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De-duplicate the Duplicate Records from Scratch

Photo credit: Trivago

Identify similar records, Sparse matrix multiplication

Online world is full of duplicate listings. In particular, if you are an online travel agency, and you accept different suppliers that provide you information for the same property.

Sometimes the duplicate records are obvious that makes you think: How is it possible?

Photo credit: agoda

Another time, the two records look like they are duplicates, but we were not sure.

Photo credit: expedia

Or, if you work for a company that has significant amount of data about companies or customers, but because the data comes from different source systems, in which are often written in different ways. Then you will have to deal with duplicate records.

Photo credit:

The Data

I think the best data set is to use my own. Using the Seattle Hotel data set that I created a while ago. I removed hotel description feature, kept hotel name and address features, and added duplicate records purposely, and the data set can be found here.

An example on how two hotels are duplicates:

Table 1

The most common way of duplication is how the street address is input. Some are using the abbreviations and others are not. For the human reader it is obvious that the above two listings are the same thing. And we will write a program to determine and remove the duplicate records and keep one only.

TF-IDF + N-gram

  • We will use name and address for input features.
  • We all familiar with tfidf and n-gram methods.
  • The result we get is a sparse matrix that each row is a document(name_address), each column is a n-gram. The tfidf score is computed for each n-gram in each document.


I discovered an excellent library that developed by ING Wholesale Banking, sparse_dot_topn which stores only the top N highest matches for each item, and we can choose to show the top similarities above a threshold.

It claims that it provides faster way to perform a sparse matrix multiplication followed by top-n multiplication result selection.

The function takes the following things as input:

  • A and B: two CSR matrix
  • ntop: n top results
  • lower_bound: a threshold that the element of A*B must greater than output

The output is a resulting matrix.

After running the function. The matrix only stores the top 5 most similar hotels.

The following code unpacks the resulting sparse matrix, the result is a table where each hotel will match to every hotel in the data(include itself), and cosine similarity score is computed for each pair.

We are only interested in the top matches except itself. So we are going to visual examine the resulting table sort by similarity scores, in which we determine a threshold a pair is the same property.

matches_df[matches_df['similarity'] < 0.99999].sort_values(by=['similarity'], ascending=False).head(30)
Table 2

I decided my safe bet is to remove any pairs where the similarity score is higher than or equal to 0.50.

matches_df[matches_df['similarity'] < 0.50].right_side.nunique()

After that, we now have 152 properties left. If you remember, in our original data set, we did have 152 properties.

Jupyter notebook and the dataset can be found on Github. Have a productive week!

De-duplicate the Duplicate Records from Scratch was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.

When Topic Modeling is Part of the Text Pre-processing

Photo credit: Unsplash

How to effectively and creatively pre-process text data

A few months ago, we built a content based recommender system using a relative clean text data set. Because I collected the hotel descriptions my self, I made sure that the descriptions were useful for the goals we were going to accomplish. However, the real-world text data is never clean and there are different pre-processing ways and steps for different goals.

Topic modeling in NLP is rarely my final goal in an analysis, I use it often to either explore data or as a tool to make my final model more accurate. Let me show you what I meant.

The Data

We are still using the Seattle Hotel description data set I collected earlier, and I made it a bit more messier this time. We are going to skip all the EDA processes and I want to make recommendations as quickly as possible.

If you have read my previous post, I am sure you understand the following code script. Yes, we are looking for top 5 most similar hotels with “Hilton Garden Inn Seattle Downtown” (except itself), according to hotel description texts.

Make Recommendations

Figure 1

Our model returns the above 5 hotels and thinks they are top 5 most similar hotels to “Hilton Garden Inn Seattle Downtown”. I am sure you don’t agree, neither do I. Let’s say why the model thinks they are similar by looking at these descriptions.

df.loc['Hilton Garden Inn Seattle Downtown'].desc
df.loc["Mildred's Bed and Breakfast"].desc
df.loc["Seattle Airport Marriott"].desc

Found anything interesting? Yes, there are indeed somethings in common in these three hotel descriptions, they all have the same check in and check out time, and they all have the similar smoking policies. But are they important? Can we declare two hotels are similar just because they are all “non-smoking”? Of course not, these are not important characteristics and we shouldn’t measure similarity in vector space of these texts.

We need to find a way to safely remove these texts programmatically, while not removing any other useful characteristics.

Topic modeling comes to our rescue. But before that, we need to wrangle the data to make it in the right shape.

  • Split each description into sentences. Hilton Garden Seattle Downtown’s entire description will be split into 7 sentences.

Table 1

Topic Modeling

  • We are going to build topic model for all the sentences together. I decided to have 40 topics after several experiments.

Figure 2

Not too bad, there were not too much overlapping.

  • To understand better, you may want to investigate top 20 words in each topic.

