diff --git a/README.md b/README.md
index 9236c2f..66199a1 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,6 @@
 # Amazon product rating
  Project for rating products on amazon based on reviewer/product data
+
+Please see https://kasra-eshaghi.github.io/blog/software/amazon-product-rating for more details.
+
+The data for this project can be found at https://www.kaggle.com/c/csc2515-rating-prediction 
\ No newline at end of file
diff --git a/all_models.py b/all_models.py
new file mode 100644
index 0000000..cf18a2a
--- /dev/null
+++ b/all_models.py
@@ -0,0 +1,183 @@
+# -*- coding: utf-8 -*-
+
+import tensorflow as tf
+from tensorflow import keras
+
+def model_1(train_review_time, train_categories, train_summaries, train_reviews, cv_summary_fit, cv_review_fit):
+    # model 1 - min val = 0.5619
+    input_time = keras.layers.Input(shape = [train_review_time.shape[1]], name='time_input')
+    input_cat = keras.layers.Input(shape = [train_categories.shape[1]], name='cat_input')
+    input_summary = keras.layers.Input(shape = [train_summaries.shape[1]], name='summary_input')
+    input_review = keras.layers.Input(shape = [train_reviews.shape[1]], name='review_input')
+    
+    embed_summary = keras.layers.Embedding(input_dim = (len(cv_summary_fit.vocabulary_)+1), output_dim = 5, input_length = train_summaries.shape[1], name = 'embed_summary')(input_summary)
+    embed_review = keras.layers.Embedding(input_dim = (len(cv_review_fit.vocabulary_)+1), output_dim = 32, input_length = train_reviews.shape[1], name = 'embed_review')(input_review)
+    
+    flatten_summary = keras.layers.Flatten()(embed_summary)
+    flatten_review = keras.layers.Flatten()(embed_review)
+    
+    concat_all = keras.layers.concatenate([input_time, input_cat, flatten_summary, flatten_review])
+    
+    hidden_1 = keras.layers.Dense(units = 100, activation = 'relu', name = 'hidden')(concat_all)
+    
+    output = keras.layers.Dense(units = 1, name = 'output')(hidden_1)
+    
+    model = keras.Model(inputs=[input_time, input_cat, input_summary, input_review], outputs = [output])
+    
+    model.compile(loss = 'mse', optimizer = 'Adam') 
+    
+    return model
+
+def model_2(train_reviews, cv_review_fit):
+    #min val = 0.6
+    model = keras.models.Sequential([
+    keras.layers.Embedding(input_dim = (len(cv_review_fit.vocabulary_)+1), output_dim = 32, input_length = train_reviews.shape[1], name = 'embed_review'),
+    keras.layers.LSTM(units = 128),
+    keras.layers.Dense(units = 1, name = 'output'),
+    ])
+    
+    model.compile(loss = 'mse', optimizer = 'Adam') 
+    
+    return model
+    
+def model_3(train_review_time, train_categories, train_summaries, train_reviews, cv_summary_fit, cv_review_fit):
+    # model 3 - min val = 0.58
+    input_time = keras.layers.Input(shape = [train_review_time.shape[1]], name='time_input')
+    input_cat = keras.layers.Input(shape = [train_categories.shape[1]], name='cat_input')
+    input_summary = keras.layers.Input(shape = [train_summaries.shape[1]], name='summary_input')
+    input_review = keras.layers.Input(shape = [train_reviews.shape[1]], name='review_input')
+    
+    embed_cat = keras.layers.Embedding(input_dim = 5, output_dim = 5, input_length = train_categories.shape[1], name = 'embed_cat')(input_cat)
+    embed_summary = keras.layers.Embedding(input_dim = (len(cv_summary_fit.vocabulary_)+1), output_dim = 5, input_length = train_summaries.shape[1], name = 'embed_summary')(input_summary)
+    embed_review = keras.layers.Embedding(input_dim = (len(cv_review_fit.vocabulary_)+1), output_dim = 32, input_length = train_reviews.shape[1], name = 'embed_review')(input_review)
+    
+    flatten_cat = keras.layers.Flatten()(embed_cat)
+    flatten_summary = keras.layers.Flatten()(embed_summary)
