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One of the broadest use of Hadoop today is building data warehousing platform off a data lake. And in building a data warehouse, the traditions left us by Kimball and Inmon is still very much in play.
Why not every one of the legacy rules should be implemented as as-is in the big data platform, the issue of slow-changing dimensions is still a front-burner.
The slow changing dimension of warehouse dimension that is said to rarely change. However, when they change, there should be a systematic approach to capturing that change. Examples of SCDs are customer and products information.
In this hive project, we will look at the various types of SCDs and learn to implements SCDs in Hive and Spark.
Estimating churners before they discontinue using a product or service is extremely important. In this ML project, you will develop a churn prediction model in telecom to predict customers who are most likely subject to churn.
In this Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.
Use the Zillow dataset to follow a test-driven approach and build a regression machine learning model to predict the price of the house based on other variables.
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.
Hive Project -Learn to write a Hive program to find the first unique URL, given 'n' number of URL's.
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.
In this spark project, you will use the real-world production logs from NASA Kennedy Space Center WWW server in Florida to perform scalable log analytics with Apache Spark, Python, and Kafka.
In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.