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b/madyshev_egor_lab_5/main.py new file mode 100644 index 0000000..567c8d5 --- /dev/null +++ b/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)) diff --git a/madyshev_egor_lab_5/readme.md b/madyshev_egor_lab_5/readme.md new file mode 100644 index 0000000..8e22e1f --- /dev/null +++ b/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 \ No newline at end of file -- 2.25.1