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Вариант 15 + +### Задание +Использовать алгоритм кластеризации *K-means*, самостоятельно сформулировав задачу. Интерпретировать результаты и оценить, насколько хорошо он подходит для решения сформулированной задачи. + +### Как запустить лабораторную работу +Для запуска программы необходимо с помощью командной строки в корневой директории файлов прокета прописать: +``` +python main.py +``` +### Какие технологии использовали +- Библиотека *numpy* для работы с массивами. +- Библиотека *pandas* для работы с данными в формате таблицы. +- Библиотека *matplotlib pyplot* - для визуализации данных. +- Библиотека *sklearn*: + - *KMeans* для использования алгоритма кластеризации K-средних. + - *train_test_split* для разделения набора данных на обучающую и тестовую выборки. + - *LinearRegression* для создания и работы с моделью Линейной регрессии. + - RFE для рекурсивного отбора признаков + +### Описание лабораторной работы +#### Сформулированная задача +Задача анализа, решаемые алгоритмом кластеризации: выделить локации (страны) с похожими характеристиками вакансий для определения региональных тенденций и особенностей рынка труда. + +#### Оценка важности параметров +Прежде, чем перейти к решению поставленной задачи. Произведем анализ важности параметров, чтобы выделить наиболее существенные характеристики, которые влияют на выделение лакаций с похожиим характеристиками. Для этого создадим функцию `recursive_feature_elimination()`, где создаем копию исходного датафрейма `data` и присваивается переменной `df`. В `df` удаляем столбцы `"Country"` и `"location"` с помощью метода `drop()`, т.к. на данных столбца `"Country"` будем использовать как целеввые значения (заранее пропишем `y = data['Country']` общий для всех функций, которые создадим в дальнейшем). Данные разделяем на обучающую и тестовую выборки с использованием функции `train_test_split()`. Размер тестовой выборки составляет 20% от исходных данных. Разделенные данные сохраняются в переменные `X_train`, `X_test`, `y_train` и `y_test`. Задаем имена столбцов датасета в переменной `column_names`, которые будем использовать для вывода данных. Далее для ранжирования важности параметров создаем экземпляры модели линейной регрессии `LinearRegression()` и модели RFE, которую обучаем на обучающих данных с помощью метода `fit()`. Для сортировки полученных результатов воспользуемся функцией `rank_to_dict_rfe(ranking, names)` из прошлой лабораторной работы. + +```python +def recursive_feature_elimination(): + df = data.copy() + df.drop(["Country", "location"], axis=1, inplace=True) + X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.2) + column_names = ['Qualifications', 'Work Type', 'Company Size', 'Preference', 'Job Title', 'Role', 'Job Portal', + 'skills', 'Company', 'Min Experience', 'Max Experience', 'Min Salary', + 'Max Salary', 'Sector', 'Industry', 'City', 'State', 'Ticker', 'year', 'month', 'day', + "'Casual Dress Code, Social and Recreational Activities, Employee Referral Programs, Health and Wellness Facilities, Life and Disability Insurance'", + "'Childcare Assistance, Paid Time Off (PTO), Relocation Assistance, Flexible Work Arrangements, Professional Development'", + "'Employee Assistance Programs (EAP), Tuition Reimbursement, Profit-Sharing, Transportation Benefits, Parental Leave'", + "'Employee Referral Programs, Financial Counseling, Health and Wellness Facilities, Casual Dress Code, Flexible Spending Accounts (FSAs)'", + "'Flexible Spending Accounts (FSAs), Relocation Assistance, Legal Assistance, Employee Recognition Programs, Financial