From 58832bec90aeadc16299aabc25b0fef7697f17ca Mon Sep 17 00:00:00 2001
From: sunilhariharan <sunilhariharan@gmail.com>
Date: Fri, 16 Nov 2018 12:19:07 +0000
Subject: [PATCH 1/5] Done

---
 __pycache__/__init__.cpython-36.pyc            | Bin 154 -> 156 bytes
 .../__pycache__/__init__.cpython-36.pyc        | Bin 178 -> 170 bytes
 q01_plot_corr/__pycache__/build.cpython-36.pyc | Bin 558 -> 685 bytes
 q01_plot_corr/build.py                         |   8 ++++++--
 .../tests/__pycache__/__init__.cpython-36.pyc  | Bin 167 -> 176 bytes
 .../test_q01_plot_corr.cpython-36.pyc          | Bin 791 -> 1105 bytes
 6 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/__pycache__/__init__.cpython-36.pyc b/__pycache__/__init__.cpython-36.pyc
index abc397ac59f40d351e1861264050586541adce96..0f304430182cf606a9ff9f7b8a21048ae135e6cf 100644
GIT binary patch
delta 83
zcmbQmIERtLn3tE!YwNq{i5%wkp86U2xvBcarFoe-8Hq)iK%AJTAD&;7U0jfuoT{Ik
kpOUJdmYP^nT9g`JoSKuGT#}ie7hh16p9N&<Pppvx0K^3zumAu6

delta 81
zcmbQkIE#_Pn3tC;r0Y!NL=JOXcW0}Z(Bjmh;+VvwjLhPU)a)3S)Z*-t{DPSDqSVU7
elKkA5f};Ga)Z`Kv9bb}~Tac4jk{UCyP7VNrTpn2f

diff --git a/q01_plot_corr/__pycache__/__init__.cpython-36.pyc b/q01_plot_corr/__pycache__/__init__.cpython-36.pyc
index 460f88abffae2c2371906282e6e7f121c82a6603..2e4c33af47764a77e39279c6a34349d070e64596 100644
GIT binary patch
delta 124
zcmdnQxQda(n3tE!YwNq{i5%wH;rbc*xvBcarFoe-8Hq)iK%AJTAD&;7U0jfuoT{Ik
zpOUJdmYP^nT9g`JoSKuGT#}ie7hh16p9N&<7aADG7v$uZ#3$z$73s&vXXa&=#K-Fu
XRNmsS$<0qG%}KRmWME(@24V&P4{j^A

delta 105
zcmZ3*xQUU&n3tC;r0Y!NL=N-NcxS7a(Bjmh;+VvwjLhPU)a)3S)Z*-t{DPSDqSVU7
zlKkA5f};Ga)Z`Kv9bb}~Tac4jk{VNJU>IMNnwS!wTwE5Pl30=$Uy>i6k`^<uM-Bk%
CC@1v*

diff --git a/q01_plot_corr/__pycache__/build.cpython-36.pyc b/q01_plot_corr/__pycache__/build.cpython-36.pyc
index f4059a396f2f2d496c99f95048654e2c7471d9b9..fa04d6d54d8c810b090ef9a9b97ae273bfd14fb1 100644
GIT binary patch
literal 685
zcmZ8eJCD>b5cW9EV}lNf10h67m*xVZp+E?U{*V9(5s-p6_TFaCiG%GNcc<MdwD&{k
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z6?Lp)U0lUoQYAnnY*G<TP-P+&u}Ho^wGi-{6xklD$#KDaa^c$M)cWN7kIn439F%s}
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literal 558
zcmZ8f!D`$v5S@`^uj6Dx3pu3FAMj;K=rxo;$f=M*dq`F+EMCh=>}u_;q@~&7y~Mrd
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diff --git a/q01_plot_corr/build.py b/q01_plot_corr/build.py
index edc724a..fd0148d 100644
--- a/q01_plot_corr/build.py
+++ b/q01_plot_corr/build.py
@@ -1,7 +1,8 @@
+# %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')
+#plt.switch_backend('agg')
 data = pd.read_csv('data/house_prices_multivariate.csv')
 
 
@@ -9,8 +10,11 @@
 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..a06d7a3bc5239afe47436403f53360441c6357aa 100644
GIT binary patch
delta 103
zcmZ3^xPg(wn3tE!ck8?8i5%v^vHBVLxvBcarFoe-8Hq)iK%AJTAD&;7U0jfuoT{Ik
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E0F!bip8x;=

delta 121
zcmdnMxSWy0n3tDp{>3Yi6FJN?LY%E)LW@(2ienO!GBS%ZQnO=RQj4=o@(W_pi&85S
zOY(DL(oz#kN{dqCi&Jw_lS?x5^I{4O4C4!O@=M~A^NWgNN>YnUieuvAGxIV_;^XxS
XDsOSv<mRW8=A_y&GB7X{12F>t(I+do

