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1. Customer-Segments-ArvatoCustomer-Segments-ArvatoPublic

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

   HTML 2

2. Disaster-Response-with-Figure-EightDisaster-Response-with-Figure-EightPublic

   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-IBMRecommendation-with-IBMPublic

   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-ScienceSparkify-with-Apache-Spark-Mllib-Data-SciencePublic

   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-AWSCloud-Data-Warehouse-with-Redshift-AWSPublic

   Cloud Data Warehouse of Sparkify Data using Redshift

   Python 1 1

6. Spark-ETL-DataLakeSpark-ETL-DataLakePublic

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

   Python 1