jackty9

from sklearn.datasets import make_regression
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_regression
import pandas as pd

# generate dataset
X, y = make_regression(n_samples=100, n_features=50, n_informative=10)
#assign column names 
col_list = ['col_' + str(x) for x in range(0,50)]
#create a dataframe table

jackty9

from sklearn.datasets import make_classification
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_classif
import pandas as pd

# generate dataset
X, y = make_classification(n_samples=100, n_features=50, n_informative=10)
#assign column names 
col_list = ['col_' + str(x) for x in range(0,50)]
#create a dataframe table

jackty9

from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
import pandas as pd

#import raw data, data can be found in Github directory 
#https://github.com/jackty9/Feature_Selection_in_Python/blob/master/car_data.csv
df = pd.read_csv("car_data.csv")
X = df1.iloc[:,0:5]
X = pd.get_dummies(X)
y = df1.iloc[:,-1]

jackty9

from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import mutual_info_classif
import pandas as pd

#import raw data, data can be found in Github directory 
#https://github.com/jackty9/Feature_Selection_in_Python/blob/master/car_data.csv
df1 = pd.read_csv("car_data.csv")
X = df1.iloc[:,0:5]
X = pd.get_dummies(X)
y = df1.iloc[:,-1]

jackty9

#Load needed libraries
from sklearn.datasets import load_boston
from sklearn.feature_selection import RFE
from sklearn.linear_model import LinearRegression
import pandas as pd
import numpy as np

# load_boston() sklearn dataset to boston
boston = load_boston()

jackty9

#Load needed libraries
from sklearn.datasets import load_boston
from mlxtend.feature_selection import SequentialFeatureSelector as SFS
from sklearn.linear_model import LinearRegression
import pandas as pd
import numpy as np

# load_boston() sklearn dataset to boston
boston = load_boston()

jackty9

#Load needed libraries
from sklearn.datasets import load_boston
from mlxtend.feature_selection import SequentialFeatureSelector as SFS
from sklearn.linear_model import LinearRegression
import pandas as pd
import numpy as np

# load_boston() sklearn dataset to boston
boston = load_boston()

jackty9

#Load needed libraries
from mlxtend.feature_selection import SequentialFeatureSelector as SFS
from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
import matplotlib.pyplot as plt

#import and prepare data
boston = load_boston()
X, y = boston.data, boston.target

jackty9

#PART 1
# import random undersampling and other necessary libraries 
from collections import Counter
from imblearn.under_sampling import RandomUnderSampler
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

jackty9

#PART 1
# import SMOTE oversampling and other necessary libraries 
from collections import Counter
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)