Our style guide for writing readable and maintainable PySpark code.

All updated cheat sheets regarding data science, data analysis provided by Datacamp are here. These cheat sheets cover quick reads on Machine Learning, Deep Learning, Python, R, SQL and more. Perfect cheat sheets when you want to revise some topics in less time.

Project based on application of azure databricks

This notebook contains the usage of Pyspark to build machine learning classifiers (note that almost ml_algorithm supported by Pyspark are used in this notebook)

This notebook performs EDA over a movie ratings dataset via pyspark sql.

This repository contains the Notes for Pyspark

Batch Processing using Apache Spark and Python for data exploration

This script builds a linear regression model using PySpark to predict student admissions at Unicorn University.

Inventory value is also important for determining a company's liquidity, or its ability to meet its short-term financial obligations. A high inventory value can indicate that a company has too much money tied up in inventory, which could make it difficult for the company to pay its bills.

Clustering vs Classification

Module 22 challenge: Using Google Colab to work on Big Data queries with PySpark SQL, parquet, and cache partitions

List of useful commands for Pyspark

Worked on Pyspark file streaming

Utilizing Apache Spark & PySpark to analyze a movie dataset. Tasks include data exploration, identifying top-rated movies, training a linear regression model, and experimenting with Airflow.

PySpark House Price Prediction features a PySpark-based Linear Regression model for predicting median house prices. It showcases data preprocessing, model training, and evaluation, yielding an RMSE of around 0.11. The code offers insights into building robust predictive models using PySpark.

twitter real-time sentiment analysis

Creates a ML Pipeline leveraging PySpark SQL and PySpark MLib to predict sound level