+    flatten_review = keras.layers.Flatten()(embed_review)
+    
+    concat_all = keras.layers.concatenate([input_time, flatten_cat, flatten_summary, flatten_review])
+    
+    hidden_1 = keras.layers.Dense(units = 100, activation = 'relu', name = 'hidden')(concat_all)
+    
+    output = keras.layers.Dense(units = 1, name = 'output')(hidden_1)
+    
+    model = keras.Model(inputs=[input_time, input_cat, input_summary, input_review], outputs = [output])
+    
+    model.compile(loss = 'mse', optimizer = 'Adam') 
+            
+    return model
+
+def model_4(train_review_time, train_categories, train_summaries, train_reviews, cv_summary_fit, cv_review_fit):
+    # model 4 - min val = 0.86
+    input_time = keras.layers.Input(shape = (train_review_time.shape[1],), name='time_input')
+    input_cat = keras.layers.Input(shape = (train_categories.shape[1],), name='cat_input')
+    
+    embed_cat = keras.layers.Embedding(input_dim = 5, output_dim = 5, input_length = train_categories.shape[1], name = 'embed_cat')(input_cat)
+    flatten_cat = keras.layers.Flatten(name = 'flatten_cat')(embed_cat)
+    concat_time_cat = keras.layers.concatenate([input_time, flatten_cat], name = 'concat_time_cat')
+    dense_time_cat = keras.layers.Dense(units = 10, activation = 'relu')(concat_time_cat)
+    
+    
+    input_summary = keras.layers.Input(shape = (train_summaries.shape[1],), name='summary_input')
+    embed_summary = keras.layers.Embedding(input_dim = (len(cv_summary_fit.vocabulary_)+1), output_dim = 32, input_length = train_summaries.shape[1], name = 'embed_summary')(input_summary)
+    lstm_summary = keras.layers.LSTM(units = 128)(embed_summary)
+    dense_summary = keras.layers.Dense(units = 1)(lstm_summary)
+
+    input_review = keras.layers.Input(shape = (train_reviews.shape[1],), name='review_input')
+    embed_review = keras.layers.Embedding(input_dim = (len(cv_review_fit.vocabulary_)+1), output_dim = 32, input_length = train_reviews.shape[1], name = 'embed_review')(input_review)
+    lstm_review = keras.layers.LSTM(units = 128)(embed_review) 
+    dense_review = keras.layers.Dense(units = 1)(lstm_review)   
+    
+    concat_all = keras.layers.concatenate([dense_time_cat, dense_summary, dense_review])
+    
+    
+    output = keras.layers.Dense(units = 1, name = 'output')(concat_all)
+    
+    model = keras.Model(inputs=[input_time, input_cat, input_summary, input_review], outputs = [output])
+    
+    model.compile(loss = 'mse', optimizer = 'Adam') 
+            
+    return model
+
+def model_5(train_review_time, train_categories, train_summaries, train_reviews, cv_summary_fit, cv_review_fit):
+    # model 5 - min val =0.55
+    input_time = keras.layers.Input(shape = [train_review_time.shape[1]], name='time_input')
+    input_cat = keras.layers.Input(shape = [train_categories.shape[1]], name='cat_input')
+    input_summary = keras.layers.Input(shape = [train_summaries.shape[1]], name='summary_input')
+    input_review = keras.layers.Input(shape = [train_reviews.shape[1]], name='review_input')
+    
+    embed_summary = keras.layers.Embedding(input_dim = (len(cv_summary_fit.vocabulary_)+1), output_dim = 16, input_length = train_summaries.shape[1], name = 'embed_summary')(input_summary)
+    embed_review = keras.layers.Embedding(input_dim = (len(cv_review_fit.vocabulary_)+1), output_dim = 32, input_length = train_reviews.shape[1], name = 'embed_review')(input_review)
+    
+    conv1d_summary = keras.layers.Conv1D(filters = 128, kernel_size=5)(embed_summary)
+    conv1d_review = keras.layers.Conv1D(filters = 128, kernel_size=5)(embed_review)
+    
+    flatten_summary = keras.layers.Flatten()(conv1d_summary)
+    flatten_review = keras.layers.Flatten()(conv1d_review)
+    
+    concat_all = keras.layers.concatenate([input_time, input_cat, flatten_summary, flatten_review])
