Bitcoin Data Mining on AWS Free Tier

Bitcoin Mining on AWS - Learn how to use AWS Cloud for building a data pipeline and analysing bitcoin data.

Churn Prediction in Telecom using Machine Learning in R

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.

Design a Hadoop Architecture

Learn to design Hadoop Architecture and understand how to store data using data acquisition tools in Hadoop.

Web Server Log Processing using Hadoop

In this hadoop project, you will be using a sample application log file from an application server to a demonstrated scaled-down server log processing pipeline.

Online Hadoop Projects -Solving small file problem in Hadoop

In this hadoop project, we are going to be continuing the series on data engineering by discussing and implementing various ways to solve the hadoop small file problem.

Data Science Project-TalkingData AdTracking Fraud Detection

Machine Learning Project in R-Detect fraudulent click traffic for mobile app ads using R data science programming language.

Forecasting Business KPI's with Tensorflow and Python

In this machine learning project, you will use the video clip of an IPL match played between CSK and RCB to forecast key performance indicators like the number of appearances of a brand logo, the frames, and the shortest and longest area percentage in the video.

Automatic event-based ingestion GCP

Automatic event-based ingestion GCP

Slow Changing Dimensions for Warehouse

One of the broadest uses of Snowflake is building a data warehouse platform or enhancing the existing data lake. It offers all sorts of services to build an efficient Data warehouse with ETL capability and support for various external data partners. Slowly Changing dimensions are a common database modeling technique used to capture data in a table and show how it changes over time. The slowly changing dimension of the warehouse dimension is said to rarely change. However, when they change, there should be a systematic approach to capturing that change. Examples of SCDs are customer and products information. This project explains how to build a Slowly Changing Dimension (SCD) using Snowflake’s Stream functionality and how to automate the process using Snowflake’s Task functionality.

Customer Churn Prediction Analysis using Ensemble Techniques

In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.