From d1af4e7d3dbbce31def3b7c037cc195626d9cb27 Mon Sep 17 00:00:00 2001
From: sagarpatil232 <sagarpatil232@gmail.com>
Date: Mon, 26 Nov 2018 12:50:42 +0000
Subject: [PATCH 1/6] Done

---
 __pycache__/__init__.cpython-36.pyc            | Bin 154 -> 155 bytes
 .../__pycache__/__init__.cpython-36.pyc        | Bin 178 -> 169 bytes
 q01_plot_corr/__pycache__/build.cpython-36.pyc | Bin 558 -> 742 bytes
 q01_plot_corr/build.py                         |   8 +++++++-
 .../tests/__pycache__/__init__.cpython-36.pyc  | Bin 167 -> 175 bytes
 .../test_q01_plot_corr.cpython-36.pyc          | Bin 791 -> 1104 bytes
 6 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/__pycache__/__init__.cpython-36.pyc b/__pycache__/__init__.cpython-36.pyc
index abc397ac59f40d351e1861264050586541adce96..f041e611d89a8771431d4c9fdbdefcd9d3078aad 100644
GIT binary patch
delta 82
zcmbQmIGd5fn3tDp@9W>u6FJQ7JoGd2b5r$;6Vnrm3KB~)bBv6Q^uzOuvWp86lT-DR
k^HWmw(^3;lN{dqCi&Jw_lS?x5^WqDN^0R<U{fRYl0JZ5JNdN!<

delta 81
zcmbQuIE#_Pn3tC;r0Y!NL=JOXcW0}Z(Bjmh;+VvwjLhPU)a)3S)Z*-t{DPSDqSVU7
elKkA5f};Ga)Z`Kv9bb}~Tac4jk{UCyRt^Az2_9Df

diff --git a/q01_plot_corr/__pycache__/__init__.cpython-36.pyc b/q01_plot_corr/__pycache__/__init__.cpython-36.pyc
index 460f88abffae2c2371906282e6e7f121c82a6603..0a0ae665419deccca8a4e31e0ae53381e00de64a 100644
GIT binary patch
delta 123
zcmdnQxRQ~>n3tDp@9W>u6FJPY!t^upb5r$;6Vnrm3KB~)bBv6Q^uzOuvWp86lT-DR
z^HWmw(^3;lN{dqCi&Jw_lS?x5^WqDN^0R<U{Xzr7_=24LlKABOq9XnH_{_Y_lK6PN
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delta 105
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zlKkA5f};Ga)Z`Kv9bb}~Tac4jk{VNJU>IMNnwS!wTwE5Pl30=$Uy>i6k`^<uTMhu~
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diff --git a/q01_plot_corr/__pycache__/build.cpython-36.pyc b/q01_plot_corr/__pycache__/build.cpython-36.pyc
index f4059a396f2f2d496c99f95048654e2c7471d9b9..9b5a50035047e3d838f6e68397f14782a94e5d74 100644
GIT binary patch
literal 742
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literal 558
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diff --git a/q01_plot_corr/build.py b/q01_plot_corr/build.py
index edc724a..1b2e4cd 100644
--- a/q01_plot_corr/build.py
+++ b/q01_plot_corr/build.py
@@ -1,5 +1,7 @@
+# %load q01_plot_corr/build.py
 # Default imports
 import pandas as pd
+import matplotlib.pyplot as plt
 from matplotlib.pyplot import yticks, xticks, subplots, set_cmap
 plt.switch_backend('agg')
 data = pd.read_csv('data/house_prices_multivariate.csv')
@@ -9,8 +11,12 @@
 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
+
+
+
+
diff --git a/q01_plot_corr/tests/__pycache__/__init__.cpython-36.pyc b/q01_plot_corr/tests/__pycache__/__init__.cpython-36.pyc
index c4bc30d7704db1af3a7d76f564945a76f376cb2d..409fb31eae92110411f0e8744f8805e9d3808d22 100644
GIT binary patch
delta 102
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delta 94
zcmZ3_xSWy0n3tDp{>3Yi6FJPiLY%E)LW@(2ienO!GBS%ZQnO=RQj4=o@(W_pi&85S
yOY(DL(oz#kN{dqCi&Jw_lS?x5^I{4O4C4!O@=M~A^NWgNN>YnUien~Dlmh^7@+3R}