+    
+    hidden_1 = keras.layers.Dense(units = 100, activation = 'relu', name = 'hidden')(concat_all)
+    
+    output = keras.layers.Dense(units = 1, name = 'output')(hidden_1)
+    
+    model = keras.Model(inputs=[input_time, input_cat, input_summary, input_review], outputs = [output])
+    
+    model.compile(loss = 'mse', optimizer = 'Adam') 
+    
+    return model
+
+def model_6(train_summaries, train_reviews, vocab_size, max_sent_len):
+    # create model, val error = 0.63
+    input_reviews = keras.layers.Input(shape = (train_reviews.shape[1],), name = 'review_input')
+    input_summaries = keras.layers.Input(shape = (train_summaries.shape[1],), name = 'summary_input')
+    
+    embed_words = keras.layers.Embedding(input_dim = vocab_size, output_dim = 8, input_length = max_sent_len, mask_zero=True, name = 'word_embedding')
+    
+    input_reviews_encoded = embed_words(input_reviews)
+    input_summaries_encoded = embed_words(input_summaries)
+    
+    
+    concat_embeded_sentences = keras.layers.Concatenate(axis = -1, name = 'summary_and_review')([input_reviews_encoded, input_summaries_encoded])
+        
+    lstm_sentences = keras.layers.LSTM(64, return_sequences = False, name = 'lstm_out')(concat_embeded_sentences)
+    
+    output = keras.layers.Dense(1)(lstm_sentences)
+    
+    model = keras.Model(inputs=[input_reviews, input_summaries], outputs = [output])
+    
+    model.compile(loss = 'mse', optimizer = 'Adam') 
+
+def model_7(train_reviews, vocab_size, max_sent_len):
+    #%% create model, val error = 0.5
+    input_reviews = keras.layers.Input(shape = (train_reviews.shape[1],), name = 'review_input')
+    
+    embed_review = keras.layers.Embedding(input_dim = vocab_size, output_dim = 32, input_length = max_sent_len, mask_zero=False)(input_reviews)
+    
+    gru_review = keras.layers.GRU(units = 128, return_sequences=False)(embed_review)
+    #gru_review_2 = keras.layers.GRU(units = 128)(gru_review)
+    
+    output = keras.layers.Dense(1)(gru_review)
+    
+    model = keras.Model(inputs=[input_reviews], outputs = [output])
+    
+    model.compile(loss = 'mse', optimizer = 'Adam') 
+
+def build_model(n_hidden = 1, n_neurons = 50, learning_rate = 3e-3,  inputshape= 5):
+    model = keras.models.Sequential()
+    # add input layer
+    model.add(keras.layers.InputLayer(input_shape = (inputshape,)))
+    
+    model.add(keras.layers.Dense(n_neurons, activation = 'relu', input_shape = (inputshape,)))
+    # add desired number of hidden layers:
+    for layer in range(n_hidden-1):
+        model.add(keras.layers.Dense(n_neurons, activation = 'relu'))
+        
+    # add last dense layer to get output:
+    model.add(keras.layers.Dense(1))
+    
+    # compile model:
+    model.compile(loss ='mse', optimizer = keras.optimizers.SGD(lr = learning_rate))
+    
+    return model
+    
diff --git a/data_analysis.py b/data_analysis.py
new file mode 100644
index 0000000..75cd6aa
--- /dev/null
+++ b/data_analysis.py
@@ -0,0 +1,147 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Dec  6 17:43:10 2020
+
+@author: Kasra
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer, PorterStemmer 
+
+# import data
+df = pd.read_json('train.json', lines=True)
+# drop unnecessary columns
+df.drop('image', inplace = True, axis = 1)
+df.drop('reviewTime', inplace = True, axis = 1)
+df.drop('reviewHash', inplace = True, axis = 1)
+
+# clean up price data:
+df['price'] = df['price'].replace({'\$':''}, regex = True)
+
+# clean up review text and summary text:
+df['reviewText'] = df['reviewText'].replace(np.nan,'', regex = True)
+df['summary'] = df['summary'].replace(np.nan,'', regex = True)
+
+#%% Look at rating for each music category over time
+categories = df['category'].unique()
+for category in categories:
+    colors = ['red', 'blue', 'green', 'magenta','gold']