Counseling'", + "'Health Insurance, Retirement Plans, Flexible Work Arrangements, Employee Assistance Programs (EAP), Bonuses and Incentive Programs'", + "'Health Insurance, Retirement Plans, Paid Time Off (PTO), Flexible Work Arrangements, Employee Assistance Programs (EAP)'", + "'Legal Assistance, Bonuses and Incentive Programs, Wellness Programs, Employee Discounts, Retirement Plans'", + "'Life and Disability Insurance, Stock Options or Equity Grants, Employee Recognition Programs, Health Insurance, Social and Recreational Activities'", + "'Transportation Benefits, Professional Development, Bonuses and Incentive Programs, Profit-Sharing, Employee Discounts'", + "'Tuition Reimbursement, Stock Options or Equity Grants, Parental Leave, Wellness Programs, Childcare Assistance'"] + + estimator = LinearRegression() + rfe_model = RFE(estimator) + rfe_model.fit(X_train.values, y_train.values) + ranks = rank_to_dict_rfe(rfe_model.ranking_, column_names) + sorted_dict = dict(sorted(ranks.items(), key=lambda x: x[1], reverse=True)) + print(sorted_dict) + +def rank_to_dict_rfe(ranking, names): + n_ranks = [float(1 / i) for i in ranking] + n_ranks = map(lambda x: round(x, 2), n_ranks) + return dict(zip(names, n_ranks)) +``` +После запуска функции `recursive_feature_elimination()`, получаем следующий результат: +``` +{'Qualifications': 1.0, 'Work Type': 1.0, 'Preference': 1.0, 'Job Portal': 1.0, 'Min Experience': 1.0, "'Casual Dress Code, Social and Recreational Activities, Employee Referral Programs, Health and Wellness Facilities, Life and Disability Insurance'": 1.0, "'Childcare Assistance, Paid Time Off (PTO), Relocation Assistance, Flexible Work Arrangements, Professional Development'": 1.0, "'Employee Assistance Programs (EAP), Tuition Reimbursement, Profit-Sharing, Transportation Benefits, Parental Leave'": 1.0, "'Employee Referral Programs, Financial Counseling, Health and Wellness Facilities, Casual Dress Code, Flexible Spending Accounts (FSAs)'": 1.0, "'Flexible Spending Accounts (FSAs), Relocation Assistance, Legal Assistance, Employee Recognition Programs, Financial Counseling'": 1.0, "'Health Insurance, Retirement Plans, Flexible Work Arrangements, Employee Assistance Programs (EAP), Bonuses and Incentive Programs'": 1.0, "'Health Insurance, Retirement Plans, Paid Time Off (PTO), Flexible Work Arrangements, Employee Assistance Programs (EAP)'": 1.0, "'Legal Assistance, Bonuses and Incentive Programs, Wellness Programs, Employee Discounts, Retirement Plans'": 1.0, "'Life and Disability Insurance, Stock Options or Equity Grants, Employee Recognition Programs, Health Insurance, Social and Recreational Activities'": 1.0, "'Transportation Benefits, Professional Development, Bonuses and Incentive Programs, Profit-Sharing, Employee Discounts'": 1.0, "'Tuition Reimbursement, Stock Options or Equity Grants, Parental Leave, Wellness Programs, Childcare Assistance'": 1.0, 'month': 0.5, 'year': 0.33, 'Max Experience': 0.25, 'State': 0.2, 'day': 0.17, 'Sector': 0.14, 'Company': 0.12, 'Ticker': 0.11, 'Job Title': 0.1, 'Role': 0.09, 'Industry': 0.08, 'City': 0.08, 'skills': 0.07, 'Min Salary': 0.07, 'Company Size': 0.06, 'Max Salary': 0.06} +``` +Как можно заметить, наиболее важными параметрами являются 'Qualifications': 1.0, 'Work Type': 1.0, 'Preference': 1.0, 'Job Portal': 1.0, 'Min Experience': 1.0 и все показатели льгот. + +#### Оценка количества кластеров +Для оценки количества кластеров воспользуемся методом локтя - графический метод для оценки оптимального количества кластеров при использовании алгоритма k-means. Данный метод основан на расчете суммы