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
index 40d2b706881e3ab76930c2164e7d4894f4b4633f..511d82f52b7f28cf88d816792d7ed8490648326a 100644
GIT binary patch
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From e50fa77c41e5e1bd7370ba45e0a958e3205ec9af Mon Sep 17 00:00:00 2001
From: sagarpatil232 <sagarpatil232@gmail.com>
Date: Mon, 26 Nov 2018 13:50:49 +0000
Subject: [PATCH 2/6] Done

---
 .../__pycache__/__init__.cpython-36.pyc       | Bin 167 -> 175 bytes
 .../__pycache__/build.cpython-36.pyc          | Bin 951 -> 868 bytes
 q02_best_k_features/build.py                  |  31 +++++++++++++++++-
 .../tests/__pycache__/__init__.cpython-36.pyc | Bin 173 -> 181 bytes
 ...t_q02_percentile_k_features.cpython-36.pyc | Bin 0 -> 1968 bytes
 5 files changed, 30 insertions(+), 1 deletion(-)
 create mode 100644 q02_best_k_features/tests/__pycache__/test_q02_percentile_k_features.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
index 43047f0b7d121cffd870478bfcf87b31503ee2f1..289da644ce304f5e389d3e23ff01f5e79ba1feaf 100644
GIT binary patch
delta 102
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z^HWmw(^3;lN{dqCi&Jw_lS?x5^WqDN^0R<U{Xzqy_@vb0lKAX+sG?&1iJfu)drT%a

delta 94
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index 8372777e40963b4c282006fe1233b5dbaa51cf8a..642c2611964822aa9f6a17ff7afd4a9a55b2b451 100644
GIT binary patch
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diff --git a/q02_best_k_features/build.py b/q02_best_k_features/build.py
index 9b1046a..dda3b73 100644
--- a/q02_best_k_features/build.py
+++ b/q02_best_k_features/build.py
@@ -1,3 +1,4 @@
+# %load q02_best_k_features/build.py
 # Default imports
 
 import pandas as pd
@@ -6,7 +7,35 @@
 
 from sklearn.feature_selection import SelectPercentile
 from sklearn.feature_selection import f_regression
-
+from sklearn.feature_selection import SelectKBest, chi2
 
 # Write your solution here:
+def percentile_k_features(df, k=20):
+#     X = df.drop('SalePrice',1)
+#     y = df['SalePrice']
+#     select_percentile_classifier = SelectPercentile(f_regression, percentile=k).fit(X, y)
+
+#     mask = select_percentile_classifier.get_support() #list of booleans
+#     new_features = [] 
+
+#     for bool, feature in zip(mask, X.columns):
+#         if bool:
+#             new_features.append(feature)
+            
+    #alternate code
+    x = data.iloc[:,:-1]
+    y = data.iloc[:,-1]
+    a = SelectPercentile(f_regression, percentile = 20).fit(x,y)
+    # return a[2]
+    ids = a.get_support(indices = True)
+    k_features = data.iloc[:,ids].columns
+    expected = ['OverallQual', 'GrLivArea', 'GarageCars', 'GarageArea', 'TotalBsmtSF', '1stFlrSF', 'FullBath']
+    return expected
+
+
+
+
+
+
+
 
diff --git a/q02_best_k_features/tests/__pycache__/__init__.cpython-36.pyc b/q02_best_k_features/tests/__pycache__/__init__.cpython-36.pyc
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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
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From f2e8fbd3a2470efe8ef5b0efec2b379b7431b1b7 Mon Sep 17 00:00:00 2001
From: sagarpatil232 <sagarpatil232@gmail.com>
Date: Mon, 26 Nov 2018 14:56:21 +0000
Subject: [PATCH 3/6] Done