+    data = []
+    for overall in range(1,6,1):
+        # find dataframe for specific overall and category:
+        temp_df = df.loc[(df['category'] == category) & (df['overall'] == overall)]
+        data.append(temp_df.unixReviewTime)
+
+
+
+    plt.hist(data, color = colors, label = ['1','2','3','4','5'], bins = [0.9e9, 1e9, 1.1e9, 1.2e9, 1.3e9, 1.4e9, 1.5e9, 1.6e9])
+    plt.title(category)
+    plt.xlim(0.9e9, 1.6e9)
+    plt.ylim(0, 30000)
+    plt.xlabel('unixReviewTime ')
+    plt.ylabel('Frequency')
+
+    plt.show()
+    
+#%% Look at statistics for review and summary data:
+cv = CountVectorizer(ngram_range = (1,1), lowercase = True, analyzer = 'word', binary = True)
+analyzer = cv.build_analyzer()
+
+# count number of words in the summary and reviews
+summary_texts = list(df['summary'])
+summary_lengths = []
+for summary in summary_texts:
+    # tokenize:
+    summary_tok = analyzer(summary)
+    summary_lengths.append(len(summary_tok))
+
+review_texts = list(df['reviewText'])
+review_lengths = []
+for review in review_texts:
+    # tokenize:
+    review_tok = analyzer(review)
+    review_lengths.append(len(review_tok))
+
+plt.boxplot([summary_lengths], vert = False, labels = ['Summary'])
+plt.title('Summary Length')
+plt.xlim(0,40)
+plt.show()
+
+plt.boxplot([review_lengths], vert = False, labels = ['Review'])
+plt.title('Review Length')
+plt.xlim(0, 5500)
+plt.show()
+
+# count number of unique words in summary and review
+cv_fit_summary = cv.fit(summary_texts)
+print('number of unique words in summaries:', len(cv_fit_summary.vocabulary_.keys()))
+
+cv_fit_review = cv.fit(review_texts)
+print('number of unique words in reviews:', len(cv_fit_review.vocabulary_.keys()))
+
+#%% look at common words in summary and review data:
+# get common stop words
+custom_stop_words = set(stopwords.words('english'))
+# add additional stop words:
+ps = PorterStemmer()
+lemmatizer = WordNetLemmatizer() 
+additional_stop_words = {'pop', 'classical', 'jazz', 'dance', 'electronic', 'rock'}
+additional_stop_words_normalized=set()
+for additional_word in additional_stop_words:
+    category_lem = lemmatizer.lemmatize(additional_word)
+    category_stem = ps.stem(additional_word)
+    additional_stop_words_normalized.update({category_lem,category_stem})
+
+custom_stop_words.update(additional_stop_words_normalized)
+summary_texts = list(df['summary'])
+
+
+cv_summary = CountVectorizer(ngram_range=(1,1), lowercase = True, stop_words=list(custom_stop_words), analyzer = 'word', binary = True, max_features = 30)
+cv_summary_fit = cv_summary.fit_transform(summary_texts)
+
+word_count = np.sum(cv_summary_fit.toarray(), axis = 0)
+
+df_temp = pd.DataFrame({'word':cv_summary.get_feature_names(), 'word_count':word_count})
+df_temp.sort_values(by='word_count', ascending=False, inplace = True)
+df_temp.plot(x='word', y='word_count', kind = 'bar')
+plt.xlabel('Top 30 Frequent Unigrams')
+plt.ylabel('Frequency')
+plt.legend('')
+plt.title('Summary Word Distribution')
+plt.ylim(0, 30000)
+plt.show()
+#%%
+review_texts = list(df['reviewText'])
+cv_review = CountVectorizer(ngram_range=(1,1), lowercase = True, stop_words=list(custom_stop_words), analyzer = 'word', binary = True, max_features = 30)
+cv_review_fit = cv_review.fit_transform(review_texts)
+
+word_count = np.sum(cv_review_fit.toarray(), axis = 0)
+
+df_temp = pd.DataFrame({'word':cv_review.get_feature_names(), 'word_count':word_count})
+df_temp.sort_values(by='word_count', ascending=False, inplace = True)
+df_temp.plot(x='word', y='word_count', kind = 'bar')
+plt.xlabel('Top 30 Frequent Unigrams')
+plt.ylabel('Frequency')
+plt.legend('')
+plt.ylim(0, 70000)
+plt.title('Review Word Distribution')
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/final_model.h5 b/final_model.h5
