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All State, a personal insurance company in the United States, is interested in leveraging data science to predict the severity and the cost of insurance claims post an unforeseen event.
This ensemble machine learning project will help you understand the best practices followed in approaching a data analytics problem through python language focusing on using data science packages. We will predict how severe insurance claims will be for All State. We accomplish this using ensemble machine learning algorithms.
In this deep learning project, you will build your own face recognition system in Python using OpenCV and FaceNet by extracting features from an image of a person's face.
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.
The goal of this apache kafka project is to process log entries from applications in real-time using Kafka for the streaming architecture in a microservice sense.
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.
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.
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.
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.