Работа со встроеным датасетом и csv файлом

README.md

@@ -0,0 +1,88 @@
+# Week 4 — Scikit-learn Classification
+
+## Тема проекта
+
+Classification — Linear Discriminant Analysis (LDA)
+
+Используемый пример из официальной документации scikit-learn:
+
+https://scikit-learn.org/stable/auto_examples/classification/plot_lda.html
+
+
+
+## Цель работы
+
+Изучить применение алгоритма Linear Discriminant Analysis (LDA) для решения задачи классификации с использованием библиотеки scikit-learn.
+
+В ходе работы были:
+- изучены основы классификации;
+- выполнен препроцессинг данных;
+- обучена модель LDA;
+- проведена визуализация результатов;
+- выполнен анализ качества модели.
+
+---
+
+## Используемые технологии
+
+- Python
+- JupyterLab
+- scikit-learn
+- pandas
+- matplotlib
+- seaborn
+- openml
+
+---
+
+## Структура проекта
+
+```text
+project/
+│
+├── week4_scikit_learn.ipynb
+├── requirements.txt
+├── README.md
+├── .gitignore
+└── data/
+    └── wine.csv
+
+## Используемые датасеты
+
+### 1. Встроенный датасет
+Wine Dataset из scikit-learn.
+
+### 2. Внешний датасет
+CSV-версия Wine Dataset, загружаемая через pandas.read_csv().
+
+
+## Этапы выполнения работы
+
+1. Импорт библиотек
+2. Загрузка данных
+3. Анализ данных
+4. Масштабирование признаков
+5. Разделение выборки
+6. Обучение модели LDA
+7. Предсказание классов
+8. Оценка качества модели
+9. Визуализация результатов
+10. Интерпретация результатов
+
+
+## Результаты
+
+Модель Linear Discriminant Analysis показала высокую точность классификации на датасете Wine.
+
+Было установлено, что:
+- масштабирование признаков улучшает качество модели;
+- LDA эффективно разделяет классы;
+- метод хорошо подходит для задач классификации и снижения размерности.
+
+---
+
+## Автор
+
+Студент: Борончук Савелий Сергеевич
+
+Группа: ИНБб-2302-02-00

data/wine.csv

@@ -0,0 +1,179 @@
+alcohol,malic_acid,ash,alcalinity_of_ash,magnesium,total_phenols,flavanoids,nonflavanoid_phenols,proanthocyanins,color_intensity,hue,od280/od315_of_diluted_wines,proline,target
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