new file mode 100644
index 0000000..accd3aa
Binary files /dev/null and b/final_model.h5 differ
diff --git a/final_submission.py b/final_submission.py
new file mode 100644
index 0000000..44dd5e9
--- /dev/null
+++ b/final_submission.py
@@ -0,0 +1,236 @@
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
+from nltk.corpus import stopwords
+import tensorflow as tf
+from tensorflow import keras
+
+
+def create_data_processors(train_data, n_bigrams_summary, n_unigram_review):
+    '''
+        This function create the preprocessors necessary for enconding the time, categorical, and text data
+        INTPUT:
+            train_data - all training data (in pd format)
+        OUTPUT:
+            scalar_fit, cat_encoder_fit, cv_summary_fit, cv_review_fit - preprocessing objects to be used
+            
+    '''
+    pass
+
+    scalar = MinMaxScaler(feature_range = (0,1), copy = False)
+    scalar_fit = scalar.fit(pd.DataFrame(train_data['unixReviewTime']))
+    
+    cat_encoder = OneHotEncoder()
+    cat_encoder_fit = cat_encoder.fit(pd.DataFrame(train_data['category']))
+    
+    custom_stop_words = set(stopwords.words('english'))
+    
+    summary_train = list(train_data['summary'])
+    cv_summary = CountVectorizer(ngram_range = (2,2), lowercase = True, stop_words=list(custom_stop_words), analyzer = 'word', binary = True, max_features = n_bigrams_summary)
+    cv_summary_fit = cv_summary.fit(summary_train)
+    
+    review_train = list(train_data['reviewText'])
+    cv_review = CountVectorizer(ngram_range = (1,1), lowercase = True, stop_words=list(custom_stop_words), analyzer = 'word', binary = True, max_features = n_unigram_review)
+    cv_review_fit = cv_review.fit(review_train)
+
+    return scalar_fit, cat_encoder_fit, cv_summary_fit, cv_review_fit
+
+def clean_data(df):
+    '''
+        This function cleans up the dataset
+    '''
+    # drop unnecessary columns
+    df.drop('image', inplace = True, axis = 1)
+    df.drop('reviewTime', inplace = True, axis = 1)
+    df.drop('reviewHash', inplace = True, axis = 1)
+    df.drop('price', inplace = True, axis = 1)
+    
+    # clean up review text and summary text:
+    df['reviewText'] = df['reviewText'].replace(np.nan,'', regex = True)
+    df['summary'] = df['summary'].replace(np.nan,'', regex = True)
+
+    return df
+
+def create_text_processor(train_data, n_vocab):
+    ''' 
+        This function creates the tokenizer for processing text data
+        OUTPUT:
+            tokenizer - tokenizer instance that can be used to create text sequences
+    '''
+    
+    summary_train = list(train_data['summary'])
+    review_train = list(train_data['reviewText'])
+    
+    text_all = summary_train+ review_train
+    
+    tokenizer = keras.preprocessing.text.Tokenizer(num_words = n_vocab)
+    tokenizer.fit_on_texts(text_all)
+    
+    return tokenizer
+
+def use_text_process(data, tokenizer, max_sent_len):
+    '''
+        This function turns the summary and reviewText data into sequences that are padded
+        tokenizer must be provided
+        OUTPUTS:
+            train_summaries
+            train_reviews
+    '''
+    summary = list(data['summary'])
+    review = list(data['reviewText'])
+    
+    # turn into sequence
+    summary_numbers = tokenizer.texts_to_sequences(summary)
+    review_numbers = tokenizer.texts_to_sequences(review)
+    
+    # pad:
+    summary_numbers = keras.preprocessing.sequence.pad_sequences(summary_numbers, padding = 'post', maxlen = max_sent_len)
+    review_numbers = keras.preprocessing.sequence.pad_sequences(review_numbers, padding = 'post', maxlen = max_sent_len)
+    
+    return summary_numbers, review_numbers
+ 
+def create_inputs(data, scalar_fit, cat_encoder_fit, cv_summary_fit, cv_review_fit, embed_cat, use_embed_summary, use_embed_review):
+    '''