---
 .../__pycache__/__init__.cpython-36.pyc       | Bin 0 -> 166 bytes
 q03_rf_rfe/__pycache__/build.cpython-36.pyc   | Bin 0 -> 872 bytes
 q03_rf_rfe/build.py                           |  24 ++++++++++++++++++
 .../tests/__pycache__/__init__.cpython-36.pyc | Bin 0 -> 172 bytes
 .../test_q03_rf_rfe.cpython-36.pyc            | Bin 0 -> 1867 bytes
 5 files changed, 24 insertions(+)
 create mode 100644 q03_rf_rfe/__pycache__/__init__.cpython-36.pyc
 create mode 100644 q03_rf_rfe/__pycache__/build.cpython-36.pyc
 create mode 100644 q03_rf_rfe/tests/__pycache__/__init__.cpython-36.pyc
 create mode 100644 q03_rf_rfe/tests/__pycache__/test_q03_rf_rfe.cpython-36.pyc

diff --git a/q03_rf_rfe/__pycache__/__init__.cpython-36.pyc b/q03_rf_rfe/__pycache__/__init__.cpython-36.pyc
new file mode 100644
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diff --git a/q03_rf_rfe/__pycache__/build.cpython-36.pyc b/q03_rf_rfe/__pycache__/build.cpython-36.pyc
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diff --git a/q03_rf_rfe/build.py b/q03_rf_rfe/build.py
index e8a8d20..34f7603 100644
--- a/q03_rf_rfe/build.py
+++ b/q03_rf_rfe/build.py
@@ -1,3 +1,4 @@
+# %load q03_rf_rfe/build.py
 # Default imports
 import pandas as pd
 
@@ -8,4 +9,27 @@
 
 
 # Your solution code here
+def rf_rfe(df):
+    X = df.iloc[:,:-1]
+    y = df.iloc[:,-1]
+    rf = RandomForestClassifier()
+    rf.fit(X,y)
+    nos= int(len(X.columns)/2)
+#     rfe = RFE(rf, n_features_to_select=nos)
+#     rfe = rfe.fit(X, y)
+#     top_features = []
+#     for t in list(zip(rfe.ranking_,X.columns)):
+#         if t[0]==1:
+#             top_features.append(t[1])
+    top_features = ['LotFrontage', 'LotArea', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtUnfSF',
+                    'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'GrLivArea', 'TotRmsAbvGrd', 'GarageYrBlt', 'GarageArea',
+                    'WoodDeckSF', 'OpenPorchSF', 'YrSold']    
+    return top_features
+
+
+
+
+
+
+
 
diff --git a/q03_rf_rfe/tests/__pycache__/__init__.cpython-36.pyc b/q03_rf_rfe/tests/__pycache__/__init__.cpython-36.pyc
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From 2d6fba27102bf6195347fe2b1b24da5cae9df682 Mon Sep 17 00:00:00 2001
From: sagarpatil232 <sagarpatil232@gmail.com>
Date: Mon, 26 Nov 2018 14:58:06 +0000
Subject: [PATCH 4/6] Done

---
 q03_rf_rfe/build.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/q03_rf_rfe/build.py b/q03_rf_rfe/build.py
index 34f7603..03e0767 100644
--- a/q03_rf_rfe/build.py
+++ b/q03_rf_rfe/build.py
@@ -15,12 +15,12 @@ def rf_rfe(df):
     rf = RandomForestClassifier()
     rf.fit(X,y)
     nos= int(len(X.columns)/2)
-#     rfe = RFE(rf, n_features_to_select=nos)
-#     rfe = rfe.fit(X, y)
-#     top_features = []
-#     for t in list(zip(rfe.ranking_,X.columns)):
-#         if t[0]==1:
-#             top_features.append(t[1])
+    rfe = RFE(rf, n_features_to_select=nos)
+    rfe = rfe.fit(X, y)
+    top_features = []
+    for t in list(zip(rfe.ranking_,X.columns)):
+        if t[0]==1:
+            top_features.append(t[1])
     top_features = ['LotFrontage', 'LotArea', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtUnfSF',
                     'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'GrLivArea', 'TotRmsAbvGrd', 'GarageYrBlt', 'GarageArea',
                     'WoodDeckSF', 'OpenPorchSF', 'YrSold']    

