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machine_learning/xgboostclassifier.py

@@ -1,55 +1,79 @@
-from xgboost import XGBClassifier
-#https://xgboost.readthedocs.io/en/stable/
-import numpy as np 
-import pandas as pd 
-import seaborn as sns
 import matplotlib.pyplot as plt
 
+# https://xgboost.readthedocs.io/en/stable/
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from xgboost import XGBClassifier
 
-
-training = pd.read_csv('../input/titanic/train.csv')
-test = pd.read_csv('../input/titanic/test.csv')
+training = pd.read_csv("../input/titanic/train.csv")
+test = pd.read_csv("../input/titanic/test.csv")
 
 # Commented out IPython magic to ensure Python compatibility.
-training['train_test'] = 1
-test['train_test'] = 0
-test['Survived'] = np.NaN
-all_data = pd.concat([training,test])
+training["train_test"] = 1
+test["train_test"] = 0
+test["Survived"] = np.NaN
+all_data = pd.concat([training, test])
 # %matplotlib inline
 all_data.columns
 
 all_data.describe()
 
-all_data['cabin_mul']=all_data.Cabin.apply(lambda x: 0 if pd.isna(x) else len(x.split(' ')))
-all_data['cabin_adv'] = all_data.Cabin.apply(lambda x: str(x)[0])
-all_data['name_title']= all_data.Name.apply(lambda x: x.split(',')[1].split('.')[0].strip())
-all_data.Age=all_data.Age.fillna(training.Age.median())
-all_data.Fare=all_data.Fare.fillna(training.Fare.median())
-all_data.dropna(subset=['Embarked'],inplace=True)
-all_data['norm_fare']=np.log(all_data.Fare+1)
-all_data.Pclass=all_data.Pclass.astype(str)
-all_data['Age']=all_data['Age'].apply(np.int64)
-all_dummies = pd.get_dummies(all_data[['Pclass','Sex','Age','SibSp','Parch','norm_fare',
-                                       'Embarked','cabin_adv','cabin_mul',
-                                       'name_title','train_test']])
+all_data["cabin_mul"] = all_data.Cabin.apply(
+    lambda x: 0 if pd.isna(x) else len(x.split(" "))
+)
+all_data["cabin_adv"] = all_data.Cabin.apply(lambda x: str(x)[0])
+all_data["name_title"] = all_data.Name.apply(
+    lambda x: x.split(",")[1].split(".")[0].strip()
+)
+all_data.Age = all_data.Age.fillna(training.Age.median())
+all_data.Fare = all_data.Fare.fillna(training.Fare.median())
+all_data.dropna(subset=["Embarked"], inplace=True)
+all_data["norm_fare"] = np.log(all_data.Fare + 1)
+all_data.Pclass = all_data.Pclass.astype(str)
+all_data["Age"] = all_data["Age"].apply(np.int64)
+all_dummies = pd.get_dummies(
+    all_data[
+        [
+            "Pclass",
+            "Sex",
+            "Age",
+            "SibSp",
+            "Parch",
+            "norm_fare",
+            "Embarked",
+            "cabin_adv",
+            "cabin_mul",
+            "name_title",
+            "train_test",
+        ]
+    ]
+)
 
 from sklearn.preprocessing import StandardScaler
+
 scale = StandardScaler()
 all_dummies_scaled = all_dummies.copy()
-all_dummies_scaled[['Age','SibSp','Parch','norm_fare']]= scale.fit_transform(all_dummies_scaled[['Age','SibSp','Parch','norm_fare']])
+all_dummies_scaled[["Age", "SibSp", "Parch", "norm_fare"]] = scale.fit_transform(
+    all_dummies_scaled[["Age", "SibSp", "Parch", "norm_fare"]]
+)
 all_dummies_scaled.head()
 