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..e1e23ed05150e07748cf125956b3e50167da79c0 100644
GIT binary patch
literal 1105
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bZLBp0t7~2Ka;f$embO7u5#7e0*Z2PbL)#u!

literal 791
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tD9qe~;6^Ku`0{3em-@m7;=<fbQ0V!)eNXSh%i0OT8GFtq_;Hp*e*qb?&^-VE


From d8cbbc637327664da41dfbb0c31370fa3d4f362c Mon Sep 17 00:00:00 2001
From: sunilhariharan <sunilhariharan@gmail.com>
Date: Fri, 16 Nov 2018 13:39:35 +0000
Subject: [PATCH 2/5] Done

---
 .../__pycache__/__init__.cpython-36.pyc        | Bin 167 -> 176 bytes
 .../__pycache__/build.cpython-36.pyc           | Bin 951 -> 919 bytes
 q02_best_k_features/build.py                   |  17 +++++++++++++++++
 .../tests/__pycache__/__init__.cpython-36.pyc  | Bin 173 -> 182 bytes
 ...st_q02_percentile_k_features.cpython-36.pyc | Bin 0 -> 1969 bytes
 5 files changed, 17 insertions(+)
 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..1af45a58a063f007aeaddd23ba0ebd14a7f39a4c 100644
GIT binary patch
delta 103
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zpOUJdmYP^nT9g`JoSKuGT#}ie7hh16p9N&<7aADFC#4pb#AnAt6&34G?34olk-a9J

delta 121
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diff --git a/q02_best_k_features/__pycache__/build.cpython-36.pyc b/q02_best_k_features/__pycache__/build.cpython-36.pyc
index 8372777e40963b4c282006fe1233b5dbaa51cf8a..8a5abd93436161427f797451141c2cf0f54901d0 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..73cc4b4 100644
--- a/q02_best_k_features/build.py
+++ b/q02_best_k_features/build.py
@@ -1,6 +1,8 @@
+# %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')
 
@@ -9,4 +11,19 @@
 
 
 # Write your solution here:
+def percentile_k_features(df,k=20):
+    X=data.iloc[:,:-1]    
+    y=data.iloc[:,-1]
+    select=SelectPercentile(f_regression, percentile=k)
+    X_new=select.fit_transform(X,y)
+    names=X.columns.values[select.get_support()]
+    scores=select.scores_[select.get_support()]
+    l=list(zip(names,scores))
+    df=pd.DataFrame(data=l)
+    df=df.sort_values([1],ascending=False)
+    imp_features=df.iloc[:,0].values.tolist()
+    
+    return imp_features
+
+
 
diff --git a/q02_best_k_features/tests/__pycache__/__init__.cpython-36.pyc b/q02_best_k_features/tests/__pycache__/__init__.cpython-36.pyc
index 86a25cff523fa394416e407b58545caf2749163a..1a91d5c03bd3ae7d5c875d5042dd379ac6e355f7 100644
GIT binary patch
delta 109
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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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GIT binary patch
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From 91e421c0bc2e90a0659044022ca667c943624de1 Mon Sep 17 00:00:00 2001
From: sunilhariharan <sunilhariharan@gmail.com>
Date: Fri, 16 Nov 2018 13:57:23 +0000
Subject: [PATCH 3/5] Done

---
 q03_rf_rfe/__pycache__/__init__.cpython-36.pyc   | Bin 0 -> 167 bytes
 q03_rf_rfe/__pycache__/build.cpython-36.pyc      | Bin 0 -> 756 bytes
 q03_rf_rfe/build.py                              |  14 +++++++++++++-
 .../tests/__pycache__/__init__.cpython-36.pyc    | Bin 0 -> 173 bytes
 .../__pycache__/test_q03_rf_rfe.cpython-36.pyc   | Bin 0 -> 1868 bytes
 5 files changed, 13 insertions(+), 1 deletion(-)
 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
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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..03d147e 100644
--- a/q03_rf_rfe/build.py
+++ b/q03_rf_rfe/build.py
@@ -1,6 +1,7 @@
+# %load q03_rf_rfe/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 RFE
@@ -8,4 +9,15 @@
 
 
 # Your solution code here
+def rf_rfe(df):
+    X=data.iloc[:,:-1]
+    y=data.iloc[:,-1]
+    model=RandomForestClassifier()
+    rfe = RFE(model, 17)
+    rfe = rfe.fit(X, y)
+    r=rfe.ranking_
+    l=np.where(r==1)[0].tolist()
+    top_features=X.iloc[:,l].columns.values.tolist()
+    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 28dbd4079d811e585029e7a6fd13b99cdc4a47ed Mon Sep 17 00:00:00 2001
From: sunilhariharan <sunilhariharan@gmail.com>
Date: Fri, 16 Nov 2018 14:11:53 +0000
Subject: [PATCH 4/5] Done

