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A machine learning model is built using PySpark's MLlib library to automatically flag suspicious job postings on Indeed.com. The dataset includes 18,000 job descriptions, out of which about 800 are fake.
An ETL pipeline for I94 immigration, global land temperatures and US demographics datasets is created to form an analytics database on immigration events. A data model is established with pandas and pyspark to find patterns of immigration to the United States.
The current assignment is to write the python scripts for Apache Spark. The tasks are divided into three parts as below: WordCount-To count the occurrences of words in a book on a per-book basis and compare the results with those of Assignment1. pyspark.ml. feature- To count the tf-idf values for the unigram and bigrams using the pyspark.ml.feat…