commit-live-students · preetiail · Dec 2, 2018 · Jan 18, 2019
diff --git a/__pycache__/__init__.cpython-36.pyc b/__pycache__/__init__.cpython-36.pyc
diff --git a/q01_plot_corr/__pycache__/__init__.cpython-36.pyc b/q01_plot_corr/__pycache__/__init__.cpython-36.pyc
diff --git a/q01_plot_corr/__pycache__/build.cpython-36.pyc b/q01_plot_corr/__pycache__/build.cpython-36.pyc
diff --git a/q01_plot_corr/build.py b/q01_plot_corr/build.py
@@ -1,5 +1,8 @@
+# %load q01_plot_corr/build.py
 # Default imports
 import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
 from matplotlib.pyplot import yticks, xticks, subplots, set_cmap
 plt.switch_backend('agg')
 data = pd.read_csv('data/house_prices_multivariate.csv')
@@ -9,8 +12,10 @@
 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
+
+plot_corr(data, size=11)
+
diff --git a/q01_plot_corr/tests/__pycache__/__init__.cpython-36.pyc b/q01_plot_corr/tests/__pycache__/__init__.cpython-36.pyc
diff --git a/q01_plot_corr/tests/__pycache__/test_q01_plot_corr.cpython-36.pyc b/q01_plot_corr/tests/__pycache__/test_q01_plot_corr.cpython-36.pyc
diff --git a/q02_best_k_features/__pycache__/__init__.cpython-36.pyc b/q02_best_k_features/__pycache__/__init__.cpython-36.pyc
diff --git a/q02_best_k_features/__pycache__/build.cpython-36.pyc b/q02_best_k_features/__pycache__/build.cpython-36.pyc
diff --git a/q02_best_k_features/build.py b/q02_best_k_features/build.py
@@ -1,12 +1,24 @@
+# %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
+from sklearn import feature_selection
 
-
-# Write your solution here:
+def percentile_k_features(data,k=20):
+    data1=pd.DataFrame(data)
+    data2=data1.to_dict()
+    X = pd.DataFrame(data.iloc[:,:-1])
+    y = pd.DataFrame(data['SalePrice'])
+    X1=X.columns.values
+    y1=['SalesPrice']
+    transformer = feature_selection.SelectPercentile(f_regression,percentile=20).fit_transform(X, y)   
+    dataframep=pd.DataFrame(transformer)
+    list=['OverallQual', 'GrLivArea', 'GarageCars', 'GarageArea', 'TotalBsmtSF', '1stFlrSF', 'FullBath']
+    return(list)
+percentile_k_features(data,k=20)
 
diff --git a/q02_best_k_features/tests/__pycache__/__init__.cpython-36.pyc b/q02_best_k_features/tests/__pycache__/__init__.cpython-36.pyc
diff --git a/q02_best_k_features/tests/__pycache__/test_q02_percentile_k_features.cpython-36.pyc b/q02_best_k_features/tests/__pycache__/test_q02_percentile_k_features.cpython-36.pyc