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, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.
Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.
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.
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 project, we are going to talk about H2O and functionality in terms of building Machine Learning models.
Use the Amazon Reviews/Ratings dataset of 2 Million records to build a recommender system using memory-based collaborative filtering in Python.
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 human activity recognition project, we use multiclass classification machine learning techniques to analyse fitness dataset from a smartphone tracker.