квадратов расстояний между каждым объектом данных и центроидом его кластера. Эта сумма называется инерцией. Чем меньше инерция, тем лучше кластеризация. + +Сначала создаем копию данных `df`, после чего удаляем ненужных столбцов из `df` с помощью метода `drop()`. Затем данные разделяем на обучающую и тестовую выборки с помощью функции `train_test_split()`. +Далее инициализируем пустой список `inertias`, который будет хранить значения инерции для различного количества кластеров. В цикле for перебираем значения `k` от 1 до 14. Внутри цикла создаем объект KMeans с параметром `n_clusters=k`, который выполняет кластеризацию обучающей выборки. Значение инерции для текущего количества кластеров добавляется в список `inertias`. +После завершения цикла строим график метода локтя, где по оси *x* отображается количество кластеров, а по оси *y* - значение инерции. + +С помощью графика выберем точку, где изменение инерции становится менее значительным по сравнению с предыдущими значениями. + +``` python +df = data.copy() + df.drop(['Country', 'location', 'Company Size', 'Job Title', 'Role', + 'skills', 'Company', 'Max Experience', 'Min Salary', + 'Max Salary', 'Sector', 'Industry', 'City', 'State', 'Ticker', 'year', 'month', 'day' + ], + axis=1, inplace=True) + X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.2) + inertias = [] + for k in range(1, 15): + kmeans = KMeans(n_clusters=k, random_state=1).fit(X_train.values, y_train.values) + inertias.append(np.sqrt(kmeans.inertia_)) + plt.plot(range(1, 15), inertias, marker='o') + plt.xlabel('Number of clusters') + plt.ylabel('Inertia') + plt.title("Метод локтя") + plt.savefig('static/charts/ElbowMethod.png') + plt.close() +``` +Выполним построение графика: + + +![График "Метод локтя"](ElbowMethod.png) + +Таким образом, с помощью метода локтя получилось визуально определить оптимальное количество кластеров для алгоритма k-means на основе значения инерции, равное 9. + +#### Алгоритм кластеризации *K-means* +Для работы с алгоритмом кластеризации *K-means* создадим функцию `k_means()` . Сохраняем копию оригинальных данных в переменной `df`. Затем, из этой копии удаляем столбцы, которые имеют наименьшую важность. Далее, данные разделяем на обучающую и тестовую выборки с помощью функции `train_test_split`. Обучающие данные сохраняются в переменные `X_train` и `y_train`, а тестовые данные - в переменные `X_test` и `y_test`. Здесь `y` представляет собой целевую переменную. Затем, создаем объект `kmeans` класса KMeans с параметром `n_clusters=9`, что означает, что алгоритм будет искать 9 кластеров в данных. Обучение модели выполняется с помощью метода `fit`, передавая в него значения `X_train` и `y_train`.Далее, применяется обученная модель к тестовым данным с помощью метода `predict`, чтобы получить метки кластеров. Координаты центроидов кластеров сохраняются в переменной `centroids`. + +```python +def k_means(): + df = data.copy() + df.drop(['Country', 'location', 'Company Size', 'Preference', 'Job Title', 'Role', 'Job Portal', + 'skills', 'Company', 'Min Experience', 'Max Experience', 'Min Salary', + 'Max Salary', 'Sector', 'Industry', 'City', 'State', 'Ticker', 'year', 'month', 'day', + "'Casual Dress Code, Social and Recreational Activities, Employee Referral Programs, Health and Wellness Facilities, Life and Disability Insurance'", + "'Childcare Assistance, Paid Time Off (PTO), Relocation Assistance, Flexible Work Arrangements, Professional Development'", + "'Employee Assistance Programs (EAP), Tuition Reimbursement, Profit-Sharing, Transportation Benefits, Parental Leave'", + "'Employee Referral Programs, Financial