as pd +import matplotlib.pyplot as plt +from sklearn.cluster import DBSCAN +from sklearn.preprocessing import StandardScaler + +# Загрузка данных из .csv-файла +data = pd.read_csv('stroke_prediction_ds.csv') + +# В датасете встречаются null-значения, поэтому предварительно очистим датасет от них +data = data.dropna() + +# Выбор признаков (гипертензия и сердечные заболевания) +features = data[['age', 'avg_glucose_level']] + +# Нормализация признаков перед применением алгоритма DBScan, что позволит алгоритму работать быстрее и давать лучшие +# результаты, так как данные будут приведены к одному виду на единой шкале +scaler = StandardScaler() +features = scaler.fit_transform(features) + +# Создание экземпляра DBScan +dbscan = DBSCAN(eps=0.1, min_samples=5) + +# Применение алгорита DBScan к данным +data['cluster'] = dbscan.fit_predict(features) + +# Визуализация данных +plt.scatter(data['age'], data['avg_glucose_level'], c=data['cluster']) +plt.xlabel('Возраст') +plt.ylabel('Средний уровень сахара в крови') +plt.title('Кластеризация данных по возрасту и среднему уровню сахара в крови') +plt.show() diff --git a/shadaev_anton_lab_4/myplot.png b/shadaev_anton_lab_4/myplot.png new file mode 100644 index 0000000000000000000000000000000000000000..d5763e6a330d739f737238da09dc38ecae40d3f5 GIT binary patch literal 82764 zcmeFYQ+s9I7A+jxuGm(ERbj<;#YV+WDz__%aNjTJdVB6fa_YDp6<&0Hcn!x<)XG5akEY(t z$nh(eD(*Eb=m=T1+wF+bzwXA%9rA|heO;nuVq*I8rCFs?!xX*kW}G{d+quK@rLkU) zysoKfuuIm{$De|R-L@F3YxGvZaxkgt4Ej$TmN*=)dx@&=H`yPvm731| zVVIEV$4=*~M~8=xQ({EQRa#HI$o!`(O&9*q$OR2n%k|wV({esH{aI5^B>Y#=uT<4_ 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