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Clicksteam data records the flow or trail of a user when he/she visits a website. For example, if you have pages A-Z and want to see how many people land on Page G and then go to Page B - you can analyze this data and see the clickstream pattern of your visitors. This data is stored in semi structured web logs. Often you will hear the term web log analysis - this is the same as analyzing clickstream data. Segmenting, and analyzing this clickstream data will give you a more refined look at your customer's behavior patterns - from the time they land on your website till the time they either buy your product or leave without buying.
You have built a wonderful website and your transaction page has all the information that is required for someone to know before buying the product. Still you see that a huge number of your website visitors leave before buying a single product. This is because of a broken link or path somewhere which prevents users to quickly and easily buy your product. Hadoop helps you to extract, store and analyze the clickstream data or web log data and merge it with the traditional customer data - in order to get better insights into the behavior of the visitor and optimize the path to product buying. Hive is the easiest of the Hadoop tools to learn. If you are from a data warehousing background and know SQL well - it will be a breeze to work on Hive. Hive is a data warehouse infrastructure built on top of Hadoop and is quite versatile in its usage, as it supports different storage types such as plain text, RCFile, Amazon S3, HBase, ORC, etc. Hive has its own SQL like language called HiveQL with schemas - which transparently converts queries to MapReduce or Apache Spark jobs.
You will be working on solving these business problems for the end-user in this Hadoop Hive Project:
Optimizing the click through path of the users
Which is the most optimum path for a user to follow in order to buy the product?
After how many clicks does a user lose interest to buy a product?
Which products do users usually buy together?
Where can the website resources be allocated to provide the best user experience to a visitor to make him return again?
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.
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.
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.
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.
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 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.
Hive Project -Learn to write a Hive program to find the first unique URL, given 'n' number of URL's.
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.
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.
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.