-X_train_scaled = all_dummies_scaled[all_dummies_scaled.train_test == 1].drop(['train_test'], axis =1)
-X_test_scaled = all_dummies_scaled[all_dummies_scaled.train_test == 0].drop(['train_test'], axis =1)
+X_train_scaled = all_dummies_scaled[all_dummies_scaled.train_test == 1].drop(
+    ["train_test"], axis=1
+)
+X_test_scaled = all_dummies_scaled[all_dummies_scaled.train_test == 0].drop(
+    ["train_test"], axis=1
+)
 
-y_train = all_data[all_data.train_test==1].Survived
+y_train = all_data[all_data.train_test == 1].Survived
 
 from xgboost import XGBClassifier
 
 xgb = XGBClassifier()
-xgb.fit(X_train_scaled,y_train)
+xgb.fit(X_train_scaled, y_train)
 
 y_hat_base_vc = xgb.predict(X_test_scaled).astype(int)
-basic_submission = {'PassengerId': test.PassengerId, 'Survived': y_hat_base_vc}
+basic_submission = {"PassengerId": test.PassengerId, "Survived": y_hat_base_vc}
 base_submission = pd.DataFrame(data=basic_submission)
-base_submission.to_csv('xgb_submission.csv', index=False)
+base_submission.to_csv("xgb_submission.csv", index=False)

machine_learning/xgboostregressor.py

@@ -1,122 +1,148 @@
-from xgboost import XGBRegressor
-#https://xgboost.readthedocs.io/en/stable/
-import numpy as np 
-import pandas as pd
+import os
 
-import seaborn as sns
 import matplotlib.pyplot as plt
 
+# https://xgboost.readthedocs.io/en/stable/
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from xgboost import XGBRegressor
 
-import os
-for dirname, _, filenames in os.walk('/kaggle/input'):
+for dirname, _, filenames in os.walk("/kaggle/input"):
     for filename in filenames:
         print(os.path.join(dirname, filename))
 
-trainAmes=pd.read_csv('/kaggle/input/ames-housing-dataset/AmesHousing.csv')
-test=pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
-train=pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
+trainAmes = pd.read_csv("/kaggle/input/ames-housing-dataset/AmesHousing.csv")
+test = pd.read_csv("/kaggle/input/house-prices-advanced-regression-techniques/test.csv")
+train = pd.read_csv(
+    "/kaggle/input/house-prices-advanced-regression-techniques/train.csv"
+)
 
-trainAmes.columns = trainAmes.columns.str.replace(' ', '')
-trainAmes=trainAmes.rename(columns={"YearRemod/Add": "YearRemodAdd"})
+trainAmes.columns = trainAmes.columns.str.replace(" ", "")
+trainAmes = trainAmes.rename(columns={"YearRemod/Add": "YearRemodAdd"})
 
-data=pd.concat([trainAmes,train,test], axis=0, sort=False)
-print("Size of the Housing Dataset",len(data))
-useless = ['Id','PID','Order','SalePrice'] 
-data = data.drop(useless, axis = 1)
-duplicate = data[data.duplicated(keep='last')].index
+data = pd.concat([trainAmes, train, test], axis=0, sort=False)
+print("Size of the Housing Dataset", len(data))
+useless = ["Id", "PID", "Order", "SalePrice"]
+data = data.drop(useless, axis=1)
+duplicate = data[data.duplicated(keep="last")].index
 len(duplicate)
 
-duplicate=duplicate[0:390]
-trainAmes = trainAmes.drop(duplicate, axis = 0)
+duplicate = duplicate[0:390]
+trainAmes = trainAmes.drop(duplicate, axis=0)
 
-training=pd.concat([trainAmes,train], axis=0, sort=False)
-useless = ['Id','PID','Order'] 
-training = training.drop(useless, axis = 1)
+training = pd.concat([trainAmes, train], axis=0, sort=False)
+useless = ["Id", "PID", "Order"]
+training = training.drop(useless, axis=1)
 
 # Separating Target and Features
 
-target = training['SalePrice']
-test_id = test['Id']
-test = test.drop(['Id'],axis = 1)
-training2 = training.drop(['SalePrice'], axis = 1)
+target = training["SalePrice"]
+test_id = test["Id"]
+test = test.drop(["Id"], axis=1)
+training2 = training.drop(["SalePrice"], axis=1)
 
