There's two issues here.

1. By taking .values you actually access the underlying numpy array; you no longer have a pandas.Series. numpy arrays do not have an apply method.
2. You are trying to use apply for a simple multiplication, which will be orders of magnitude slower than using a vectorized approach.

See below:

import pandas as pd
import numpy as np

df = pd.DataFrame({'a': np.arange(1000, dtype=np.float64)})
print(type(df['a']))
# Gives pandas.core.series.Series

print(type(df['a'].values))
# Gives numpy.ndarray

# The vectorized approach
df['a'] = df['a'] * 1.3

It would be helpful if you could post the full stack trace, so that we can see which line your error occurs at. In general, the more information you can provide in a question, the better.

In this case, it looks like your full_model_pipeline may somehow become a numpy array. Since you have a one-element pipeline, you could try changing

full_model_pipeline = Pipeline(steps =[
                        ('full_pipeline',full_pipeline),
                        ('model',LinearRegression())
                    ])

full_model_pipeline.fit(X_train,y_train)

to

model = LinearRegression()
model.fit(X_train, y_train)

This is more of a programming question than a data science question and is therefore better suited for stackoverflow. The vectorizer is trying to convert the string values to lowercase using the .lower() method. However, you are passing in a numpy array, which does not have this method. Instead of passing in a numpy array, you can simply use a pandas dataframe (which does have the .lower() method, see the documentation) by removing the .values attribute in dataset.iloc[:,:-1].values.