We shall have 40 topics, and each topic shows 20 keywords. Its very hard to print out the entire table, I will only show a small part of it.

Table 2

By staring at the table, we can guess that at least topic 12 should be one of the topics we would like to dismiss, because it contains several words that meaningless for our purpose.

In the following code scripts, we:

  • Create document-topic matrix.
  • Create a data frame where each document is a row, and each column is a topic.
  • The weight of each topic is assigned to each document.
  • The last column is the dominant topic for that document, in which it carries the most weight.
  • When we merge this data frame to the previous sentence data frame. We are able to find the the weight of each topic in every sentence, and the dominant topic for each sentence.

  • Now we can visually examine dominant topics assignment of each sentence for “Hilton Garden Inn Seattle Downtown”.
df_sent_topic.loc[df_sent_topic['name'] == 'Hilton Garden Inn Seattle Downtown'][['sentence', 'dominant_topic']]
Table 3
  • By staring at the above table, my assumption is that if a sentence’s dominant topic is topic 4 or topic 12, that sentence is likely to be useless.
  • Let’s see a few more example sentences that have topic 4 or topic 12 as their dominant topic.
df_sent_topic.loc[df_sent_topic['dominant_topic'] == 4][['sentence', 'dominant_topic']].sample(20)
Table 4
df_sent_topic.loc[df_sent_topic['dominant_topic'] == 12][['sentence', 'dominant_topic']].sample(10)
Table 5
  • After reviewing the above two tables, I decided to remove all the sentences that have topic 4 or topic 12 as their dominant topic.
print('There are', len(df_sent_topic.loc[df_sent_topic['dominant_topic'] == 4]), 'sentences that belong to topic 4 and we will remove')
print('There are', len(df_sent_topic.loc[df_sent_topic['dominant_topic'] == 12]), 'sentences that belong to topic 12 and we will remove')
df_sent_topic_clean = df_sent_topic.drop(df_sent_topic[(df_sent_topic.dominant_topic == 4) | (df_sent_topic.dominant_topic == 12)].index)
  • Next, we will join the clean sentence together in to a descriptions. That is, making it back to one description per hotel.
df_description = df_sent_topic_clean[['sentence','name']]
df_description = df_description.groupby('name')['sentence'].agg(lambda col: ' '.join(col)).reset_index()
  • Let’s see what left for our “Hilton Garden Inn Seattle Downtown”

There is only one sentence left and it is about the location of the hotel and this is what I had expected.

Make Recommendations

Using the same cosine similarity measurement, we are going to find the top 5 most similar hotels with “Hilton Garden Inn Seattle Downtown” (except itself), according to the cleaned hotel description texts.

Figure 3

Nice! Our method worked!

Jupyter notebook can be found on Github. Have a great weekend!

Classify Toxic Online Comments with LSTM and GloVe

Photo credit: Pixabay

Deep learning, text classification, NLP

This article shows how to use a simple LSTM and one of the pre-trained GloVe files to create a strong baseline for the toxic comments classification problem.

The article consist of 4 main sections:

  • Preparing the data
  • Implementing a simple LSTM (RNN) model
  • Training the model
  • Evaluating the model

The Data

In the following steps, we will set the key model parameters and split the data.

  • MAX_NB_WORDS” sets the maximum number of words to consider as features for tokenizer.
  • MAX_SEQUENCE_LENGTH” cuts off texts after this number of words (among the MAX_NB_WORDS most common words).
  • VALIDATION_SPLIT” sets a portion of data for validation and not used in training.
  • EMBEDDING_DIM” defines the size of the “vector space”.
  • GLOVE_DIR” defines the GloVe file directory.
  • Split the data into the texts and the labels.

Text Pre-processing

In the following step, we remove stopwords, punctuation and make everything lowercase.

Have a look a sample data.

print('Sample data:', texts[1], y[1])
  • We create a tokenizer, configured to only take into account the MAX_NB_WORDS most common words.
  • We build the word index.
  • We can recover the word index that was computed.
tokenizer = Tokenizer(num_words=MAX_NB_WORDS)
sequences = tokenizer.texts_to_sequences(texts)
word_index = tokenizer.word_index
print('Vocabulary size:', len(word_index))
  • Turns the lists of integers into a 2D integer tensor of shape (samples, maxlen)
  • Pad after each sequence.
data = pad_sequences(sequences, padding = 'post', maxlen = MAX_SEQUENCE_LENGTH)
print('Shape of data tensor:', data.shape)
print('Shape of label tensor:', y.shape)
  • Shuffle the data.
indices = np.arange(data.shape[0])
data = data[indices]
labels = y[indices]

Create the train-validation split.

