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In this Big Data project, a senior Big Data Architect will demonstrate how to implement a Big Data pipeline on AWS at scale. You will be using the Covid-19 dataset. This will be streamed real-time from an external API using NiFi. The complex json data will be parsed into csv format using NiFi and the result will be stored in HDFS.
Then this data will be sent to Kafka for data processing using PySpark. The processed data will then be consumed from Spark and stored in HDFS. Then a Hive external table is created on top of HDFS. Finally the cleaned, transformed data is stored in the data lake and deployed. Visualisation is then done using Tableau and AWS QuickSight.
In this Databricks Azure tutorial project, you will use Spark Sql to analyse the movielens dataset to provide movie recommendations. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.
In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.
Hive Project -Learn to write a Hive program to find the first unique URL, given 'n' number of URL's.
In this time series project, you will forecast Walmart sales over time using the powerful, fast, and flexible time series forecasting library Greykite that helps automate time series problems.
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.
Estimating churners before they discontinue using a product or service is extremely important. In this ML project, you will develop a churn prediction model in telecom to predict customers who are most likely subject to churn.
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.
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.
In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.