Counseling, Health and Wellness Facilities, Casual Dress Code, Flexible Spending Accounts (FSAs)'", + "'Flexible Spending Accounts (FSAs), Relocation Assistance, Legal Assistance, Employee Recognition Programs, Financial Counseling'", + "'Health Insurance, Retirement Plans, Flexible Work Arrangements, Employee Assistance Programs (EAP), Bonuses and Incentive Programs'", + "'Health Insurance, Retirement Plans, Paid Time Off (PTO), Flexible Work Arrangements, Employee Assistance Programs (EAP)'", + "'Legal Assistance, Bonuses and Incentive Programs, Wellness Programs, Employee Discounts, Retirement Plans'", + "'Life and Disability Insurance, Stock Options or Equity Grants, Employee Recognition Programs, Health Insurance, Social and Recreational Activities'", + "'Transportation Benefits, Professional Development, Bonuses and Incentive Programs, Profit-Sharing, Employee Discounts'", + "'Tuition Reimbursement, Stock Options or Equity Grants, Parental Leave, Wellness Programs, Childcare Assistance'"], + axis=1, inplace=True) + X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.2) + kmeans = KMeans(n_clusters=9) + kmeans.fit(X_train.values, y_train.values) + labels = kmeans.predict(X_test.values) + centroids = kmeans.cluster_centers_ + print("Метки кластеров:", labels) + print("Координаты центроидов:", centroids) + plt.scatter(X_test['Qualifications'], X_test['Work Type'], c=labels, cmap='viridis') + plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', color='red') + plt.xlabel('Qualifications') + plt.ylabel('Work Type') + plt.title('KMeans Clustering') + plt.savefig('static/charts/KMeansClustering.png') + plt.close() + + print("Уникальных Work Type :", data['Work Type'].nunique()) + print("Уникальных Qualifications:", data['Qualifications'].nunique()) + + unique_labels = np.unique(labels) + for label in unique_labels: + indices = np.where(labels == label) + y_values = data_orig.loc[indices, 'Country'] + print(f"Значения y для кластера {label}: {y_values}") +``` +Выполним построение графика: + + +![График "Алгоритм кластеризации K-means"](KMeansClustering.png) + +Также выводим результаты работы алгоритма кластеризации в консоль, где можно увидеть подробные результаты разбиения на кластеры: + +``` +Значения y для кластера 0: 9 Antigua and Barbuda +16 San Marino +21 Tuvalu +22 Eritrea +35 New Caledonia + ... +319899 Nepal +319912 Uzbekistan +319927 Colombia +319933 Spain +319955 Niger +Name: Country, Length: 25672, dtype: object +Значения y для кластера 1: 10 Bahrain +14 Syrian Arab Republic +19 Democratic Republic Of Congo +31 Chile +40 Cote d'Ivoire + ... +319904 Mauritania +319905 Macao SAR, China +319906 Vietnam +319929 French Polynesia +319945 Mauritius +Name: Country, Length: 31971, dtype: object +Значения y для кластера 2: 7 Sao Tome and Principe +15 Yemen +17 French Polynesia +20 Azerbaijan +24 British Virgin Islands + ... +319935 Dominican Republic +319937 Japan +319938 Gambia +319946 Indonesia +319949 Latvia +Name: Country, Length: 38400, dtype: object +Значения y для кластера 3: 0 Isle of Man +6 Cayman Islands +11 Bermuda +23 Honduras +30 Jordan + ... +319931 Sint Maarten (Dutch part) +319942 Hong Kong SAR, China +319943 Bangladesh +319950 Croatia +319952 Syrian Arab Republic +Name: Country, Length: 51148, dtype: object +Значения y для кластера 4: 3 Benin +4 Chile +5 Belgium +12 Jamaica +18 North Korea + ... +319922 Gabon +319925 Korea, Rep. +319926 Ethiopia +319941 Zimbabwe +319951 Korea, Rep. +Name: Country, Length: 38574, dtype: object +Значения y для кластера 5: 1 Turkmenistan +38 Solomon Islands +42 Virgin Islands (U.S.) +47 Belize +59 Republic Of Congo + ... +319918 Spain +319924 Paraguay +319947 Brazil +319948 Macao SAR, China +319953 China +Name: Country, Length: 38422, dtype: object +Значения y для кластера 6: 37 West Bank and Gaza +57 Guam +102 Fiji +111 Ecuador +124 Cabo Verde + ... +319896 Sint Maarten (Dutch part) +319903 Madagascar +319914 South Africa +319940 Netherlands +319954 Albania +Name: Country, Length: 25701, dtype: object +Значения y для кластера 7: 2 Macao SAR, China +8 Maldives +13 Gambia +25 Cuba +27 Gambia + ... +319892 Turks and Caicos Islands +319920 Luxembourg +319923 Trinidad and Tobago +319939 China +319944 Fiji +Name: Country, Length: 38449, dtype: object +Значения y для кластера 8: 48 Malta +49 Panama +52 Republic Of Congo +81 Bangladesh +82 Nepal + ... +319902 Cambodia +319921 Barbados +319932 Lesotho +319934 St. Kitts and Nevis +319936 Nigeria +Name: Country, Length: 31619, dtype: object +``` + +Также выведем координаты центроидов: +``` +Координаты центроидов: [[2.49909662 0.4986962 ] + [6.49757212 3.00005221] + [1.12332665 3.12941867] + [6.79715885 0.40238426] + [3.99726263 3.50030885] + [8.50117367 3.49953735] + [4.2877207 1.14426123] + [8.60271303 1.19776253] + [0.40101301 0.79759215]] +``` + +И оценим качество кластеризации, используя силуэтный коэффициент и индекс Дэвиса-Болдина: +1. Силуэтный коэффициент - это метрика, которая измеряет, насколько точка хорошо соответствует своему собственному кластеру в сравнении с другими кластерами. Он находится в диапазоне от -1 до 1, где значение ближе к 1 указывает на хорошую кластеризацию, а значение ближе к -1 указывает на плохую кластеризацию. +```python + # Оценка силуэтного коэффициента + silhouette = silhouette_score(X_test.values, kmeans.predict(X_test.values)) + print("Силуэтный коэффициент:", silhouette) +``` +В нашем случае силуэтный коэффициент равен 0.4086103390706535. +2. Индекс Дэвиса-Болдина - это метрика, которая измеряет сходство между кластерами на основе их средних расстояний и средних расстояний между кластерами. Чем меньше значение этого индекса, тем лучше кластеризация. +```python + # Оценка индекса Дэвиса-Болдина + davies_bouldin = davies_bouldin_score(X_test.values, kmeans.predict(X_test.values)) + print("Индекс Дэвиса-Болдина:", davies_bouldin) +``` +В нашем случае индекс Дэвиса-Болдина равен 0.8682047121172671. + +### Вывод + +Таким образом, с помощью оценок силуэтного коэффициента и индекса Дэвиса-Болдина, можно сделать следующие выводы о качестве кластеризации: +1. Силуэтный коэффициент равен 0.4086103390706535. В данном случае, значение силуэтного коэффициента выше 0, что говорит о том, что кластеры имеют некоторую степень разделения, но не являются идеально разделимыми. В целом, это может указывать на некоторое качество кластеризации, но не является оптимальным. +2. Индекс Дэвиса-Болдина равен 0.8682047121172671. Значение этого индекса является положительным числом. В данном случае, значение индекса Дэвиса-Болдина выше 0, что указывает на некоторое сходство между кластерами. + +Таким образом, на основе предоставленных значений можно сказать, что кластеризация имеет некоторую степень разделения, но не является идеальной. \ No newline at end of file diff --git a/kochkareva_elizaveta_lab_4/main.py b/kochkareva_elizaveta_lab_4/main.py new file mode 100644 index 0000000..19178ad --- /dev/null +++ b/kochkareva_elizaveta_lab_4/main.py @@ -0,0 +1,122 @@ +import os.path +import numpy as np +import pandas as pd +from sklearn.cluster import KMeans +from sklearn.metrics import silhouette_score, davies_bouldin_score +from sklearn.model_selection import train_test_split +from