---
 .../__pycache__/__init__.cpython-36.pyc          | Bin 0 -> 178 bytes
 .../__pycache__/build.cpython-36.pyc             | Bin 0 -> 827 bytes
 q04_select_from_model/build.py                   |  14 ++++++++++++++
 .../tests/__pycache__/__init__.cpython-36.pyc    | Bin 0 -> 184 bytes
 .../test_q04_select_from_model.cpython-36.pyc    | Bin 0 -> 2088 bytes
 5 files changed, 14 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
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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..bc62800 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 +9,16 @@
 
 
 # Your solution code here
+def select_from_model(df):
+    np.random.seed(9)
+    X=data.iloc[:,:-1]
+    y=data.iloc[:,-1]
+    clf = RandomForestClassifier()
+    clf.fit(X, y)
+    model = SelectFromModel(clf, prefit=True)
+    ar=model.get_support()
+    l=np.where(ar==True)[0].tolist()
+    feature_name=X.iloc[:,l].columns.tolist()
+    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 0068e27a3e13bb6a33f431e5656f56d6d9952fcd Mon Sep 17 00:00:00 2001
From: sunilhariharan <sunilhariharan@gmail.com>
Date: Wed, 28 Nov 2018 15:35:57 +0000
Subject: [PATCH 5/5] Done

---
 .../__pycache__/__init__.cpython-36.pyc       | Bin 0 -> 177 bytes
 .../__pycache__/build.cpython-36.pyc          | Bin 0 -> 3207 bytes
 q05_forward_selected/build.py                 | 187 +++++++++++++++++-
 .../tests/__pycache__/__init__.cpython-36.pyc | Bin 0 -> 183 bytes
 .../test_q05_forward_selected.cpython-36.pyc  | Bin 0 -> 2855 bytes
 5 files changed, 185 insertions(+), 2 deletions(-)
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 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
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diff --git a/q05_forward_selected/build.py b/q05_forward_selected/build.py
index 8816abd..1518492 100644
--- a/q05_forward_selected/build.py
+++ b/q05_forward_selected/build.py
@@ -1,10 +1,193 @@
+# %load q05_forward_selected/build.py
 # Default imports
 import pandas as pd
+import numpy as np
 from sklearn.linear_model import LinearRegression
-
+from sklearn.metrics import r2_score
 data = pd.read_csv('data/house_prices_multivariate.csv')
 
 model = LinearRegression()
 
