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.
Data Science Project in Python- Build a machine learning algorithm that automatically suggests the right product prices.
In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.
This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.
In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.
In this deep learning project, we will predict customer churn using Artificial Neural Networks and learn how to model an ANN in R with the keras deep learning package.
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.
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.
Machine Learning Project in R-Detect fraudulent click traffic for mobile app ads using R data science programming language.