sklearn.linear_model import LinearRegression +from sklearn.feature_selection import RFE +import matplotlib.pyplot as plt + + +picfld = os.path.join('static', 'charts') + +data = pd.read_csv('D:/Интеллектуальные информационные системы/Dataset/updated_job_descriptions.csv') +data_orig = pd.read_csv('D:/Интеллектуальные информационные системы/Dataset/job_descriptions.csv') +y = data['Country'] + + +def k_means(): + df = data.copy() + df.drop(['Country', 'location', 'Company Size', 'Preference', 'Job Title', 'Role', 'Job Portal', + 'skills', 'Company', 'Min Experience', 'Max Experience', 'Min Salary', + 'Max Salary', 'Sector', 'Industry', 'City', 'State', 'Ticker', 'year', 'month', 'day', + "'Casual Dress Code, Social and Recreational Activities, Employee Referral Programs, Health and Wellness Facilities, Life and Disability Insurance'", + "'Childcare Assistance, Paid Time Off (PTO), Relocation Assistance, Flexible Work Arrangements, Professional Development'", + "'Employee Assistance Programs (EAP), Tuition Reimbursement, Profit-Sharing, Transportation Benefits, Parental Leave'", + "'Employee Referral Programs, Financial Counseling, Health and Wellness Facilities, Casual Dress Code, Flexible Spending Accounts (FSAs)'", + "'Flexible Spending Accounts (FSAs), Relocation Assistance, Legal Assistance, Employee Recognition Programs, Financial Counseling'", + "'Health Insurance, Retirement Plans, Flexible Work Arrangements, Employee Assistance Programs (EAP), Bonuses and Incentive Programs'", + "'Health Insurance, Retirement Plans, Paid Time Off (PTO), Flexible Work Arrangements, Employee Assistance Programs (EAP)'", + "'Legal Assistance, Bonuses and Incentive Programs, Wellness Programs, Employee Discounts, Retirement Plans'", + "'Life and Disability Insurance, Stock Options or Equity Grants, Employee Recognition Programs, Health Insurance, Social and Recreational Activities'", + "'Transportation Benefits, Professional Development, Bonuses and Incentive Programs, Profit-Sharing, Employee Discounts'", + "'Tuition Reimbursement, Stock Options or Equity Grants, Parental Leave, Wellness Programs, Childcare Assistance'"], + axis=1, inplace=True) + X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.2) + kmeans = KMeans(n_clusters=9) + kmeans.fit(X_train.values) + labels = kmeans.predict(X_test.values) + centroids = kmeans.cluster_centers_ + print("Координаты центроидов:", centroids) + plt.scatter(X_test['Qualifications'], X_test['Work Type'], c=labels, cmap='viridis') + plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', color='red') + plt.xlabel('Qualifications') + plt.ylabel('Work Type') + plt.title('KMeans Clustering') + plt.savefig('static/charts/KMeansClustering.png') + plt.close() + print("Уникальных Work Type :", data['Work Type'].nunique()) + print("Уникальных Qualifications:", data['Qualifications'].nunique()) + unique_labels = np.unique(labels) + for label in unique_labels: + indices = np.where(labels == label) + y_values = data_orig.loc[indices, 'Country'].values + print(f"Значения y для кластера {label}: {y_values}") + # Оценка силуэтного коэффициента + silhouette = silhouette_score(X_test.values, kmeans.predict(X_test.values)) + print("Силуэтный коэффициент:", silhouette) + # Оценка индекса Дэвиса-Болдина + davies_bouldin = davies_bouldin_score(X_test.values, kmeans.predict(X_test.values)) + print("Индекс Дэвиса-Болдина:", davies_bouldin) + + +# оценка количества кластеров +def selection_number_clusters(): + df = data.copy() + df.drop(['Country', 'location', 'Company Size', 'Job Title', 'Role', + 'skills', 'Company', 'Max Experience', 'Min Salary', + 