46 lines
1.3 KiB
Python
46 lines
1.3 KiB
Python
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#! /usr/bin/env python3
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# -*- coding: utf-8 -*-
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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from seaborn import relplot
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from sklearn.cluster import KMeans
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from sklearn.decomposition import PCA
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data = pd.read_csv('mieszkania4.tsv', sep='\t')
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data = data[
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(data['Powierzchnia w m2'] < 10000)
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& (data['cena'] < 10000000)
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]
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X_columns = [
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'cena',
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'Powierzchnia w m2',
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'Liczba pokoi',
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'Liczba pięter w budynku',
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'Piętro',
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]
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data['Piętro'] = data['Piętro'].apply(lambda x: 0 if x in ['parter', 'niski parter'] else x)
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data['Piętro'] = data['Piętro'].apply(pd.to_numeric, errors='coerce')
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data=data[:100]
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X = data[X_columns].dropna().reset_index().drop(['index'], axis=1)
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kmeans = KMeans(n_clusters=5).fit(X.values)
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labels = pd.DataFrame(kmeans.labels_, columns=['label'])
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labeled = pd.concat([labels, X], axis=1)
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print(labeled)
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relplot(data=labeled, x='Liczba pokoi', y='Piętro', hue='label')
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relplot(data=labeled, x='Powierzchnia w m2', y='cena', hue='label')
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plt.show()
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pca = PCA(n_components=2)
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pca.fit(X)
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X_transformed = pd.DataFrame(pca.transform(X), columns=['x1', 'x2'])
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labeled_transformed = pd.concat([labels, X_transformed, X], axis=1)
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relplot(data=labeled_transformed, x='x1', y='x2', hue='label')
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plt.show()
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