This is a practical, step-by-step example of logistic regression in Python.
Learn how to implement the model with a hands-on and real-world example.
Data Science Application
This category includes data science applications.
In this tutorial, we’ll show how to detect outliers or anomalies on unlabeled bank transactions with Python.
– 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
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.
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!
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.