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q01_plot_corr/build.py

@@ -1,16 +1,22 @@
+# %load q01_plot_corr/build.py
 # Default imports
 import pandas as pd
 from matplotlib.pyplot import yticks, xticks, subplots, set_cmap
-plt.switch_backend('agg')
+import matplotlib.pyplot 
+#plt.switch_backend('agg')
 data = pd.read_csv('data/house_prices_multivariate.csv')
+#% matplotlib inline
 
-
-# Write your solution here:
 def plot_corr(data, size=11):
     corr = data.corr()
     fig, ax = subplots(figsize=(size, size))
-    set_cmap("YlOrRd")
+    set_cmap('YlOrRd')
     ax.matshow(corr)
     xticks(range(len(corr.columns)), corr.columns, rotation=90)
     yticks(range(len(corr.columns)), corr.columns)
     return ax
+
+
+
+
+

q02_best_k_features/build.py

@@ -1,12 +1,31 @@
+# %load q02_best_k_features/build.py
 # Default imports
 
 import pandas as pd
-
+import numpy as np
 data = pd.read_csv('data/house_prices_multivariate.csv')
 
 from sklearn.feature_selection import SelectPercentile
 from sklearn.feature_selection import f_regression
 
+def percentile_k_features(df,k = 20):
+    X = df.drop(['SalePrice'], axis = 1)
+    y = df['SalePrice']
+
+    #regression model and transform method on predictors and target
+    selector = SelectPercentile(f_regression, k)
+    X_new = selector.fit_transform(X, y)
+
+
+    #list of best features with implementation of k percentile method
+    featurelist = list(X.columns.values[np.argsort(selector.scores_)
+                                        [-1:-X_new.shape[1]-1:-1]])
+
+    return featurelist
+
+
+
+
+ #percentile_k_features(df, 20)
 
-# Write your solution here:
 

q03_rf_rfe/build.py

@@ -1,3 +1,4 @@
+# %load q03_rf_rfe/build.py
 # Default imports
 import pandas as pd
 
@@ -9,3 +10,18 @@
 
 # Your solution code here
 
+def rf_rfe(df):
+    X = df.drop('SalePrice',axis=1)
+    y = df['SalePrice']
+
+    model = RandomForestClassifier()
+    rfe = RFE(model,n_features_to_select=len(X.columns)/2)
+    rfe = rfe.fit(X,y)
+
+    return list(X.columns[rfe.support_])
+
+
+
+rf_rfe(data)
+
+

q04_select_from_model/build.py

@@ -1,3 +1,4 @@
+# %load q04_select_from_model/build.py
 # Default imports
 from sklearn.feature_selection import SelectFromModel
 from sklearn.ensemble import RandomForestClassifier
@@ -8,3 +9,19 @@
 
 
 # Your solution code here
+def select_from_model(data):
+    X = data.drop('SalePrice',axis=1)
+    y = data['SalePrice']
+
+    model = RandomForestClassifier()
+
+    sfm = SelectFromModel(model)
+    sfm.fit_transform(X,y)
+
+    feature_name = list(X.columns[sfm.get_support()])
+
+    return feature_name
+
+select_from_model(data)
+
+