commit-live-students · sahusaurabh65 · Nov 1, 2018 · Nov 1, 2018 · Nov 1, 2018 · Nov 1, 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 +1,27 @@
+# %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'
 
+# def load_data(path):
+#     df= pd.read_csv(path)
+#     #return df
+#     X=df.iloc[:,:-1]
+#     y=df['SalePrice']
+# #tts 
+#     X_train, X_test, y_train, y_test = train_test_split(df, y,random_state=9, test_size=0.33)
+#     return df,X_train, X_test, y_train, y_test
+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
+
+load_data(path)
+
 
-# 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 +7,10 @@
 
 
 # Write your code here
+def Max_important_feature(data_set,target_variable='SalePrice',n=4):
+    #f.drop('Target', axis=1).apply(lambda x: x.corr(df.Target))
+    coloumns=data_set.corr().nlargest(n+1,target_variable)[target_variable].index[1:n+1]
+    return coloums
+Max_important_feature(data_set)
+
+
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
@@ -7,5 +8,11 @@
 # 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')
 
+def polynomial(power=5,random_state=9):
+    model_P = make_pipeline(PolynomialFeatures(power,include_bias=False),LinearRegression())
+    model_P.fit(X_train[['OverallQual','GrLivArea','GarageCars','GarageArea']],y_train)
+    return model_P
+
+polynomial()
+
 
-# 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 +1,31 @@
+# %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)
+
+def ridge(alpha=0.01):
+    ridgeReg = Ridge(alpha=alpha , normalize=True,random_state=9)
+    ridgeReg.fit(X_train,y_train)
+    ypred_train= ridgeReg.predict(X_train)
+
+    #calculate RMSE for training data
+    rmse_train = math.sqrt(mean_squared_error(y_train, ypred_train))
+
+    #calculate Rmse for testing data
+    ypred_test= ridgeReg.predict(X_test)
+    rmse_test = math.sqrt(mean_squared_error(y_test, ypred_test))
+
+    return rmse_train,rmse_test, ridgeReg
+
 
-# Write your solution here
+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 +1,28 @@
+# %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
diff --git a/q06_cross_validation/__pycache__/__init__.cpython-36.pyc b/q06_cross_validation/__pycache__/__init__.cpython-36.pyc
diff --git a/q06_cross_validation/__pycache__/build.cpython-36.pyc b/q06_cross_validation/__pycache__/build.cpython-36.pyc
diff --git a/q06_cross_validation/build.py b/q06_cross_validation/build.py
@@ -1,13 +1,21 @@
+# %load q06_cross_validation/build.py
 # Default imports
 from sklearn.model_selection import cross_val_score
 import numpy as np
 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)
 
-# Write your solution here
+from sklearn.linear_model import LinearRegression, Lasso, Ridge
 
+def cross_validation(Model, X, y):
+    scores = cross_val_score(Model, X_train, y_train, scoring='neg_mean_squared_error', cv=5)
+    mean_error_score = scores.mean()
+
+    return mean_error_score
+
+cross_validation(Ridge(0.1), X_train, y_train)
 
 
diff --git a/q06_cross_validation/tests/__pycache__/__init__.cpython-36.pyc b/q06_cross_validation/tests/__pycache__/__init__.cpython-36.pyc
diff --git a/q06_cross_validation/tests/__pycache__/test_q06_cross_validation.cpython-36.pyc b/q06_cross_validation/tests/__pycache__/test_q06_cross_validation.cpython-36.pyc