+        This function creates the training data based on the preprocessing objects scalar_fit, cat_encoder_fit, cv_summary_fit, cv_review_fit
+        use_embed_summary, use_embed_review are used to indicate how text data should be processed
+    '''
+    # create time data
+    review_time_normalized = scalar_fit.transform(pd.DataFrame(data['unixReviewTime']))
+    
+    # create category data
+    categories_1hot = cat_encoder_fit.transform(pd.DataFrame(data['category'])).toarray()
+    if embed_cat:
+       categories_1hot = embed_categories(categories_1hot)
+    
+    # create summary data
+    summary = list(data['summary'])
+    if use_embed_summary:
+        emb_sentence_length = 15
+        summaries_encoded = embed_data(summary, cv_summary_fit, emb_sentence_length)
+    else:
+        summaries_encoded = cv_summary_fit.transform(summary)
+
+    # create review data
+    review = list(data['reviewText'])
+    if use_embed_review:
+        emb_sentence_length = 300
+        reviews_encoded = embed_data(review, cv_review_fit, emb_sentence_length)
+    else:
+        reviews_encoded = cv_review_fit.transform(review)
+        
+    return review_time_normalized, categories_1hot, summaries_encoded, reviews_encoded
+
+def embed_data(corp, cv, emb_sentence_length):
+    '''
+        This function prepares the text data for the embedding layer:
+        INPUTS:
+            corp: list of strings, each of which is a datapoint
+            cv - count vectorizer instance used
+            emb_sentence_length - number of words to be considered in each sentence
+        OUTPUTS:
+            corp_for_embedding
+    '''
+    analyzer = cv.build_analyzer()
+    corp_emb = []
+    
+    # embed the sentence based on the words in dictionary
+    for sent in corp:
+        # tokenize sentence
+        sent_tokenized = analyzer(sent)
+        sent_emb = []
+        for i, word in enumerate(sent_tokenized):
+            #onlt keep first few words in the sentence
+            if i > emb_sentence_length:
+                break
+            # check if word is in the vocabulary
+            if word in cv.vocabulary_:
+                sent_emb.append(cv.vocabulary_[word]+1) # add one to avoid 0, as the vocabulary includes index 0 as well
+                
+        # add sentences to embedded corpus
+        corp_emb.append(sent_emb)
+            
+    # padd corpus to make them all the same length
+    corp_pad = keras.preprocessing.sequence.pad_sequences(corp_emb, emb_sentence_length, padding = 'post')
+    
+    return corp_pad
+
+def embed_categories(categories_1hot):
+    '''
+        This function embeds the categories
+    '''
+    categories_embedded = np.where(categories_1hot == 1)[1]
+    categories_embedded = categories_embedded.reshape((-1,1))
+    
+    return categories_embedded
+
+
+############## Preprocessing #################
+# read data
+all_data = pd.read_json('train.json', lines=True)
+all_data = clean_data(all_data)
+
+# split training data
+train_data, val_data = train_test_split(all_data, test_size = 0.3, random_state = 0)
+y_train = np.array(train_data['overall'])
+y_val = np.array(val_data['overall'])
+
+# create tokenizer for text data
+n_vocab = 5000
+tokenizer = create_text_processor(train_data, n_vocab)
+vocab_size = len(tokenizer.word_index)+1
+
+# turn text data into sequences
+max_sent_len = 100
+train_summaries, train_reviews = use_text_process(train_data, tokenizer, max_sent_len)
+val_summaries, val_reviews = use_text_process(val_data, tokenizer, max_sent_len)
+
+train_texts = np.concatenate((train_reviews, train_summaries), axis = 1)
+val_texts = np.concatenate((val_reviews, val_summaries), axis = 1)
+
+# create one-hot encoder for categories, and scalar transform for review time. NOTE: cv_summary_fit, cv_review_fit are not used.