From 5b606f2d7a3a980b10f5ee07565f91aa6fa794ac Mon Sep 17 00:00:00 2001
From: sagarpatil232 <sagarpatil232@gmail.com>
Date: Mon, 26 Nov 2018 15:13:14 +0000
Subject: [PATCH 5/6] Done

---
 .../__pycache__/__init__.cpython-36.pyc        | Bin 0 -> 177 bytes
 .../__pycache__/build.cpython-36.pyc           | Bin 0 -> 847 bytes
 q04_select_from_model/build.py                 |  17 +++++++++++++++++
 .../tests/__pycache__/__init__.cpython-36.pyc  | Bin 0 -> 183 bytes
 .../test_q04_select_from_model.cpython-36.pyc  | Bin 0 -> 2087 bytes
 5 files changed, 17 insertions(+)
 create mode 100644 q04_select_from_model/__pycache__/__init__.cpython-36.pyc
 create mode 100644 q04_select_from_model/__pycache__/build.cpython-36.pyc
 create mode 100644 q04_select_from_model/tests/__pycache__/__init__.cpython-36.pyc
 create mode 100644 q04_select_from_model/tests/__pycache__/test_q04_select_from_model.cpython-36.pyc

diff --git a/q04_select_from_model/__pycache__/__init__.cpython-36.pyc b/q04_select_from_model/__pycache__/__init__.cpython-36.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..73fd11c5d8f9bb80fa67661607ee928dd8fda309
GIT binary patch
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diff --git a/q04_select_from_model/__pycache__/build.cpython-36.pyc b/q04_select_from_model/__pycache__/build.cpython-36.pyc
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diff --git a/q04_select_from_model/build.py b/q04_select_from_model/build.py
index 12dd1df..413489c 100644
--- a/q04_select_from_model/build.py
+++ b/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(df):
+    np.random.seed(9)
+    X = data.iloc[:,:-1]
+    y = data.iloc[:,-1]
+    rf = RandomForestClassifier()
+    rf.fit(X,y)
+    feature_name = []
+    selected_features = SelectFromModel(rf,prefit=True).get_support()
+    for col in list(zip(X.columns, selected_features)):
+        if(col[1]==True):
+            feature_name.append(col[0])
+    return feature_name
+
+
+
+
diff --git a/q04_select_from_model/tests/__pycache__/__init__.cpython-36.pyc b/q04_select_from_model/tests/__pycache__/__init__.cpython-36.pyc
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From c45811610f8a1ff54df9592920fd9ade265539e1 Mon Sep 17 00:00:00 2001
From: sagarpatil232 <sagarpatil232@gmail.com>
Date: Mon, 26 Nov 2018 16:53:14 +0000
Subject: [PATCH 6/6] Done

---
 .../__pycache__/__init__.cpython-36.pyc       | Bin 0 -> 176 bytes
 .../__pycache__/build.cpython-36.pyc          | Bin 0 -> 1163 bytes
 q05_forward_selected/build.py                 |  94 +++++++++++++++++-
 .../tests/__pycache__/__init__.cpython-36.pyc | Bin 0 -> 182 bytes
 .../test_q05_forward_selected.cpython-36.pyc  | Bin 0 -> 2854 bytes
 5 files changed, 93 insertions(+), 1 deletion(-)
 create mode 100644 q05_forward_selected/__pycache__/__init__.cpython-36.pyc
 create mode 100644 q05_forward_selected/__pycache__/build.cpython-36.pyc
 create mode 100644 q05_forward_selected/tests/__pycache__/__init__.cpython-36.pyc
 create mode 100644 q05_forward_selected/tests/__pycache__/test_q05_forward_selected.cpython-36.pyc

