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In this Hackerday, we will show by demonstrating how to build an ETL pipeline on streaming datasets using Kafka. We will be using the trips and fares dataset from the New York Taxi and Limousine Commission to demonstrate how to get data in real-time, join it to other streaming datasets, and store the data in a database.
Will be answering questions like reporting the income of drivers every hour, find the drivers around a certain location at any point in time amongst other things.
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.
Hive Project -Learn to write a Hive program to find the first unique URL, given 'n' number of URL's.
In this Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.
Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.
Deep Learning Project to implement an Abstractive Text Summarizer using Google's Transformers-BART Model to generate news article headlines.
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.
Use the Zillow dataset to follow a test-driven approach and build a regression machine learning model to predict the price of the house based on other variables.
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.
In this PySpark project, you will simulate a complex real-world data pipeline based on messaging. This project is deployed using the following tech stack - NiFi, PySpark, Hive, HDFS, Kafka, Airflow, Tableau and AWS QuickSight.