house.shape
house.info()
features_to_cast = ['MSSubClass']
cast_cont_to_cat(house, features_to_cast)
print(get_continuous_features(house))
print(get_categorical_features(house))
features_to_drop = ['Id', 'SalePrice']
features_to_drop.extend(get_features_to_drop_on_missingdata(house, 0.25))
house1 = drop_features(house, features_to_drop)
house1.info()
imputable_cat_features = get_categorical_features(house1)
cat_imputer = get_categorical_imputers(house1, imputable_cat_features)
house1[imputable_cat_features] = cat_imputer.transform(
house1[imputable_cat_features])
imputable_cont_features = get_continuous_features(house1)
cont_imputer = get_continuous_imputers(house1, imputable_cont_features)
house1[imputable_cont_features] = cont_imputer.transform(
house1[imputable_cont_features])
house1.info()
house2 = ohe(house1, imputable_cat_features)
scaler = get_scaler(house2)
house3 = scaler.transform(house2)
house3 = pd.DataFrame(house3, columns=house2.columns)
print(titanic_train.shape)
print(titanic_train.info())
titanic_train1 = utils.drop_features(
titanic_train, ['PassengerId', 'Name', 'Survived', 'Ticket', 'Cabin'])
#type casting
utils.cast_to_cat(titanic_train1, ['Sex', 'Pclass', 'Embarked'])
cat_features = utils.get_categorical_features(titanic_train1)
print(cat_features)
cont_features = utils.get_continuous_features(titanic_train1)
print(cont_features)
#handle missing data(imputation)
cat_imputers = utils.get_categorical_imputers(titanic_train1, cat_features)
titanic_train1[cat_features] = cat_imputers.transform(
titanic_train1[cat_features])
cont_imputers = utils.get_continuous_imputers(titanic_train1, cont_features)
titanic_train1[cont_features] = cont_imputers.transform(
titanic_train1[cont_features])
#adding new levels
#titanic_train['Pclass'] = titanic_train['Pclass'].cat.add_categories([4,5])
#one hot encoding
titanic_train2 = utils.ohe(titanic_train1, cat_features)
scaler = preprocessing.StandardScaler()
X_train = scaler.fit_transform(titanic_train2)
y_train = titanic_train['Survived']
