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 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.
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.
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.
In this deep learning project, you will learn to implement Unet++ models for medical image segmentation to detect and classify colorectal polyps.
In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.
Music Recommendation Project using Machine Learning - Use the KKBox dataset to predict the chances of a user listening to a song again after their very first noticeable listening event.
In this Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.
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.