diff --git a/build.py b/build.py
index 0f57d9f2410a0b17d7782d47bd07932671324a55..dc83f4ba41a35f0c4eec562e99bf64b1ebef57f5 100644
--- a/build.py
+++ b/build.py
@@ -18,6 +18,9 @@ from sklearn.feature_extraction.text import CountVectorizer
 from sklearn.model_selection import train_test_split
 from datetime import datetime
 
+import warnings
+warnings.filterwarnings('ignore')
+
 #Functions for splitting into train/test sets.
 def split_dataset(df, test_size=0.25):
     movies_train, movies_test = train_test_split(df, test_size=test_size, shuffle=True)
@@ -44,6 +47,7 @@ vectorizer = CountVectorizer()
 # url = "https://github.com/Jamchello/WikiMoviePlots/blob/master/wiki_movie_plots_deduped.csv?raw=true"
 movies = pd.read_csv('./data/Data.csv', delimiter=",")
 
+print("Pre-processing data...")
 MIN_MOVIES = 500
 movies['Count'] = 1
 
@@ -446,13 +450,16 @@ upsampled_df = pd.concat(resampled)
 movies = upsampled_df.copy()
 optimal_val_split_lr = 0.1
 
+print("Training model...")
 movies_train, movies_test, x_train_lr, y_train_lr, x_test_lr, y_test_lr = get_train_test(upsampled_df, test_size=optimal_val_split_lr)
 lr_classifier = LogisticRegression(max_iter=2000)
 classifier = OneVsRestClassifier(lr_classifier)
 classifier.fit(x_train_lr, y_train_lr)
 
 
+print("Pickling files...")
 for item in ['mlb', 'vectorizer','model']:
+  print("Saving " + f'{item}.pickle')
   with open(f'{item}.pickle', 'wb') as handle:
     pickle.dump(item, handle)