diff --git a/lipatov_ilya_lab_5/README.md b/lipatov_ilya_lab_5/README.md new file mode 100644 index 0000000..885e17c --- /dev/null +++ b/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 diff --git a/lipatov_ilya_lab_5/boston.csv b/lipatov_ilya_lab_5/boston.csv new file mode 100644 index 0000000..e6a0ce1 --- /dev/null +++ b/lipatov_ilya_lab_5/boston.csv @@ -0,0 +1,507 @@ +CRIM,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,B,LSTAT,MEDV 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a/lipatov_ilya_lab_5/lab5.py b/lipatov_ilya_lab_5/lab5.py new file mode 100644 index 0000000..6616b8f --- /dev/null +++ b/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)) \ No newline at end of file