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sklearn.linear_model import LinearRegression, Perceptron +from sklearn.neural_network import MLPClassifier, MLPRegressor +from sklearn.preprocessing import LabelEncoder, OneHotEncoder, MinMaxScaler +from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier +from scipy.cluster.hierarchy import dendrogram, linkage + +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[:15] +X = X[["math score", "reading score", "writing score"]].values +labelList = [] +for i in X: + st = "" + for j in i: + st += str(j) + st += "," + st = "(" + st[:len(st) - 1] + ")" + labelList.append(st) +linked = linkage(X, 'single') + +plt.figure(figsize=(10, 7)) +dendrogram(linked, + orientation='top', + labels=labelList, + distance_sort='descending', + show_leaf_counts=True) +plt.show() \ No newline at end of file diff --git a/madyshev_egor_lab_4/readme.md b/madyshev_egor_lab_4/readme.md new file mode 100644 index 0000000..06fee3b --- /dev/null +++ b/madyshev_egor_lab_4/readme.md @@ -0,0 +1,24 @@ +# 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. +## Задание по варианту +Задача для дерева решений. Предсказание уровня образования родителей по всем остальных данным. +## Решение +### Запуск программы +Алгоритм кластеризации: dendrogram +### 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 кластеризует учеников по результатам их экзаменов. +### Тесты +![Кластеризация первых 15](lab4_1.png) + +На примере первых пятнадцати учеников хорошо видна их кластеризация дендрограммой. Ученики легко бьются на группы по их успеваемости. +Благодаря этому можно выделить группы учеников в зависимости от их оценок. Достаточно выбрать необходимую ветвь. + +![Кластеризация первых 45](lab4_2.png) +Хотя диаграмма на большее количество учеников и становиться более насыщенной. В ней всё равно достаточно просто выделить группы учеников по успеваемости. + +Вывод: Кластеризация дендрограммой позволяет достаточно эффективно делить учащихся на группы по успеваемости, основываясь на оценках их экзаменов. \ No newline at end of file