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Analysis of historical customer data can highlight if a certain combination of products purchased makes an additional purchase more likely. This is called market basket analysis (also called as MBA). It is a widely used technique to identify the best possible mix of frequently bought products or services. This is also called product association analysis. The set of items a customer buys is referred to as an itemset, and market basket analysis seeks to find relationships between purchases. Market Basket Analysis creates If-Then scenario rules, for example, if item A is purchased then item B is likely to be purchased. The rules are probabilistic in nature or, in other words, they are derived from the frequencies of co-occurrence in the observations. Market Basket analysis is particularly useful for physical retail stores as it can help in planning floor space and product placement amongst many other benefits.
Deep Learning Project to implement an Abstractive Text Summarizer using Google's Transformers-BART Model to generate news article headlines.
In this Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.
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.
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 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.
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 deep learning project, you will learn how to build your custom OCR (optical character recognition) from scratch by using Google Tesseract and YOLO to read the text from any images.
Learn to design Hadoop Architecture and understand how to store data using data acquisition tools in Hadoop.
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.
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.