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.

Scroll to Top

Learn Python for Data Analysis

with a practical online course

lectures + projects

based on real-world datasets

We use cookies to ensure you get the best experience on our website.  Learn more.