+scalar_fit, cat_encoder_fit, cv_summary_fit, cv_review_fit= create_data_processors(train_data, 200, 200)
+train_review_time, train_categories, _, _ = create_inputs(train_data, scalar_fit, cat_encoder_fit, cv_summary_fit, cv_review_fit, False, True, True)
+val_review_time, val_categories, _, _ = create_inputs(val_data, scalar_fit, cat_encoder_fit, cv_summary_fit, cv_review_fit, False, True, True)
+
+################# Fit model ##################
+# hyperparameters
+e_size = 16
+l_size = 64
+d_size = 200
+
+            
+# add early stopping criterion
+model_early_stopping_callback = tf.keras.callbacks.EarlyStopping(
+    monitor = 'val_loss',
+    patience = 5,
+    mode = 'min',
+    )
+call_backs = [model_early_stopping_callback]
+
+# inputs:
+input_text = keras.layers.Input(shape = (train_texts.shape[1],), name = 'text_input')
+input_time = keras.layers.Input(shape = (train_review_time.shape[1],), name = 'review_time')
+input_cat = keras.layers.Input(shape = (train_categories.shape[1],), name = 'categories')
+
+# text embedding and LSTM:
+embed_words = keras.layers.Embedding(input_dim = vocab_size, output_dim = e_size, input_length = max_sent_len*2, mask_zero=True, name = 'word_embedding')(input_text)
+LSTM_review = keras.layers.LSTM(units = l_size, return_sequences=False)(embed_words)
+
+# concatenate all processed inputs
+concat_all = keras.layers.concatenate([input_time, input_cat, LSTM_review], name = 'concat_all')
+
+# dense layer # 1
+dense_layer = keras.layers.Dense(units = d_size, activation = 'relu')(concat_all)
+
+# dense layer # 2
+output = keras.layers.Dense(1)(dense_layer)
+
+model = keras.Model(inputs=[input_text, input_time, input_cat], outputs = [output])
+model.compile(loss = 'mse', optimizer = 'Adam') 
+
+# fit model
+history = model.fit((train_texts, train_review_time, train_categories), y_train, batch_size=32, epochs = 100, validation_data = (((val_texts, val_review_time, val_categories)),y_val), callbacks = call_backs)
+
+########## THANK YOU #########
\ No newline at end of file
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diff --git a/preprocessing.py b/preprocessing.py
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+++ b/preprocessing.py
@@ -0,0 +1,178 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
+from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
+
+from nltk.corpus import stopwords
+from tensorflow import keras
+
+def create_text_processor(train_data, n_vocab):
+    ''' 
+        This function creates the tokenizer for processing text data
+        OUTPUT:
+            tokenizer - tokenizer instance that can be used to create text sequences
+    '''
+    
+    summary_train = list(train_data['summary'])
+    review_train = list(train_data['reviewText'])
+    
+    text_all = summary_train+ review_train
+    
+    tokenizer = keras.preprocessing.text.Tokenizer(num_words = n_vocab)
+    tokenizer.fit_on_texts(text_all)
+    
+    return tokenizer
+
+def use_text_process(data, tokenizer, max_sent_len):
+    '''
+        This function turns the summary and reviewText data into sequences that are padded
+        tokenizer must be provided
+        OUTPUTS:
+            train_summaries
+            train_reviews
+    '''
+    summary = list(data['summary'])
+    review = list(data['reviewText'])
+    
+    # turn into sequence
+    summary_numbers = tokenizer.texts_to_sequences(summary)
+    review_numbers = tokenizer.texts_to_sequences(review)
+    
+    # pad:
+    summary_numbers = keras.preprocessing.sequence.pad_sequences(summary_numbers, padding = 'post', maxlen = max_sent_len)
+    review_numbers = keras.preprocessing.sequence.pad_sequences(review_numbers, padding = 'post', maxlen = max_sent_len)
+    
+    return summary_numbers, review_numbers
+    
+
+def create_data_processors(train_data, n_bigrams_summary, n_unigram_review):
+    '''
+        This function create the preprocessors necessary for enconding the time, categorical, and text data
+        INTPUT:
+            train_data - all training data (in pd format)
+        OUTPUT:
+            scalar_fit, cat_encoder_fit, cv_summary_fit, cv_review_fit - preprocessing objects to be used
+            
+    '''
+    pass
+
+    scalar = MinMaxScaler(feature_range = (0,1), copy = False)
+    scalar_fit = scalar.fit(pd.DataFrame(train_data['unixReviewTime']))
+    
+    cat_encoder = OneHotEncoder()
+    cat_encoder_fit = cat_encoder.fit(pd.DataFrame(train_data['category']))
+    
