models/train_classifier.py
def build_model():
    """The build_model function build a model pipeline

    Args: None

    Returns:
        model(pipeline): model pipeline for fitting, prediction and scoring

    """
    # create pipeline
    plu = Pipeline([
                    ('cvect', CountVectorizer(tokenizer=tokenize,
                                 max_df=0.86, ngram_range=(1,2))),
                      ('tfidt', TfidfTransformer()),
                     ("mascaler", MaxAbsScaler()),
                     ('rforest', MultiOutputClassifier(RandomForestClassifier()))
            ])


    return plu
The build_model function build a model pipeline

    Args: None

    Returns:
        model(pipeline): model pipeline for fitting, prediction and scoring

    
The build_model function build a model pipeline
Args: None
The build_model function build a model pipeline Args : None
def build_model():
    plu = Pipeline([
                    ('cvect', CountVectorizer(tokenizer=tokenize,
                                 max_df=0.86, ngram_range=(1,2))),
                      ('tfidt', TfidfTransformer()),
                     ("mascaler", MaxAbsScaler()),
                     ('rforest', MultiOutputClassifier(RandomForestClassifier()))
            ])
    return plu
def build_model ( ) : plu = Pipeline ( [ ( 'cvect' , CountVectorizer ( tokenizer = tokenize , max_df = 0.86 , ngram_range = ( 1 , 2 ) ) ) , ( 'tfidt' , TfidfTransformer ( ) ) , ( "mascaler" , MaxAbsScaler ( ) ) , ( 'rforest' , MultiOutputClassifier ( RandomForestClassifier ( ) ) ) ] ) return plu
The build_model function build a model pipeline
Args: None
The build_model function build a model pipeline Args : None
"""The build_model function build a model pipeline

    Args: None

    Returns:
        model(pipeline): model pipeline for fitting, prediction and scoring

    """ # create pipeline
model pipeline for fitting, prediction and scoring
model pipeline for fitting prediction and scoring
model(pipeline)