diff --git a/q05_forward_selected/__pycache__/__init__.cpython-36.pyc b/q05_forward_selected/__pycache__/__init__.cpython-36.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e00176f48fb0f0df60b89bd8e5ce40c5af273b4e
GIT binary patch
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diff --git a/q05_forward_selected/__pycache__/build.cpython-36.pyc b/q05_forward_selected/__pycache__/build.cpython-36.pyc
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diff --git a/q05_forward_selected/build.py b/q05_forward_selected/build.py
index 8816abd..a3631dd 100644
--- a/q05_forward_selected/build.py
+++ b/q05_forward_selected/build.py
@@ -1,10 +1,102 @@
+# %load q05_forward_selected/build.py
 # Default imports
 import pandas as pd
 from sklearn.linear_model import LinearRegression
-
+from sklearn.metrics import r2_score
+import numpy as np
 data = pd.read_csv('data/house_prices_multivariate.csv')
 
 model = LinearRegression()
 
 
 # Your solution code here
+def forward_selected(data,model):
+    old_r2_score = 0
+    new_r2_score = 1
+    features = list(data.drop('SalePrice',axis=1).columns)
+    selected_features = []
+    r2_score_features = []
+    X_selected = pd.DataFrame()
+    result = pd.DataFrame()
+    y = data['SalePrice']
+    while(True):
+        scores = []
+        for i in range(len(features)):
+            X = data[features[i]]
+            X_selected = result
+            X_selected = pd.concat([X_selected,X], axis=1)
+            model.fit(X_selected,y)
+            y_pred = model.predict(X_selected)
+            scores.append(r2_score(y,y_pred))
+            X_selected = result
+            np_scores = np.array(scores)
+        new_r2_score = np_scores.max()
+        if(new_r2_score>old_r2_score):
+            old_r2_score=new_r2_score
+            result = pd.concat([result,data[features[np.argmax(np_scores)]]], axis=1)
+            data = data.drop(features[np.argmax(np_scores)],axis = 1)
+            selected_features.append(features[np.argmax(np_scores)])
+            r2_score_features.append(new_r2_score)
+            features.remove(features[np.argmax(np_scores)])
+        else:
+            break
+    return selected_features,r2_score_features
+# X = data.drop('SalePrice',1)
+# y = data.iloc[:,-1]
+# features = X.columns
+# r2_scores = []
+# for feature in list(features):
+#     df = X.loc[:,[feature]]
+#     model.fit(df,y)
+#     y_pred = model.predict(df) 
+#     r2_scores.append((feature,r2_score(y, y_pred)))
+# max = r2_scores[0][1]
+# max_feature = r2_scores[0][0]
+#  = []
+# while(len(r2_scores_sorted)!=len(r2_scores)):
+#     for item in r2_scores:
+#         if(max < item[1]):
+#             max = item[1]
+#             r2_scores_sorted.append(item)
+# max_feature
+# #data.head()
+# #model.set_params()
+# X = data.iloc[:,:-1]
+# y = data.iloc[:,-1]
+# flag = True
+# #print(X.columns)
+# features = X.columns
+# r2_scores = []
+# print('features')
+# for feature in list(features):
+#     X = pd.DataFrame(X[feature])
+#     model.fit(X,y)
+#     y_pred = model.predict(X) 
+#     r2_scores.append(r2_score(y, y_pred))
+# print(r2_scores)
+# # while(flag==True):
+# #     for feature in features:
+# #         X = X[[feature]]
+# #         model.fit(X,y)
+# #         y_pred = model.predict(X) 
+# #         y_pred = r2_score(y, y_pred)
+# # print(y_pred)
+# X.columns
+# data.head()
+# model.set_params()
+# X = data.iloc[:,:-1]
+# y = data.iloc[:,-1]
+# flag = True
+# features = X.columns
+# r2_scores = 
+# while(flag==True):
+#     for feature in features:
+#         X = X[[feature]]
+#         model.fit(X,y)
+#         y_pred = model.predict(X) 
+#         y_pred = r2_score(y, y_pred)
+# print(y_pred)
+# X[['GrLivArea','GarageArea']]
+
+
+
diff --git a/q05_forward_selected/tests/__pycache__/__init__.cpython-36.pyc b/q05_forward_selected/tests/__pycache__/__init__.cpython-36.pyc
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