From 539b7e32a36173356c8dbaf1149039a458220bc5 Mon Sep 17 00:00:00 2001
From: sodaler <misha_lector87@mail.ru>
Date: Sun, 3 Dec 2023 16:00:39 +0400
Subject: [PATCH 1/2] degtyarev_mikhail_lab_4_is_ready

---
 degtyarev_mikhail_lab_4/Readme.md       |  74 +++
 degtyarev_mikhail_lab_4/ds_salaries.csv | 608 ++++++++++++++++++++++++
 degtyarev_mikhail_lab_4/img.png         | Bin 0 -> 26845 bytes
 degtyarev_mikhail_lab_4/main.py         |  41 ++
 4 files changed, 723 insertions(+)
 create mode 100644 degtyarev_mikhail_lab_4/Readme.md
 create mode 100644 degtyarev_mikhail_lab_4/ds_salaries.csv
 create mode 100644 degtyarev_mikhail_lab_4/img.png
 create mode 100644 degtyarev_mikhail_lab_4/main.py

diff --git a/degtyarev_mikhail_lab_4/Readme.md b/degtyarev_mikhail_lab_4/Readme.md
new file mode 100644
index 0000000..aba9e22
--- /dev/null
+++ b/degtyarev_mikhail_lab_4/Readme.md
@@ -0,0 +1,74 @@
+# Лабораторная 3
+## Вариант 9
+
+## Задание
+Использовать метод кластеризации t-SNE,самостоятельно  сформулировав  задачу. Интерпретировать результаты и оценить, насколько хорошо он подходит для решения сформулированной вамизадачи.
+
+Задача:
+- Можно использовать кластеризацию для группировки компаний на основе их местоположения (company_location) и размера (company_size). Это поможет выделить группы компаний с похожими характеристиками.
+
+## Описание Программы
+Программа выполняет кластеризацию компаний на основе их местоположения (company_location) и размера (company_size) с использованием методов t-SNE и KMeans. 
+
+### Используемые библиотеки
+`pandas:`
+
+Используется для загрузки данных из CSV-файла и работы с ними в виде датафрейма (pd.read_csv, pd.DataFrame).
+
+`LabelEncoder (из scikit-learn):`
+
+Применяется для преобразования категориальных переменных (company_location и company_size) в числовые значения (le.fit_transform).
+
+`TSNE (из scikit-learn):`
+
+Используется для выполнения уменьшения размерности данных с помощью метода t-SNE (TSNE(n_components=2, random_state=42)).
+
+`KMeans (из scikit-learn):`
+
+Применяется для кластеризации данных методом KMeans (KMeans(n_clusters=3, random_state=42)).
+
+`matplotlib и seaborn:`
+
+Используются для визуализации данных и построения графика, который отображает результаты кластеризации (plt.figure, sns.scatterplot, plt.title, plt.show).
+
+### Шаги программы
+
+**Загрузка данных:**
+
+Данные о компаниях загружаются из CSV-файла "ds_salaries.csv" с использованием pandas.
+
+**Преобразование категориальных переменных:**
+
+Местоположение компаний (company_location) и их размер (company_size) преобразуются из категориальных в числовые значения с помощью LabelEncoder.
+
+**Выбор признаков:**
+
+Выбираются признаки для анализа, в данном случае, местоположение и размер компаний.
+
+**Уменьшение размерности с использованием t-SNE:**
+
+Применяется метод t-SNE для уменьшения размерности данных до двух компонент.
+
+**Кластеризация данных с использованием KMeans:**
+
+Кластеризация данных выполняется с помощью метода KMeans с 3 кластерами, определенными на основе результата t-SNE.
+
+**Создание и визуализация нового датафрейма:**
+
+Создается новый датафрейм (data_tsne_df), содержащий новые координаты компаний после применения t-SNE, а также метки кластеров.
+Добавляется номер кластера к исходным данным.
+
+**Визуализация кластеров:**
+
