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Aug 27 2016 09:48 PM
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.
GCP Data Ingestion with SQL using Google Cloud Dataflow
In this GCP Project, you will learn to build a data processing pipeline With Apache Beam, Dataflow & BigQuery on GCP using Yelp Dataset.
Data Science Project - Instacart Market Basket Analysis
Data Science Project - Build a recommendation engine which will predict the products to be purchased by an Instacart consumer again.
Inventory Demand Forecasting using Machine Learning in R
In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.
Time Series Analysis Project in R on Stock Market forecasting
In this time series project, you will build a model to predict the stock prices and identify the best time series forecasting model that gives reliable and authentic results for decision making.
Data Science Project in Python on BigMart Sales Prediction
The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.
Loan Eligibility Prediction in Python using H2O.ai
In this loan prediction project you will build predictive models in Python using H2O.ai to predict if an applicant is able to repay the loan or not.
Medical Image Segmentation Deep Learning Project
In this deep learning project, you will learn to implement Unet++ models for medical image segmentation to detect and classify colorectal polyps.
German Credit Dataset Analysis to Classify Loan Applications
In this data science project, you will work with German credit dataset using classification techniques like Decision Tree, Neural Networks etc to classify loan applications using R.
Machine Learning project for Retail Price Optimization
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.
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