commit-live-students · soham009 · Dec 27, 2018 · Dec 27, 2018 · Dec 27, 2018 · Dec 27, 2018
diff --git a/__pycache__/__init__.cpython-36.pyc b/__pycache__/__init__.cpython-36.pyc
diff --git a/q01_load_data/__pycache__/__init__.cpython-36.pyc b/q01_load_data/__pycache__/__init__.cpython-36.pyc
diff --git a/q01_load_data/__pycache__/build.cpython-36.pyc b/q01_load_data/__pycache__/build.cpython-36.pyc
diff --git a/q01_load_data/build.py b/q01_load_data/build.py
@@ -1,10 +1,16 @@
+# %load q01_load_data/build.py
 # Default imports
 import pandas as pd
 from sklearn.model_selection import train_test_split
 
 
-path = 'data/house_prices_multivariate.csv'
+# Write your solution here
+def load_data(path,test_size=0.33,Random_state=9):
+    df = pd.read_csv(path)
+    X = df.iloc[:,:-1]
+    y = df['SalePrice']
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=9, test_size=test_size)
+    return df,X_train, X_test, y_train, y_test
 
 
-# Write your solution here
 
diff --git a/q01_load_data/tests/__pycache__/__init__.cpython-36.pyc b/q01_load_data/tests/__pycache__/__init__.cpython-36.pyc
diff --git a/q01_load_data/tests/__pycache__/test_q01_load_data.cpython-36.pyc b/q01_load_data/tests/__pycache__/test_q01_load_data.cpython-36.pyc
diff --git a/q02_Max_important_feature/__pycache__/__init__.cpython-36.pyc b/q02_Max_important_feature/__pycache__/__init__.cpython-36.pyc
diff --git a/q02_Max_important_feature/__pycache__/build.cpython-36.pyc b/q02_Max_important_feature/__pycache__/build.cpython-36.pyc
diff --git a/q02_Max_important_feature/build.py b/q02_Max_important_feature/build.py
@@ -1,3 +1,4 @@
+# %load q02_Max_important_feature/build.py
 # Default imports
 from greyatomlib.advanced_linear_regression.q01_load_data.build import load_data
 
@@ -6,3 +7,13 @@
 
 
 # Write your code here
+
+def Max_important_feature(data_set,target_variable='SalePrice',n=4):
+    cols=data_set.corr().nlargest(n+1,target_variable)[target_variable].index[1:n+1]
+    return cols
+#Max_important_feature(data_set,target_variable='SalePrice',n=4)
+
+
+
+
+
diff --git a/q02_Max_important_feature/tests/__pycache__/__init__.cpython-36.pyc b/q02_Max_important_feature/tests/__pycache__/__init__.cpython-36.pyc
diff --git a/q02_Max_important_feature/tests/__pycache__/test_q02max_important_feature.cpython-36.pyc b/q02_Max_important_feature/tests/__pycache__/test_q02max_important_feature.cpython-36.pyc
diff --git a/q03_polynomial/__pycache__/__init__.cpython-36.pyc b/q03_polynomial/__pycache__/__init__.cpython-36.pyc
diff --git a/q03_polynomial/__pycache__/build.cpython-36.pyc b/q03_polynomial/__pycache__/build.cpython-36.pyc
diff --git a/q03_polynomial/build.py b/q03_polynomial/build.py
@@ -1,3 +1,4 @@
+# %load q03_polynomial/build.py
 # Default imports
 from greyatomlib.advanced_linear_regression.q01_load_data.build import load_data
 from sklearn.preprocessing import PolynomialFeatures
@@ -9,3 +10,6 @@
 
 
 # Write your solution here
+
+
+
diff --git a/q03_polynomial/tests/__pycache__/__init__.cpython-36.pyc b/q03_polynomial/tests/__pycache__/__init__.cpython-36.pyc
diff --git a/q03_polynomial/tests/__pycache__/test_q03_polynomial.cpython-36.pyc b/q03_polynomial/tests/__pycache__/test_q03_polynomial.cpython-36.pyc
diff --git a/q04_ridge/__pycache__/__init__.cpython-36.pyc b/q04_ridge/__pycache__/__init__.cpython-36.pyc
diff --git a/q04_ridge/__pycache__/build.cpython-36.pyc b/q04_ridge/__pycache__/build.cpython-36.pyc
diff --git a/q04_ridge/build.py b/q04_ridge/build.py
@@ -1,15 +1,33 @@
+# %load q04_ridge/build.py
 # Default imports
 from sklearn.linear_model import Ridge
 import pandas as pd
 import numpy as np
 from sklearn.metrics import mean_squared_error
 from greyatomlib.advanced_linear_regression.q01_load_data.build import load_data
-np.random.seed(9)
 
+import math
 # We have already loaded the data for you
 data_set, X_train, X_test, y_train, y_test = load_data('data/house_prices_multivariate.csv')
 
+np.random.seed(9)
+
 
 # Write your solution here
 
+def ridge(alpha=0.01):
+    model=Ridge(alpha=alpha,normalize=True,random_state=9)
+    model.fit(X_train,y_train)
+
+    ypred_train=model.predict(X_train)
+    rmse_train = math.sqrt(mean_squared_error(y_train, ypred_train))
+
+    ypred_test=model.predict(X_test)
+    rmse_test = math.sqrt(mean_squared_error(y_test, ypred_test))
+
+    return rmse_train,rmse_test,model
+ridge(alpha=0.01)
+
+
+
 
diff --git a/q04_ridge/tests/__pycache__/__init__.cpython-36.pyc b/q04_ridge/tests/__pycache__/__init__.cpython-36.pyc
diff --git a/q04_ridge/tests/__pycache__/test_q04_ridge.cpython-36.pyc b/q04_ridge/tests/__pycache__/test_q04_ridge.cpython-36.pyc
diff --git a/q05_lasso/__pycache__/__init__.cpython-36.pyc b/q05_lasso/__pycache__/__init__.cpython-36.pyc
diff --git a/q05_lasso/__pycache__/build.cpython-36.pyc b/q05_lasso/__pycache__/build.cpython-36.pyc
diff --git a/q05_lasso/build.py b/q05_lasso/build.py
@@ -1,14 +1,29 @@
+# %load q05_lasso/build.py
 # Default imports
 from sklearn.linear_model import Lasso
 import pandas as pd
 import numpy as np
 from sklearn.metrics import mean_squared_error
 from greyatomlib.advanced_linear_regression.q01_load_data.build import load_data
-np.random.seed(9)
 
 # We have already loaded the data for you
 data_set, X_train, X_test, y_train, y_test = load_data('data/house_prices_multivariate.csv')
 
+np.random.seed(9)
 
+import math
 # Write your solution here
+def lasso(alpha=0.01):
+
+    model=Lasso(alpha=alpha,normalize=True,random_state=9)
+    model.fit(X_train,y_train)
+
+    ypred_train=model.predict(X_train)
+    rmse_train = math.sqrt(mean_squared_error(y_train, ypred_train))
+
+    ypred_test=model.predict(X_test)
+    rmse_test = math.sqrt(mean_squared_error(y_test, ypred_test))
+
+    return rmse_train,rmse_test
+
 
diff --git a/q05_lasso/tests/__pycache__/__init__.cpython-36.pyc b/q05_lasso/tests/__pycache__/__init__.cpython-36.pyc
diff --git a/q05_lasso/tests/__pycache__/test_q05_lasso.cpython-36.pyc b/q05_lasso/tests/__pycache__/test_q05_lasso.cpython-36.pyc