From 2782063554822e599b4284cf784d877e3f90bfb1 Mon Sep 17 00:00:00 2001 From: Ilya Lipatov Date: Fri, 1 Dec 2023 21:29:29 +0400 Subject: [PATCH] lipatov_ilya_lab_6 --- lipatov_ilya_lab_6/README.md | 25 ++ lipatov_ilya_lab_6/boston.csv | 507 ++++++++++++++++++++++++++++++++++ lipatov_ilya_lab_6/lab6.py | 34 +++ lipatov_ilya_lab_6/result.png | Bin 0 -> 12268 bytes 4 files changed, 566 insertions(+) create mode 100644 lipatov_ilya_lab_6/README.md create mode 100644 lipatov_ilya_lab_6/boston.csv create mode 100644 lipatov_ilya_lab_6/lab6.py create mode 100644 lipatov_ilya_lab_6/result.png diff --git a/lipatov_ilya_lab_6/README.md b/lipatov_ilya_lab_6/README.md new file mode 100644 index 0000000..46c1c79 --- /dev/null +++ b/lipatov_ilya_lab_6/README.md @@ -0,0 +1,25 @@ +## Лабораторная работа №6 + +### Нейронная сеть + +## Выполнил студент группы ПИбд-41 Липатов Илья + +### Как запустить лабораторную работу: + +* установить python, pandas, sklearn +* запустить проект (стартовая точка класс lab6) + +### Какие технологии использовались: + +* Язык программирования `Python`, библиотеки pandas, sklearn +* Среда разработки `PyCharm` + +### Что делает лабораторная работа: + +* Осуществляет обучение модели нейронной сети по варианту (MLPClassifier) для предсказания цен на квартиры в Бостоне относительно среднего количества комнат, % более низкого статуса населения и уровня преступности на душу населения. + +### Примеры работы: + +#### Результаты: + +![Result](result.png) \ No newline at end of file diff --git a/lipatov_ilya_lab_6/boston.csv b/lipatov_ilya_lab_6/boston.csv new file mode 100644 index 0000000..e6a0ce1 --- /dev/null +++ b/lipatov_ilya_lab_6/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 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+from sklearn.metrics import mean_absolute_percentage_error +from sklearn.model_selection import train_test_split +from sklearn.neural_network import MLPClassifier +import pandas as pd + +def MEDV_to_number(MEDV): + if MEDV < (minMEDV + meanMEDV) / 2: + return 1 + if MEDV < (maxMEDV + meanMEDV) / 2: + return 2 + + return 3 + +data = pd.read_csv('boston.csv') + +minMEDV = data['MEDV'].min() +maxMEDV = data['MEDV'].max() +meanMEDV = data['MEDV'].mean() + + +data['MEDV'] = data['MEDV'].apply(MEDV_to_number) +X = (data[['RM', 'LSTAT', 'CRIM']]) +y = data['MEDV'] + +X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.1, random_state=42) + +clf = MLPClassifier(hidden_layer_sizes=(100, 100), activation='relu', solver='lbfgs', max_iter=1000) +clf.fit(X_train, Y_train) + +y_predict = clf.predict(X_test) +score = clf.score(X_test, Y_test) + +print(f'Score: {score}') +print(f'MAPE: {mean_absolute_percentage_error(Y_test, y_predict)}') diff --git a/lipatov_ilya_lab_6/result.png 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