fixed typo

02_regression/09_pca_boston.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Hauptkomponentenanalyse mit dem Bostong Housing-Datensatz"
+    "# Hauptkomponentenanalyse mit dem Boston Housing-Datensatz"
    ]
   },
   {
 %% Cell type:markdown id: tags:
 
-# Hauptkomponentenanalyse mit dem Bostong Housing-Datensatz
+# Hauptkomponentenanalyse mit dem Boston Housing-Datensatz
 
 %% Cell type:markdown id: tags:
 
 ### Daten einlesen
 
 %% Cell type:code id: tags:
 
 ``` python
 from sklearn.datasets import load_boston
 boston = load_boston()
 
 import pandas as pd, numpy as np
 pd.set_option('display.max_rows', 6)
 df = pd.DataFrame(data = np.c_[boston['data'], boston['target']], columns = boston['feature_names'].tolist() + ['TARGET'])
 df
 ```
 
 %%%% Output: execute_result
 
             CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \
     0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0
     1    0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0
     2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0
     ..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...
     503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0
     504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0
     505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0
     
          PTRATIO       B  LSTAT  TARGET
     0       15.3  396.90   4.98    24.0
     1       17.8  396.90   9.14    21.6
     2       17.8  392.83   4.03    34.7
     ..       ...     ...    ...     ...
     503     21.0  396.90   5.64    23.9
     504     21.0  393.45   6.48    22.0
     505     21.0  396.90   7.88    11.9
     
     [506 rows x 14 columns]
 
 %% Cell type:markdown id: tags:
 
 ### Matrix der Korrelationskoeffizienten
 
 %% Cell type:code id: tags:
 
 ``` python
 corr = df.corr()
 
 # generate a mask for the upper triangle
 mask = np.zeros_like(corr, dtype=np.bool); mask[np.triu_indices_from(mask)] = True
 
 # generate a custom colormap
 import seaborn as sns
 cmap = sns.diverging_palette(220, 10, as_cmap=True)
 
 %matplotlib inline
 sns.heatmap(corr, mask=mask, cmap=cmap, vmin=-1, vmax=1)
 ```
 
 %%%% Output: execute_result
 
     <AxesSubplot:>
 
 %%%% Output: display_data
 
 
 %% Cell type:markdown id: tags:
 
 ### Standardisierung und Hauptkomponentenanalyse
 
 %% Cell type:code id: tags:
 
 ``` python
 from sklearn.preprocessing import StandardScaler
 x = StandardScaler().fit_transform(boston['data'])
 
 from sklearn.decomposition import PCA
 pca = PCA(n_components = 3)
 xd = pca.fit_transform(x)
 pca.components_.shape
 ```
 
 %%%% Output: execute_result
 
     (3, 13)
 
 %% Cell type:code id: tags:
 
 ``` python
 print( "Erklärter Anteil der Varianz je Hauptkomponente = ", pca.explained_variance_ratio_ )
 sum(pca.explained_variance_ratio_)
 ```
 
 %%%% Output: stream
 
     Erklärter Anteil der Varianz je Hauptkomponente =  [0.47129606 0.11025193 0.0955859 ]
 
 %%%% Output: execute_result
 
     0.6771338939748568
 
 %% Cell type:markdown id: tags:
 
 ### Beitrag je Merkmal zu den Hauptkomponenten
 
 %% Cell type:code id: tags:
 
 ``` python
 import matplotlib.pyplot as plt
 plt.matshow(pca.components_, cmap=cmap)
 plt.yticks([0, 1, 2], ["PC1", "PC2", "PC3"])
 plt.xticks(range(len(boston.feature_names)), boston.feature_names)
 plt.colorbar()
 ```
 
 %%%% Output: execute_result
 
     <matplotlib.colorbar.Colorbar at 0x268ff204c70>
 
 %%%% Output: display_data