282 lines
342 KiB
Plaintext
282 lines
342 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Лабораторная работа 6\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<b>1. Выбрать бизнес-цель для набора данных:</b><br><br> \n",
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"\n",
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"Сегментация клиентов для разработки персонализированных маркетинговых стратегий. Цель кластеризации: разделить клиентов на группы на основе их возраста, дохода и расходов, чтобы определить наиболее перспективные сегменты. Будем использовать Age, Income и Total_Spending с прошлой лабы"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<b>2. Выполнить понижение размерности и визуализацию данных:</b><br><br> \n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Figure size 800x600 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from sklearn.decomposition import PCA\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"import pandas as pd\n",
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"\n",
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"# Загружаем данные\n",
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"data = pd.read_csv(\"..//..//static//csv//marketing_campaign.csv\", sep=\"\\t\")\n",
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"\n",
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"\n",
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"# Фильтрация и выбор нужных столбцов\n",
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"data['Age'] = 2024 - data['Year_Birth'] \n",
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"data['Total_Spending'] = (data['MntWines'] + data['MntFruits'] + data['MntMeatProducts'] +\n",
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" data['MntFishProducts'] + data['MntSweetProducts'] + data['MntGoldProds'])\n",
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"data = data[['Age', 'Income', 'Total_Spending']].dropna()\n",
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"\n",
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"# Стандартизация данных\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"scaler = StandardScaler()\n",
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"data_scaled = scaler.fit_transform(data)\n",
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"\n",
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"# Применение PCA\n",
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"pca = PCA(n_components=2)\n",
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"data_pca = pca.fit_transform(data_scaled)\n",
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"\n",
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"# Создание DataFrame с результатами PCA\n",
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"pca_df = pd.DataFrame(data_pca, columns=['PC1', 'PC2'])\n",
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"\n",
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"# Визуализация\n",
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"plt.figure(figsize=(8, 6))\n",
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"sns.scatterplot(x='PC1', y='PC2', data=pca_df, s=50, alpha=0.7)\n",
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"plt.title('Визуализация данных после PCA', fontsize=14)\n",
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"plt.xlabel('Первая главная компонента')\n",
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"plt.ylabel('Вторая главная компонента')\n",
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"plt.grid(True)\n",
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"plt.show()\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<b>3. Выполнить выбор количества кластеров </b><br><br>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x500 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from sklearn.cluster import KMeans\n",
|
|||
|
|
"from sklearn.metrics import silhouette_score\n",
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Выбираем признаки для кластеризации\n",
|
|||
|
|
"features = data[['Age', 'Income', 'Total_Spending']].dropna()\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Стандартизация данных\n",
|
|||
|
|
"from sklearn.preprocessing import StandardScaler\n",
|
|||
|
|
"scaler = StandardScaler()\n",
|
|||
|
|
"features_scaled = scaler.fit_transform(features)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Списки для хранения метрик\n",
|
|||
|
|
"inertia = []\n",
|
|||
|
|
"silhouette_scores = []\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Оценка для числа кластеров от 2 до 10\n",
|
|||
|
|
"k_values = range(2, 11)\n",
|
|||
|
|
"for k in k_values:\n",
|
|||
|
|
" kmeans = KMeans(n_clusters=k, random_state=42)\n",
|
|||
|
|
" labels = kmeans.fit_predict(features_scaled)\n",
|
|||
|
|
" \n",
|
|||
|
|
" inertia.append(kmeans.inertia_)\n",
|
|||
|
|
" silhouette_scores.append(silhouette_score(features_scaled, labels))\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Визуализация инерции\n",
|
|||
|
|
"plt.figure(figsize=(12, 5))\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.subplot(1, 2, 1)\n",
|
|||
|
|
"plt.plot(k_values, inertia, marker='o', color='orange')\n",
|
|||
|
|
"plt.title('Инерция для различных k', fontsize=14)\n",
|
|||
|
|
"plt.xlabel('Количество кластеров (k)')\n",
|
|||
|
|
"plt.ylabel('Инерция')\n",
|
|||
|
|
"plt.grid(True)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Визуализация коэффициента силуэта\n",
|
|||
|
|
"plt.subplot(1, 2, 2)\n",
|
|||
|
|
"plt.plot(k_values, silhouette_scores, marker='o', color='orange')\n",
|
|||
|
|
"plt.title('Коэффициент силуэта для различных k', fontsize=14)\n",
|
|||
|
|
"plt.xlabel('Количество кластеров (k)')\n",
|
|||
|
|
"plt.ylabel('Силуэт')\n",
|
|||
|
|
"plt.grid(True)\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.tight_layout()\n",
|
|||
|
|
"plt.show()\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"По графику инерции оптимальное значение находится около k = 3 (точка изгиба \"локтя\"). <br><br>\n",
|
|||
|
|
"Коэффициент силуэта также максимален при k = 2, но при k = 3 он все еще достаточно высокий. Выбираем k = 3 для баланса."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"<b>4. Выполнить кластерный анализ с применением иерархического и неиерархического алгоритма кластеризации.</b>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1000x700 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from scipy.cluster.hierarchy import dendrogram, linkage\n",
|
|||
|
|
"from sklearn.cluster import AgglomerativeClustering\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Выполняем агломеративную кластеризацию иерархически\n",
|
|||
|
|
"linkage_matrix = linkage(features_scaled, method='ward')\n",
|
|||
|
|
"plt.figure(figsize=(10, 7))\n",
|
|||
|
|
"dendrogram(linkage_matrix)\n",
|
|||
|
|
"plt.title('Дендрограмма')\n",
|
|||
|
|
"plt.xlabel('Объекты')\n",
|
|||
|
|
"plt.ylabel('Евклидово расстояние')\n",
|
|||
|
|
"plt.show()\n",
|
|||
|
|
"hierarchical = AgglomerativeClustering(n_clusters=3)\n",
|
|||
|
|
"data['Cluster_Hierarchical'] = hierarchical.fit_predict(features_scaled)\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 800x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"kmeans = KMeans(n_clusters=3, random_state=42)\n",
|
|||
|
|
"data['Cluster_KMeans'] = kmeans.fit_predict(features_scaled)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Визуализация кластеров неиерархически\n",
|
|||
|
|
"plt.figure(figsize=(8, 6))\n",
|
|||
|
|
"sns.scatterplot(x='Age', y='Income', hue='Cluster_KMeans', data=data, palette='viridis', s=50)\n",
|
|||
|
|
"plt.title('Кластеры (K-Means)', fontsize=14)\n",
|
|||
|
|
"plt.xlabel('Age')\n",
|
|||
|
|
"plt.ylabel('Income')\n",
|
|||
|
|
"plt.legend(title='Кластер')\n",
|
|||
|
|
"plt.grid(True)\n",
|
|||
|
|
"plt.show()\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"<b>5. Оценить качество решения </b>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Коэффициент силуэта (K-Means): 0.374\n",
|
|||
|
|
"Коэффициент силуэта (Иерархическая кластеризация): 0.339\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"silhouette_kmeans = silhouette_score(features_scaled, data['Cluster_KMeans'])\n",
|
|||
|
|
"\n",
|
|||
|
|
"silhouette_hierarchical = silhouette_score(features_scaled, data['Cluster_Hierarchical'])\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(f\"Коэффициент силуэта (K-Means): {silhouette_kmeans:.3f}\")\n",
|
|||
|
|
"print(f\"Коэффициент силуэта (Иерархическая кластеризация): {silhouette_hierarchical:.3f}\")\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "aimenv",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 3
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
|
|||
|
|
"version": "3.12.7"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|