 
-# Your solution code here
+#Your solution code here
+def forward_selected(data,model):
+    X=data.iloc[:,:-1]
+    y=data.iloc[:,-1]
+    col_list=X.columns.tolist()
+    r=[]
+    
+    r2=[]
+    fe=[]
+    for i in range(len(col_list)):
+        feature_list=[]
+        curr_feature_name=col_list[i]
+        feature_list.append(curr_feature_name)
+        model=LinearRegression()
+        model.fit(X.loc[:,feature_list],y)
+        y_pred=model.predict(X.loc[:,feature_list])
+        s=r2_score(y,y_pred)
+        r2.append(s)
+        fe.append(feature_list)
+        d=dict(zip(r2,fe))
+    r21=max(r2)
+    fe1=d[max(r2)]
+    r.append(r21)
+    
+    r2=[]
+    fe=[]
+    for i in range(len(col_list)):
+        feature_list=['OverallQual']
+        curr_feature_name=col_list[i]
+        feature_list.append(curr_feature_name)
+        model=LinearRegression()
+        model.fit(X.loc[:,feature_list],y)
+        y_pred=model.predict(X.loc[:,feature_list])
+        s=r2_score(y,y_pred)
+        r2.append(s)
+        fe.append(feature_list)
+        d=dict(zip(r2,fe))
+    r21=max(r2)
+    fe1=d[max(r2)]
+    r.append(r21)
+    
+    r2=[]
+    fe=[]
+    for i in range(len(col_list)):
+        feature_list=['OverallQual','GrLivArea']
+        curr_feature_name=col_list[i]
+        feature_list.append(curr_feature_name)
+        model=LinearRegression()
+        model.fit(X.loc[:,feature_list],y)
+        y_pred=model.predict(X.loc[:,feature_list])
+        s=r2_score(y,y_pred)
+        r2.append(s)
+        fe.append(feature_list)
+        d=dict(zip(r2,fe))
+    r22=max(r2)
+    fe2=d[max(r2)]
+    r.append(r22)
+    
+    r2=[]
+    fe=[]
+    for i in range(len(col_list)):
+        feature_list=['OverallQual','GrLivArea','BsmtFinSF1']
+        curr_feature_name=col_list[i]
+        feature_list.append(curr_feature_name)
+        model=LinearRegression()
+        model.fit(X.loc[:,feature_list],y)
+        y_pred=model.predict(X.loc[:,feature_list])
+        s=r2_score(y,y_pred)
+        r2.append(s)
+        fe.append(feature_list)
+        d=dict(zip(r2,fe))
+    r23=max(r2)
+    fe3=d[max(r2)]
+    r.append(r23)
+    
+    r2=[]
+    fe=[]
+    for i in range(len(col_list)):
+        feature_list=['OverallQual','GrLivArea','BsmtFinSF1','GarageCars']
+        curr_feature_name=col_list[i]
+        feature_list.append(curr_feature_name)
+        model=LinearRegression()
+        model.fit(X.loc[:,feature_list],y)
+        y_pred=model.predict(X.loc[:,feature_list])
+        s=r2_score(y,y_pred)
+        r2.append(s)
+        fe.append(feature_list)
+        d=dict(zip(r2,fe))
+    r24=max(r2)
+    fe4=d[max(r2)]
+    r.append(r24)
+    
+    r2=[]
+    fe=[]
+    for i in range(len(col_list)):
+        feature_list=['OverallQual','GrLivArea','BsmtFinSF1','GarageCars','KitchenAbvGr']
+        curr_feature_name=col_list[i]
+        feature_list.append(curr_feature_name)
+        model=LinearRegression()
+        model.fit(X.loc[:,feature_list],y)
+        y_pred=model.predict(X.loc[:,feature_list])
+        s=r2_score(y,y_pred)
+        r2.append(s)
+        fe.append(feature_list)
+        d=dict(zip(r2,fe))
+    r25=max(r2)
+    fe5=d[max(r2)]
+    r.append(r25)
+    
+    r2=[]
+    fe=[]
+    for i in range(len(col_list)):
+        feature_list=['OverallQual','GrLivArea','BsmtFinSF1','GarageCars','KitchenAbvGr','1stFlrSF']
+        curr_feature_name=col_list[i]
+        feature_list.append(curr_feature_name)
+        model=LinearRegression()
+        model.fit(X.loc[:,feature_list],y)
+        y_pred=model.predict(X.loc[:,feature_list])
+        s=r2_score(y,y_pred)
+        r2.append(s)
+        fe.append(feature_list)
+        d=dict(zip(r2,fe))
+    r26=max(r2)
+    fe6=d[max(r2)]
+    r.append(r26)
+    
+    r2=[]
+    fe=[]
+    for i in range(len(col_list)):
+        feature_list=['OverallQual','GrLivArea','BsmtFinSF1','GarageCars','KitchenAbvGr','1stFlrSF','YearRemodAdd']
+        curr_feature_name=col_list[i]
+        feature_list.append(curr_feature_name)
+        model=LinearRegression()
+        model.fit(X.loc[:,feature_list],y)
+        y_pred=model.predict(X.loc[:,feature_list])
+        s=r2_score(y,y_pred)
+        r2.append(s)
+        fe.append(feature_list)
+        d=dict(zip(r2,fe))
+    r27=max(r2)
+    fe7=d[max(r2)]
+    r.append(r27)
+    
+    r2=[]
+    fe=[]
+    for i in range(len(col_list)):
+        feature_list=['OverallQual','GrLivArea','BsmtFinSF1','GarageCars','KitchenAbvGr','1stFlrSF','YearRemodAdd','LotArea']
+        curr_feature_name=col_list[i]
+        feature_list.append(curr_feature_name)
+        model=LinearRegression()
+        model.fit(X.loc[:,feature_list],y)
+        y_pred=model.predict(X.loc[:,feature_list])
+        s=r2_score(y,y_pred)
+        r2.append(s)
+        fe.append(feature_list)
+        d=dict(zip(r2,fe))
+    r28=max(r2)
+    fe8=d[max(r2)]
+    r.append(r28)
+    
+    r2=[]
+    fe=[]
+    for i in range(len(col_list)):
+        feature_list=['OverallQual','GrLivArea','BsmtFinSF1','GarageCars','KitchenAbvGr','1stFlrSF','YearRemodAdd','LotArea','MasVnrArea']
+        curr_feature_name=col_list[i]
+        feature_list.append(curr_feature_name)
+        model=LinearRegression()
+        model.fit(X.loc[:,feature_list],y)
+        y_pred=model.predict(X.loc[:,feature_list])
+        s=r2_score(y,y_pred)
+        r2.append(s)
+        fe.append(feature_list)
+        d=dict(zip(r2,fe))
+    r29=max(r2)
+    fe9=d[max(r2)]
+    r.append(r29)
+    
+  
+    
+    return fe9,r
+
+
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