Data Science Application

This category includes data science applications.

anomaly outlier detection eggs in a tray

How to apply Unsupervised Anomaly Detection on bank transactions
 A use case of Unsupervised Learning with Python, step-by-step

In this tutorial, we’ll show how to detect outliers or anomalies on unlabeled bank transactions with Python.

You’ll learn:
– How to identify rare events in an unlabeled dataset using machine learning algorithms: isolation forest (clustering).
– How to visualize the anomaly detection results.
– How to fight crime with anti-money laundering (AML) or fraud analytics in banks
Use case and tip from people with industry experience

sentiment analysis leaves

How to do Sentiment Analysis with Deep Learning (LSTM Keras)
 Automatically Classify Reviews as Positive or Negative in Python

In this tutorial, we build a deep learning neural network model to classify the sentiment of Yelp reviews.

Following the step-by-step procedures in Python, you’ll see a real life example and learn:

– How to prepare review text data for sentiment analysis, including NLP techniques.
– How to tune the hyperparameters for the machine learning models.
– How to predict sentiment by building an LSTM model in Tensorflow Keras.
– How to evaluate model performance.
– How sample sizes impact the results compared to a pre-trained tool.
And more.

covid-19 death rate breaking life

What is the Coronavirus Death Rate with Hyperparameter Tuning
 Examine the death rate and time to death/recovery distribution with Python

We examine the death rate and time to death/recovery distribution of coronavirus with Python.
You’ll see the step-by-step procedures of how to find the parameters of a model that is best fitting the COVID-19 data.
If you want:
– more insights about coronavirus
– or to see an example of hyperparameter tuning/optimization in Python
take a look!

youtube views going up like rocket launching with machine learning

How to Get MORE YouTube Views with Machine Learning techniques
 Step-by-Step process on Popular Fitness Channel in Python

In this post, we apply machine learning algorithms on YouTube data, to make recommendation on content creation strategies. We will include the end-to-end process of:
– Scraping the YouTube data
– Using NLP on the video titles
– Feature engineering
– Building predictive decision trees
– And more
All in Python.

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