commit-live-students · Abhimanyu22 · Oct 29, 2018 · Oct 29, 2018 · Oct 31, 2018 · Oct 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 +1,17 @@
-# Default imports
 import pandas as pd
 from sklearn.model_selection import train_test_split
 
-
 path = 'data/house_prices_multivariate.csv'
 
+def load_data(path, test_size=0.33, randomst=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=randomst, train_size=0.67)
+
+    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
@@ -1,8 +1,18 @@
-# Default imports
+import pandas as pd
+import numpy as np
 from greyatomlib.advanced_linear_regression.q01_load_data.build import load_data
 
-# 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 Max_important_feature(data_set, target_variable='SalePrice', n=4):
+
+    cols = data_set.corr().nlargest(n+1, target_variable)[target_variable].drop([target_variable]).index
+
+    cm = data_set[cols].corr()
+
+    return cm
+
+Max_important_feature(data_set, 'SalePrice')
+
+
 
-# Write your code here
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
@@ -1,11 +1,21 @@
-# Default imports
+import pandas as pd
+import numpy as np
 from greyatomlib.advanced_linear_regression.q01_load_data.build import load_data
 from sklearn.preprocessing import PolynomialFeatures
 from sklearn.pipeline import make_pipeline
 from sklearn.linear_model import LinearRegression
 
-# 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')
+data_set, X_train, X_test, y_train, y_test = load_data('data/house_prices_multivariate.csv', 0.33, 9)
+
+def polynomial(power = 5, randomst=9):
+    cols = data_set.corr().nlargest(5, 'SalePrice')['SalePrice'].index
+    poly_model = make_pipeline(PolynomialFeatures(degree=power, include_bias=False), LinearRegression())
+    poly_model.fit(X_train[cols[1:]], y_train)
+
+    return poly_model
+
+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 +1,28 @@
-# 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)
+
+def ridge(al=0.01):
+
+    ridge_model = Ridge(alpha=al, normalize=True, random_state=9)
+    ridge_model.fit(X_train, y_train)
+
+    y_pred_train = ridge_model.predict(X_train)
+    y_pred_test = ridge_model.predict(X_test)
+
+    rmse_train = mean_squared_error(y_train, y_pred_train)**0.5
+    rmse_test = mean_squared_error(y_test, y_pred_test)**0.5
+
+    return rmse_train, rmse_test, ridge_model
+
+ridge(0.01)
+
 
-# Write your solution here
 
 
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 @@
-# 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)
+
+def lasso(al=0.01):
+
+    lasso_model = Lasso(alpha=al, normalize=True, random_state=9)
+    lasso_model.fit(X_train, y_train)
+
+    y_pred_train = lasso_model.predict(X_train)
+    y_pred_test = lasso_model.predict(X_test)
+
+    rmse_train = mean_squared_error(y_train, y_pred_train)**0.5
+    rmse_test = mean_squared_error(y_test, y_pred_test)**0.5
+
+    return rmse_train, rmse_test
+
+lasso(0.01)
+
 
-# Write your solution here
 
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 +1,20 @@
-# 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)
+
+def cross_validation(model, X, y):
+
+    model.fit(X_train, y_train)
+    scores = cross_val_score(model, X, y, scoring='neg_mean_squared_error', cv=5)
+    neg_mse = scores.mean()
+
+    return neg_mse
+
 
-# Write your solution here
 
 
 
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