+    custom_stop_words = set(stopwords.words('english'))
+    
+    summary_train = list(train_data['summary'])
+    cv_summary = CountVectorizer(ngram_range = (2,2), lowercase = True, stop_words=list(custom_stop_words), analyzer = 'word', binary = True, max_features = n_bigrams_summary)
+    cv_summary_fit = cv_summary.fit(summary_train)
+    
+    review_train = list(train_data['reviewText'])
+    cv_review = CountVectorizer(ngram_range = (1,1), lowercase = True, stop_words=list(custom_stop_words), analyzer = 'word', binary = True, max_features = n_unigram_review)
+    cv_review_fit = cv_review.fit(review_train)
+
+    return scalar_fit, cat_encoder_fit, cv_summary_fit, cv_review_fit
+        
+def clean_data(df):
+    '''
+        This function cleans up the dataset
+    '''
+    # drop unnecessary columns
+    df.drop('image', inplace = True, axis = 1)
+    df.drop('reviewTime', inplace = True, axis = 1)
+    df.drop('reviewHash', inplace = True, axis = 1)
+    df.drop('price', inplace = True, axis = 1)
+    
+    # clean up review text and summary text:
+    df['reviewText'] = df['reviewText'].replace(np.nan,'', regex = True)
+    df['summary'] = df['summary'].replace(np.nan,'', regex = True)
+
+    return df
+
+def embed_data(corp, cv, emb_sentence_length):
+    '''
+        This function prepares the text data for the embedding layer:
+        INPUTS:
+            corp: list of strings, each of which is a datapoint
+            cv - count vectorizer instance used
+            emb_sentence_length - number of words to be considered in each sentence
+        OUTPUTS:
+            corp_for_embedding
+    '''
+    analyzer = cv.build_analyzer()
+    corp_emb = []
+    
+    # embed the sentence based on the words in dictionary
+    for sent in corp:
+        # tokenize sentence
+        sent_tokenized = analyzer(sent)
+        sent_emb = []
+        for i, word in enumerate(sent_tokenized):
+            #onlt keep first few words in the sentence
+            if i > emb_sentence_length:
+                break
+            # check if word is in the vocabulary
+            if word in cv.vocabulary_:
+                sent_emb.append(cv.vocabulary_[word]+1) # add one to avoid 0, as the vocabulary includes index 0 as well
+                
+        # add sentences to embedded corpus
+        corp_emb.append(sent_emb)
+            
+    # padd corpus to make them all the same length
+    corp_pad = keras.preprocessing.sequence.pad_sequences(corp_emb, emb_sentence_length, padding = 'post')
+    
+    return corp_pad
+
+def embed_categories(categories_1hot):
+    '''
+        This function embeds the categories
+    '''
+    categories_embedded = np.where(categories_1hot == 1)[1]
+    categories_embedded = categories_embedded.reshape((-1,1))
+    
+    return categories_embedded
+
+def create_inputs(data, scalar_fit, cat_encoder_fit, cv_summary_fit, cv_review_fit, embed_cat, use_embed_summary, use_embed_review):
+    '''
+        This function creates the training data based on the preprocessing objects scalar_fit, cat_encoder_fit, cv_summary_fit, cv_review_fit
+        use_embed_summary, use_embed_review are used to indicate how text data should be processed
+    '''
+    # create time data
+    review_time_normalized = scalar_fit.transform(pd.DataFrame(data['unixReviewTime']))
+    
+    # create category data
+    categories_1hot = cat_encoder_fit.transform(pd.DataFrame(data['category'])).toarray()
+    if embed_cat:
+       categories_1hot = embed_categories(categories_1hot)
+    
+    # create summary data
+    summary = list(data['summary'])
+    if use_embed_summary:
+        emb_sentence_length = 15
+        summaries_encoded = embed_data(summary, cv_summary_fit, emb_sentence_length)
+    else:
+        summaries_encoded = cv_summary_fit.transform(summary)
+
+    # create review data
+    review = list(data['reviewText'])
+    if use_embed_review:
+        emb_sentence_length = 300
+        reviews_encoded = embed_data(review, cv_review_fit, emb_sentence_length)
+    else:
+        reviews_encoded = cv_review_fit.transform(review)
+        
+    return review_time_normalized, categories_1hot, summaries_encoded, reviews_encoded
+
+
+def cap_results(y):
+    '''
+        This function caps the results, namely, ones over 5 are set to 5, and ones below 1 are set to 1
+    '''
+    y[np.where(y>5)] = 5
+    y[np.where(y<1)] = 1
+
+    return y
\ No newline at end of file
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