From ef18e8f2b691d5f1c35b16b31c73a3a6c2b09812 Mon Sep 17 00:00:00 2001
From: Danila Kamyshov <github.com/Gentle_Dan>
Date: Wed, 6 Dec 2023 13:43:07 +0400
Subject: [PATCH] kamyshov_danila_lab_4 is done

---
 kamyshov_danila_lab_4/app.py                  |  54 +++
 kamyshov_danila_lab_4/readme.md               |  32 ++
 kamyshov_danila_lab_4/static/cluster_plot.png | Bin 0 -> 46860 bytes
 kamyshov_danila_lab_4/student-mat.csv         | 396 ++++++++++++++++++
 kamyshov_danila_lab_4/templates/index.html    |  11 +
 5 files changed, 493 insertions(+)
 create mode 100644 kamyshov_danila_lab_4/app.py
 create mode 100644 kamyshov_danila_lab_4/readme.md
 create mode 100644 kamyshov_danila_lab_4/static/cluster_plot.png
 create mode 100644 kamyshov_danila_lab_4/student-mat.csv
 create mode 100644 kamyshov_danila_lab_4/templates/index.html

diff --git a/kamyshov_danila_lab_4/app.py b/kamyshov_danila_lab_4/app.py
new file mode 100644
index 0000000..6ab2beb
--- /dev/null
+++ b/kamyshov_danila_lab_4/app.py
@@ -0,0 +1,54 @@
+from flask import Flask, render_template
+import pandas as pd
+from scipy.cluster.hierarchy import linkage, fcluster
+import seaborn as sns
+from sklearn.preprocessing import LabelEncoder
+import io
+import base64
+
+app = Flask(__name__)
+
+# Load data from student-mat.csv
+data = pd.read_csv('student-mat.csv')
+
+def generate_clusters_image():
+    # Selecting columns of interest for clustering
+    features = data[['Pstatus', 'guardian', 'internet', 'romantic', 'famrel', 'freetime', 'goout', 'Dalc', 'Walc', 'health', 'absences']]
+
+    # Convert categorical variables to numerical using LabelEncoder
+    label_encoder = LabelEncoder()
+    features_categorical = features.select_dtypes(include='object')
+    features[features_categorical.columns] = features_categorical.apply(label_encoder.fit_transform)
+
+    # Calculate linkage matrix
+    linkage_matrix = linkage(features, method='ward')
+
+    # Cut the tree to form clusters
+    data['cluster'] = fcluster(linkage_matrix, 3, criterion='maxclust')  # You can adjust the number of clusters
+
+    # Drop the 'cluster' column to avoid confusion in sns.clustermap
+    features_for_heatmap = features.copy()  # Make a copy to avoid modifying the original DataFrame
+    features_for_heatmap['cluster'] = data['cluster']
+    features_for_heatmap = features_for_heatmap.drop('cluster', axis=1)
+
+    # Visualize clusters
+    sns.set(style='white')
+    g = sns.clustermap(features_for_heatmap, col_cluster=False, figsize=(24, 16), cmap="viridis")
+
+    # Save the image to a buffer
+    img_buffer = io.BytesIO()
+    g.savefig(img_buffer, format='png')
+    img_buffer.seek(0)
+
+    # Convert the image to a base64 string
+    img_base64 = base64.b64encode(img_buffer.read()).decode()
+
+    return img_base64
+
+@app.route('/')
+def index():
+    clusters_image = generate_clusters_image()
+    return render_template('index.html', clusters_image=clusters_image)
+
+if __name__ == '__main__':
+    app.run(port=5000)
diff --git a/kamyshov_danila_lab_4/readme.md b/kamyshov_danila_lab_4/readme.md
new file mode 100644
index 0000000..023ada4
--- /dev/null
+++ b/kamyshov_danila_lab_4/readme.md
@@ -0,0 +1,32 @@
+Общее задание:
+Использовать метод кластеризации к данным из курсовой работы, самостоятельно сформулировав задачу.
+Интерпретировать результаты и оценить, насколько хорошо он подходит для решения сформулированной вами задачи.
+
+Задание по вариантам:
+Тема: Анализ благополучия студентов
+Датасет: Student Alcohol Consumption
+ссылка: https://www.kaggle.com/datasets/uciml/student-alcohol-consumption?resource=download
+
+Задача для кластарезации:
+модель linkage
+применение алгоритмов кластеризации к данным о студентах, для выявления схожих групп студентов по признакам
+
+Чтобы Запустить приложение нужно запустить файл app.py
+
+Технологии:
+
+Flask: Фреймворк для создания веб-приложений на языке Python.
+pandas: Библиотека для обработки и анализа данных.
+scipy: Библиотека для научных вычислений, включая алгоритмы кластеризации.
+seaborn: Библиотека для визуализации данных.
+Описание работы программы:
+Программа использует Flask для создания веб-приложения. Она загружает данные о студентах из файла 'student-mat.csv' с использованием pandas. Затем она выполняет кластеризацию студентов с использованием алгоритма linkage из scipy и визуализирует результаты с помощью seaborn в виде тепловой карты (heatmap).
+
+Программа предоставляет веб-страницу, на которой отображается тепловая карта, показывающая схожесть студентов по выбранным признакам.
+
+Входные данные:
+
+Файл 'student-mat.csv', содержащий данные о студентах.
+Выходные данные:
+
+Веб-страница с тепловой картой, отображающей результаты кластеризации студентов.
\ No newline at end of file
diff --git a/kamyshov_danila_lab_4/static/cluster_plot.png b/kamyshov_danila_lab_4/static/cluster_plot.png
new file mode 100644
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diff --git a/kamyshov_danila_lab_4/student-mat.csv b/kamyshov_danila_lab_4/student-mat.csv
new file mode 100644
index 0000000..6c65182
--- /dev/null
+++ b/kamyshov_danila_lab_4/student-mat.csv
@@ -0,0 +1,396 @@
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diff --git a/kamyshov_danila_lab_4/templates/index.html b/kamyshov_danila_lab_4/templates/index.html
new file mode 100644
index 0000000..95deee5
--- /dev/null
+++ b/kamyshov_danila_lab_4/templates/index.html
@@ -0,0 +1,11 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Student Clusters</title>
+</head>
+<body>
+    <h1>Student Clusters</h1>
+    <img src="data:image/png;base64,{{ clusters_image }}" alt="Student Clusters">
+</body>
+</html>