commit-live-students · mario-the-legend · Nov 10, 2018 · Dec 29, 2018 · Dec 29, 2018 · Dec 31, 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 +1,18 @@
+# %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,index_col=0)
+    Y=df.iloc[:,33]
+    x=df.iloc[:,:34]
+    X_train, X_test, y_train, y_test = train_test_split(x,Y,test_size=test_size, random_state=Random_state)
+    return df,X_train, X_test, y_train, y_test
+dff,X_train, X_test, y_train, y_test = load_data(path='data/house_prices_multivariate.csv')
 
+X_train.shape
 
-# 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):
+    col=len(data_set.columns)-1
+    return data_set.iloc[:,:col].apply(lambda x: x.corr(data_set.loc[:,target_variable])).abs().sort_values(ascending = False).head(n).index
+
+Max_important_feature(data_set,'SalePrice',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 +10,9 @@
 
 
 # Write your solution here
+def polynomial(power=5,Random_state=9):
+    return make_pipeline(PolynomialFeatures(power,include_bias=False),LinearRegression()).fit(X_train[['OverallQual','GrLivArea','GarageCars','GarageArea']],y_train)
+
+polynomial(5,9)
+
+
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 +1,23 @@
+# %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)
 
 # 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)
+    return np.sqrt(mean_squared_error(model.predict(X_train), y_train)), np.sqrt(mean_squared_error(model.predict(X_test), y_test)), model
+
+ridge()
+
 
 
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 +1,30 @@
+# %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)
 
 # Write your solution here
+def lasso(alpha=0.01):
+    model = Lasso(alpha=alpha,normalize=True,random_state=9)
+    model.fit(X_train,y_train)
+
+    y_pred_train=model.predict(X_train) 
+    y_pred_test=model.predict(X_test)
+
+    m1=mean_squared_error(y_train,y_pred_train)
+    m2=mean_squared_error(y_test,y_pred_test)
+
+    return np.sqrt(m1), np.sqrt(m2)
+
+
+lasso()
+
 
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 +1,22 @@
+# %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
+from sklearn.linear_model import Ridge
 
-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)
+
+model = Ridge(alpha=0.01)
 
 # Write your solution here
+def cross_validation(Model, X, y):
+    scores = cross_val_score(Model, X, y, scoring='neg_mean_squared_error', cv=5)
+    return scores.mean()
+
 
+cross_validation(model,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