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1. Customer-Segments-Arvato Customer-Segments-Arvato Public

   Analyze customers using unsupervised learning, PCA and K-Mean Clustering of Arvato dataset

   HTML 2

2. Disaster-Response-with-Figure-Eight Disaster-Response-with-Figure-Eight Public

   Apply Data Engineering to Build ETL & NLP Machine Learning Pipelines and Create an App for Disaster Relief using Flask

   Jupyter Notebook 1

3. Recommendation-with-IBM Recommendation-with-IBM Public

   Make a recommendation engine using ranked based, user-user based collaborative filtering, content based, and matrix factorization

   HTML 1

4. Sparkify-with-Apache-Spark-Mllib-Data-Science Sparkify-with-Apache-Spark-Mllib-Data-Science Public

   Manipulate large and realistic datasets with Spark to engineer relevant features for predicting churn. Use Spark MLlib to build machine learning models with large datasets.

   Jupyter Notebook 1

5. Cloud-Data-Warehouse-with-Redshift-AWS Cloud-Data-Warehouse-with-Redshift-AWS Public

   Cloud Data Warehouse of Sparkify Data using Redshift

   Python 1 1

6. Spark-ETL-DataLake Spark-ETL-DataLake Public

   EMR - Spark ETL of JSON Data Lake to Parquet DL for DWH

   Python 1