num_validation_samples = int(VALIDATION_SPLIT*data.shape[0])
x_train = data[: -num_validation_samples]
y_train = labels[: -num_validation_samples]
x_val = data[-num_validation_samples: ]
y_val = labels[-num_validation_samples: ]
print('Number of entries in each category:')
print('training: ', y_train.sum(axis=0))
print('validation: ', y_val.sum(axis=0))

This is what the data looks like:

print('Tokenized sentences: n', data[10])
print('One hot label: n', labels[10])
Figure 1

Create the model

  • We will use pre-trained GloVe vectors from Stanford to create an index of words mapped to known embeddings, by parsing the data dump of pre-trained embeddings.
  • Then load word embeddings into an embeddings_index

  • Create the embedding layers.
  • Specifies the maximum input length to the Embedding layer.
  • Make use of the output from the previous embedding layer which outputs a 3-D tensor into the LSTM layer.
  • Use a Global Max Pooling layer to to reshape the 3D tensor into a 2D one.
  • We set the dropout layer to drop out 10% of the nodes.
  • We define the Dense layer to produce a output dimension of 50.
  • We feed the output into a Dropout layer again.
  • Finally, we feed the output into a “Sigmoid” layer.

Its time to Compile the model into a static graph for training.

  • Define the inputs, outputs and configure the learning process.
  • Set the model to optimize our loss function using “Adam” optimizer, define the loss function to be “binary_crossentropy” .
model = Model(sequence_input, preds)
model.compile(loss = 'binary_crossentropy',
metrics = ['accuracy'])


  • Feed in a list of 32 padded, indexed sentence for each batch. The validation set will be used to assess whether the model has overfitted.
  • The model will run for 2 epochs, because even 2 epochs is enough to overfit.
print('Training progress:')
history =, y_train, epochs = 2, batch_size=32, validation_data=(x_val, y_val))

Evaluate the model

loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(loss)+1)
plt.plot(epochs, loss, label='Training loss')
plt.plot(epochs, val_loss, label='Validation loss')
plt.title('Training and validation loss')
Figure 2
accuracy = history.history['accuracy']
val_accuracy = history.history['val_accuracy']
plt.plot(epochs, accuracy, label='Training accuracy')
plt.plot(epochs, val_accuracy, label='Validation accuracy')
plt.title('Training and validation accuracy')
Figure 3

Jupyter notebook can be found on Github. Happy Monday!

Classify Toxic Online Comments with LSTM and GloVe was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.

Visa, Inc. Data Science Interviews

Visa Data Science Interviews

In 2018, Visa had $20.61 billion in revenue.

In 2015, the Nilson Report, a publication that tracks the credit card industry, found that Visa’s global network (known as VisaNet) processed 100 billion transactions during 2014 with a total volume of US$6.8 trillion. VisaNet data centers can handle up to 30,000 simultaneous transactions and up to 100 billion computations every second. Visa is a very household name all over the world. If you ever owned a credit card, you will surely know what Visa is. With a 100 billion transactions, the scale of data in the company is beyond compare. It could be a highlight of a Data professionals’ career.

Photo by Sharon McCutcheon on Unsplash

Interview Process

A senior data scientist from the team reaches out for the first telephonic interview after the resume is selected. The interview involves resume based questions, SQL, and or a business case study. After the first round, there is another telephonic technical interview. Eventually, there are five on-site interviews. On-site interviews are with top level personnel, directors and VPs. Each of those interviews is 45 minutes long.

Important Reading

Source: Data Science at Visa

Data Science Related Interview Questions

  • Invert an integer without the use of strings.
  • Write a sorting algorithm.
  • How do you estimate a customer’s location based on Visa transaction data?
  • Write a code for a Fibonacci sequence.
  • What functions can I perform using a spreadsheet?
  • Who would be your first line of contact to report a missing data you’re keeping record of?
  • Give the top three employee salaries in each department in a company.
  • What is Node.js?
  • What is MVC?
  • What is synchronous vs asynchronous Javascript?

Reflecting on the Interviews

The data science interview at Visa, Inc. is a rigorous process which involves many different interviews. The team is top notch and they are looking for similar candidates to hire. Most interviews look for fundamentals in SQL, coding, probability and statistics as well as ML. A decent amount of hard work can surely get you a job with the world’s largest credit transaction processing company!

Subscribe to our Acing AI newsletter, I promise not to spam and its FREE!


Thanks for reading! 😊 If you enjoyed it, test how many times can you hit 👏 in 5 seconds. It’s great cardio for your fingers AND will help other people see the story.

The sole motivation of this blog article is to learn about Visa Inc. and its technologies and help people to get into it. All data is sourced from online public sources. I aim to make this a living document, so any updates and suggested changes can always be included. Please provide relevant feedback.

Visa, Inc. Data Science Interviews was originally published in Acing AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

Building A Collaborative Filtering Recommender System with TensorFlow

Source: Pixabay

Collaborative Filtering is a technique widely used by recommender systems when you have a decent size of user — item data. It makes recommendations based on the content preferences of similar users.