+Выполняется визуализация результатов кластеризации с использованием библиотеки seaborn.
+
+### Запуск программы
+- Склонировать или скачать код `main.py`.
+- Запустите файл в среде, поддерживающей выполнение Python. `python main.py`
+
+### Результаты
+![](img.png)
+
+На графике представлены компании в двумерном пространстве, где каждая точка относится к конкретному кластеру. Различные цвета точек обозначают принадлежность к разным кластерам.
+
+Кластеры можно рассматривать как группы компаний с схожими характеристиками местоположения и размера. Компании, находящиеся близко в двумерном пространстве, могут иметь схожие характеристики.
diff --git a/degtyarev_mikhail_lab_4/ds_salaries.csv b/degtyarev_mikhail_lab_4/ds_salaries.csv
new file mode 100644
index 0000000..4f56347
--- /dev/null
+++ b/degtyarev_mikhail_lab_4/ds_salaries.csv
@@ -0,0 +1,608 @@
+,work_year,experience_level,employment_type,job_title,salary,salary_currency,salary_in_usd,employee_residence,remote_ratio,company_location,company_size
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+286,2021,SE,FT,Head of Data,87000,EUR,102839,SI,100,SI,L
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+320,2022,SE,FT,Data Engineer,132320,USD,132320,US,0,US,M
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diff --git a/degtyarev_mikhail_lab_4/main.py b/degtyarev_mikhail_lab_4/main.py
new file mode 100644
index 0000000..f8b991d
--- /dev/null
+++ b/degtyarev_mikhail_lab_4/main.py
@@ -0,0 +1,41 @@
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+from sklearn.manifold import TSNE
+from sklearn.cluster import KMeans
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Загрузка данных
+data = pd.read_csv("ds_salaries.csv")
+
+# Преобразование категориальных переменных в числовые
+le = LabelEncoder()
+data['company_location'] = le.fit_transform(data['company_location'])
+data['company_size'] = le.fit_transform(data['company_size'])
+
+# Выбор нужных признаков
+features = ['company_location', 'company_size']
+
+# Применение t-SNE для уменьшения размерности
+tsne = TSNE(n_components=2, random_state=42)
+data_tsne = tsne.fit_transform(data[features])
+
+# Кластеризация данных
+kmeans = KMeans(n_clusters=3, random_state=42)
+labels = kmeans.fit_predict(data_tsne)
+
+# Создание датафрейма с новыми координатами и метками кластеров
+data_tsne_df = pd.DataFrame(data=data_tsne, columns=['Dimension 1', 'Dimension 2'])
+data_tsne_df['Cluster'] = labels
+
+# Добавление номера кластера в исходные данные
+data['Cluster'] = labels
+
+# Визуализация
+plt.figure(figsize=(10, 6))
+sns.scatterplot(x='Dimension 1', y='Dimension 2', hue='Cluster', data=data_tsne_df, palette='viridis', s=50)
+plt.title('t-SNE Clustering of Companies')
+plt.show()
+
+# Вывод номера кластера в исходных данных
+print(data[['company_location', 'company_size', 'Cluster']])

From d4ab124a2b5fdcac18179140250c566ce29e2f53 Mon Sep 17 00:00:00 2001
From: sodaler <misha_lector87@mail.ru>
Date: Sun, 3 Dec 2023 16:01:36 +0400
Subject: [PATCH 2/2] change readme

---
 degtyarev_mikhail_lab_4/Readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/degtyarev_mikhail_lab_4/Readme.md b/degtyarev_mikhail_lab_4/Readme.md
index aba9e22..07faf35 100644
--- a/degtyarev_mikhail_lab_4/Readme.md
+++ b/degtyarev_mikhail_lab_4/Readme.md
@@ -1,4 +1,4 @@
-# Лабораторная 3
+# Лабораторная 4
 ## Вариант 9
 
 ## Задание