Data Science

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...

  * Data ko split krna jruri hai, isse hamara model rough tough banenga * Data ko split krne ke do traike traditional way hai train_test_split, aur doosra tarika hai cross validation , cross validation is more sophisticated one * remember when we use k fold or cross validation , we don't partition it , we don't fit it , we don't predict it *There are some feature selection techniques are there first is Selectkbest, second is RFE (recursive feature elimination) * then we have something for dimensionalty reduction  and that is pca(Principal component analysis) * # kya hota hai ki koi bhi chiz 3 dimension mai hai,  # aur usme hame mushkil ho rahi hai ki ye a, b,c hai # to use hm two dimension mai daal denge , aur isse hme a,b,c clear ho jayenge # So by reducing the dimension , you are acheving the separation that is pca * See selectkbest is selecting individual Feature , RFE is giving multiple feature at a time and pca is kind of telling that with less feature you can a...