'Max Salary', 'Sector', 'Industry', 'City', 'State', 'Ticker', 'year', 'month', 'day' + ], + axis=1, inplace=True) + X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.2) + inertias = [] + for k in range(1, 15): + kmeans = KMeans(n_clusters=k, random_state=1).fit(X_train.values, y_train.values) + inertias.append(np.sqrt(kmeans.inertia_)) + plt.plot(range(1, 15), inertias, marker='o') + plt.xlabel('Number of clusters') + plt.ylabel('Inertia') + plt.title("Метод локтя") + plt.savefig('static/charts/ElbowMethod.png') + plt.close() + + +# оценка важности параметров +def recursive_feature_elimination(): + df = data.copy() + df.drop(["Country", "location"], axis=1, inplace=True) + X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.2) + column_names = ['Qualifications', 'Work Type', 'Company Size', 'Preference', 'Job Title', 'Role', 'Job Portal', + 'skills', 'Company', 'Min Experience', 'Max Experience', 'Min Salary', + 'Max Salary', 'Sector', 'Industry', 'City', 'State', 'Ticker', 'year', 'month', 'day', + "'Casual Dress Code, Social and Recreational Activities, Employee Referral Programs, Health and Wellness Facilities, Life and Disability Insurance'", + "'Childcare Assistance, Paid Time Off (PTO), Relocation Assistance, Flexible Work Arrangements, Professional Development'", + "'Employee Assistance Programs (EAP), Tuition Reimbursement, Profit-Sharing, Transportation Benefits, Parental Leave'", + "'Employee Referral Programs, Financial Counseling, Health and Wellness Facilities, Casual Dress Code, Flexible Spending Accounts (FSAs)'", + "'Flexible Spending Accounts (FSAs), Relocation Assistance, Legal Assistance, Employee Recognition Programs, Financial Counseling'", + "'Health Insurance, Retirement Plans, Flexible Work Arrangements, Employee Assistance Programs (EAP), Bonuses and Incentive Programs'", + "'Health Insurance, Retirement Plans, Paid Time Off (PTO), Flexible Work Arrangements, Employee Assistance Programs (EAP)'", + "'Legal Assistance, Bonuses and Incentive Programs, Wellness Programs, Employee Discounts, Retirement Plans'", + "'Life and Disability Insurance, Stock Options or Equity Grants, Employee Recognition Programs, Health Insurance, Social and Recreational Activities'", + "'Transportation Benefits, Professional Development, Bonuses and Incentive Programs, Profit-Sharing, Employee Discounts'", + "'Tuition Reimbursement, Stock Options or Equity Grants, Parental Leave, Wellness Programs, Childcare Assistance'"] + + estimator = LinearRegression() + rfe_model = RFE(estimator) + rfe_model.fit(X_train.values, y_train.values) + ranks = rank_to_dict_rfe(rfe_model.ranking_, column_names) + sorted_dict = dict(sorted(ranks.items(), key=lambda x: x[1], reverse=True)) + print(sorted_dict) + + +def rank_to_dict_rfe(ranking, names): + n_ranks = [float(1 / i) for i in ranking] + n_ranks = map(lambda x: round(x, 2), n_ranks) + return dict(zip(names, n_ranks)) + + +if __name__ == '__main__': + # selection_number_clusters() + # recursive_feature_elimination() + k_means() diff --git a/kochkareva_elizaveta_lab_4/static/charts/ElbowMethod.png b/kochkareva_elizaveta_lab_4/static/charts/ElbowMethod.png new file mode 100644 index 0000000000000000000000000000000000000000..aaf455507e5aace02e57bc74bf52f07b994a7ca9 GIT binary patch literal 23777 zcmeFZby!y4`!2ZYR9cW05kZh{X#o)s0qHJLX_4+O1q38iN)YLkZUG5FLO?`1M5Pf0 zl$vMriSO^6IcMg~nQQ(VF1_G=-~Fz=*4k@5ao_i|BGuKdog$XW+LWh} z)~_S!%`x+Uvq|PpZqtxzX=;#l7;C7!mrQOnFgSB>=Z3(8o(r$N_w!vTepTxy>DN@) zKArOJ)f8}8N_rJxQTw7cgs|crKNdb#U+N4~_aZ*~yVkQyjp?yn5|188Jy??5P7&pi 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