Therefore, collaborative filtering is not a suitable model to deal with cold start problem, in which it cannot draw any inference for users or items about which it has not yet gathered sufficient information.

But once you have relative large user — item interaction data, then collaborative filtering is the most widely used recommendation approach. And we are going to learn how to build a collaborative filtering recommender system using TensorFlow.

The Data

We are again using booking crossing dataset that can be found here. The data pre-processing steps does the following:

  • Merge user, rating and book data.
  • Remove unused columns.
  • Filtering books that have had at least 25 ratings.
  • Filtering users that have given at least 20 ratings. Remember, collaborative filtering algorithms often require users’ active participation.

So, our final dataset contains 3,192 users for 5,850 books. And each user has given at least 20 ratings and each book has received at least 25 ratings. If you do not have a GPU, this would be a good size.

The collaborative filtering approach focuses on finding users who have given similar ratings to the same books, thus creating a link between users, to whom will be suggested books that were reviewed in a positive way. In this way, we look for associations between users, not between books. Therefore, collaborative filtering relies only on observed user behavior to make recommendations — no profile data or content data is necessary.

Our technique will be based on the following observations:

  • Users who rate books in a similar manner share one or more hidden preferences.
  • Users with shared preferences are likely to give ratings in the same way to the same books.

The Process in TensorFlow

First, we will normalize the rating feature.

scaler = MinMaxScaler()
combined['Book-Rating'] = combined['Book-Rating'].values.astype(float)
rating_scaled = pd.DataFrame(scaler.fit_transform(combined['Book-Rating'].values.reshape(-1,1)))
combined['Book-Rating'] = rating_scaled

Then, build user, book matrix with three features:

combined = combined.drop_duplicates(['User-ID', 'Book-Title'])
user_book_matrix = combined.pivot(index='User-ID', columns='Book-Title', values='Book-Rating')
user_book_matrix.fillna(0, inplace=True)
users = user_book_matrix.index.tolist()
books = user_book_matrix.columns.tolist()
user_book_matrix = user_book_matrix.as_matrix()

tf.placeholder only available in v1, so I have to work around like so:

import tensorflow.compat.v1 as tf

In the following code scrips

  • We set up some network parameters, such as the dimension of each hidden layer.
  • We will initialize the TensorFlow placeholder.
  • Weights and biases are randomly initialized.
  • The following code are taken from the book: Python Machine Learning Cook Book — Second Edition

Now, we can build the encoder and decoder model.

Now, we construct the model and the predictions

encoder_op = encoder(X)
decoder_op = decoder(encoder_op)
y_pred = decoder_op
y_true = X

In the following code, we define loss function and optimizer, and minimize the squared error, and define the evaluation metrics.

loss = tf.losses.mean_squared_error(y_true, y_pred)
optimizer = tf.train.RMSPropOptimizer(0.03).minimize(loss)
eval_x = tf.placeholder(tf.int32, )
eval_y = tf.placeholder(tf.int32, )
pre, pre_op = tf.metrics.precision(labels=eval_x, predictions=eval_y)

Because TensorFlow uses computational graphs for its operations, placeholders and variables must be initialized before they have values. So in the following code, we initialize the variables, then create an empty data frame to store the result table, which will be top 10 recommendations for every user.

init = tf.global_variables_initializer()
local_init = tf.local_variables_initializer()
pred_data = pd.DataFrame()

We can finally start training our model.

  • We split training data into batches, and we feed the network with them.
  • We train our model with vectors of user ratings, each vector represents a user and each column a book, and entries are ratings that the user gave to books.
  • After a few trials, I discovered that training model for 100 epochs with a batch size of 35 would be consuming enough memories. This means that the entire training set will feed our neural network 100 times, every time using 35 users.
  • At the end, we must make sure to remove user’s ratings in the training set. That is, we must not recommend books to a user in which he (or she) has already rated.

Finally, let’s see how our model works. I randomly selected a user, to see what books we should recommended to him (or her).

top_ten_ranked.loc[top_ten_ranked['User-ID'] == 278582]
Table 2

The above are the top 10 results for this user, sorted by the normalized predicted ratings.

Let’s see what books he (or she) has rated, sorted by ratings.

book_rating.loc[book_rating['User-ID'] == 278582].sort_values(by=['Book-Rating'], ascending=False)
Table 2

The types of the books this user liked are: historical mystery novel, thriller and suspense novel, science and fiction novel, fantasy novel and so on.

The top 10 results for this user are: murder fantasy novel, mystery thriller novel and so on.

The results were not disappointing.

The Jupyter notebook can be found on Github. Happy Friday!


Python Machine Learning Cook Book — Second Edition

Building A Collaborative Filtering Recommender System with TensorFlow was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.

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