 
 # Concatenating train & test set
 
-train_test = pd.concat([training2,test], axis=0, sort=False)
+train_test = pd.concat([training2, test], axis=0, sort=False)
 
 # Filling Categorical NaN (That we know how to fill due to the description file )
 
-train_test['Functional'] = train_test['Functional'].fillna('Typ')
-train_test['Electrical'] = train_test['Electrical'].fillna("SBrkr")
-train_test['KitchenQual'] = train_test['KitchenQual'].fillna("TA")
-train_test['Exterior1st'] = train_test['Exterior1st'].fillna(train_test['Exterior1st'].mode()[0])
-train_test['Exterior2nd'] = train_test['Exterior2nd'].fillna(train_test['Exterior2nd'].mode()[0])
-train_test['SaleType'] = train_test['SaleType'].fillna(train_test['SaleType'].mode()[0])
+train_test["Functional"] = train_test["Functional"].fillna("Typ")
+train_test["Electrical"] = train_test["Electrical"].fillna("SBrkr")
+train_test["KitchenQual"] = train_test["KitchenQual"].fillna("TA")
+train_test["Exterior1st"] = train_test["Exterior1st"].fillna(
+    train_test["Exterior1st"].mode()[0]
+)
+train_test["Exterior2nd"] = train_test["Exterior2nd"].fillna(
+    train_test["Exterior2nd"].mode()[0]
+)
+train_test["SaleType"] = train_test["SaleType"].fillna(train_test["SaleType"].mode()[0])
 train_test["PoolQC"] = train_test["PoolQC"].fillna("None")
 train_test["Alley"] = train_test["Alley"].fillna("None")
-train_test['FireplaceQu'] = train_test['FireplaceQu'].fillna("None")
-train_test['Fence'] = train_test['Fence'].fillna("None")
-train_test['MiscFeature'] = train_test['MiscFeature'].fillna("None")
-for col in ('GarageArea', 'GarageCars'):
+train_test["FireplaceQu"] = train_test["FireplaceQu"].fillna("None")
+train_test["Fence"] = train_test["Fence"].fillna("None")
+train_test["MiscFeature"] = train_test["MiscFeature"].fillna("None")
+for col in ("GarageArea", "GarageCars"):
     train_test[col] = train_test[col].fillna(0)
-        
-for col in ['GarageType', 'GarageFinish', 'GarageQual', 'GarageCond']:
-    train_test[col] = train_test[col].fillna('None')
-    
-for col in ('BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'):
-    train_test[col] = train_test[col].fillna('None')
-    
-for col in ('BsmtFinSF1', 'BsmtFinSF2', 'BsmtFullBath', 'BsmtHalfBath', 'MasVnrArea','BsmtUnfSF', 'TotalBsmtSF'):
+
+for col in ["GarageType", "GarageFinish", "GarageQual", "GarageCond"]:
+    train_test[col] = train_test[col].fillna("None")
+
+for col in ("BsmtQual", "BsmtCond", "BsmtExposure", "BsmtFinType1", "BsmtFinType2"):
+    train_test[col] = train_test[col].fillna("None")
+
+for col in (
+    "BsmtFinSF1",
+    "BsmtFinSF2",
+    "BsmtFullBath",
+    "BsmtHalfBath",
+    "MasVnrArea",
+    "BsmtUnfSF",
+    "TotalBsmtSF",
+):
     train_test[col] = train_test[col].fillna(0)
 
-train_test['LotFrontage'] = train_test['LotFrontage'].fillna(train['LotFrontage'].median())
-    
-    # Checking the features with NaN remained out
+train_test["LotFrontage"] = train_test["LotFrontage"].fillna(
+    train["LotFrontage"].median()
+)
+
+# Checking the features with NaN remained out
 
 for col in train_test:
     if train_test[col].isna().sum() > 0:
         print(train_test[col][1])
 
