Machine learning Hack

1. To calculate  the null values in dependent variable in dataset data.

data.isnull().sum()

2. To compare the particular feature with dependent variable.

pd.crosstab(data.Gender,data.Loan_Status)

3.To check that particular categorical coloum has how many different values(data - dataframe, gender- coloum)

print(data.Gender.value_counts())

4. To fill the na in the categorical column with highest number of values in it

data['Gender'].fillna(data['Gender'].mode()[0],inplace = True)

5.  To get the set of numerical feature coloum

numerical_feature_columns = list(df._get_numeric_data().columns)

numerical_feature_columns

6. To get the set of categorical coloum

categorical_feature_columns = list(set(df.columns) - set(df._get_numeric_data().columns))

categorical_feature_columns

7.  To drop the  particular coloum in dataframe

data = data.drop(['Loan_ID'],axis=1)

8. To convert features into dummy variable in oneshot

X_features = list( data.columns )

X_features

data = pd.get_dummies(data[X_features], drop_first = True )

9. To take only feature which provided by rfecv

x_train_rfecv = rfecv.transform(x_train)

x_train_rfecv.shape 

10.  To do min - max scaler

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler(feature_range=(0,1)) 

rescaled = scaler.fit_transform(x)

print(rescaled)

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