This is the material for Jose Portilla's Spark and Python for Big Data and ML course.
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Updated
Aug 4, 2024 - Jupyter Notebook
This is the material for Jose Portilla's Spark and Python for Big Data and ML course.
This repo contains implementations of PySpark for real-world use cases for batch data processing, streaming data processing sourced from Kafka, sockets, etc., spark optimizations, business specific bigdata processing scenario solutions, and machine learning use cases.
Implementation of K-means,Bisecting K-means and Decision Tree in PySpark on the Iris Dataset.
Cardiovascular Disease Detection using PySpark
Big data management with PySpark
Using PySpark to train machine learning models.
Worked on diffrent Spark classification and regression algorithms
Analysis of information about startup companies done using machine learning and data analytics methods to predict the success of the startup companies.
PySpark is a Python API for support Python with Spark. Whether it is to perform computations on large datasets or to just analyze them
A course project with implementation of machine learning with spark structured streaming in python
Twitter sentiment analysis based on weather
Is it feasable to train a model on 100 million ratings using nothing more than a common laptop? Let's find out.
12 year nutrient intake analysis across financial classes with PySpark and KMeans clustering
Weather Analysis using PySpark
My Practice and project on PySpark
A PySpark MLlib classification model to classify songs based on a number of characteristics into a set of 23 electronic genres.
Loan Default Prediction using PySpark, with jobs scheduled by Apache Airflow and Integration with Spark using Apache Livy
PySpark functions and utilities with examples. Assists ETL process of data modeling
Scale your Python Code with PySpark in Apache Spark - PyData Charlotte January 2020 Meeting
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