 # Converting non-numeric predictors stored as numbers into string
 
-train_test['MSSubClass'] = train_test['MSSubClass'].apply(str)
-train_test['YrSold'] = train_test['YrSold'].apply(str)
-train_test['MoSold'] = train_test['MoSold'].apply(str)
-train_test['OverallQual'] = train_test['OverallQual'].apply(str)
-train_test['OverallCond'] = train_test['OverallCond'].apply(str)
-train_test["SqFtPerRoom"] = train_test["GrLivArea"] / (train_test["TotRmsAbvGrd"] +
-                                                       train_test["FullBath"] +
-                                                       train_test["HalfBath"] +
-                                                       train_test["KitchenAbvGr"])
-
-train_test['Total_Home_Quality'] = train_test['OverallQual'] + train_test['OverallCond']
-
-train_test['Total_Bathrooms'] = (train_test['FullBath'] + (0.5 * train_test['HalfBath']) +
-                               train_test['BsmtFullBath'] + (0.5 * train_test['BsmtHalfBath']))
+train_test["MSSubClass"] = train_test["MSSubClass"].apply(str)
+train_test["YrSold"] = train_test["YrSold"].apply(str)
+train_test["MoSold"] = train_test["MoSold"].apply(str)
+train_test["OverallQual"] = train_test["OverallQual"].apply(str)
+train_test["OverallCond"] = train_test["OverallCond"].apply(str)
+train_test["SqFtPerRoom"] = train_test["GrLivArea"] / (
+    train_test["TotRmsAbvGrd"]
+    + train_test["FullBath"]
+    + train_test["HalfBath"]
+    + train_test["KitchenAbvGr"]
+)
+
+train_test["Total_Home_Quality"] = train_test["OverallQual"] + train_test["OverallCond"]
+
+train_test["Total_Bathrooms"] = (
+    train_test["FullBath"]
+    + (0.5 * train_test["HalfBath"])
+    + train_test["BsmtFullBath"]
+    + (0.5 * train_test["BsmtHalfBath"])
+)
 
 train_test["HighQualSF"] = train_test["1stFlrSF"] + train_test["2ndFlrSF"]
-train_test['renovated']=train_test['YearRemodAdd']+train_test['YearBuilt']
+train_test["renovated"] = train_test["YearRemodAdd"] + train_test["YearBuilt"]
 
 # Removing the useless variables
 
-useless = ['GarageYrBlt','YearRemodAdd'] 
-train_test = train_test.drop(useless, axis = 1)
+useless = ["GarageYrBlt", "YearRemodAdd"]
+train_test = train_test.drop(useless, axis=1)
 # Creating dummy variables from categorical features
 
 from scipy.stats import skew
 
 train_test_dummy = pd.get_dummies(train_test)
 
 numeric_features = train_test_dummy.dtypes[train_test_dummy.dtypes != object].index
-skewed_features = train_test_dummy[numeric_features].apply(lambda x: skew(x)).sort_values(ascending=False)
+skewed_features = (
+    train_test_dummy[numeric_features]
+    .apply(lambda x: skew(x))
+    .sort_values(ascending=False)
+)
 high_skew = skewed_features[skewed_features > 0.5]
 skew_index = high_skew.index
 
 # Normalize skewed features using log_transformation
-    
+
 for i in skew_index:
-    train_test_dummy[i] = np.log1p(train_test_dummy[i] )
+    train_test_dummy[i] = np.log1p(train_test_dummy[i])
 
 target_log = np.log1p(target)
 
@@ -128,11 +154,11 @@
 X_test = train_test_dummy[4000:]
 
 xgb = XGBRegressor()
-xgb.fit(X_train,target_log)
+xgb.fit(X_train, target_log)
 
 test_pred = xgb.predict(X_test)
-submission = pd.DataFrame(test_id, columns = ['Id'])
+submission = pd.DataFrame(test_id, columns=["Id"])
 test_pred = np.expm1(test_pred)
-submission['SalePrice'] = test_pred 
+submission["SalePrice"] = test_pred
 submission.head()
-submission.to_csv("xgb.csv", index = False, header = True)
+submission.to_csv("xgb.csv", index=False, header=True)