You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This project is focused on end to end application of Machine Learning methodologies to achieve best predicting accuracy. Our goal is to predict churn rate of customers for a telecom service provider based on service charges and usage data of customer.
A Machine Learning project to predict the success or failure of startups based on data by using ensemble modeling techniques, MLflow for tracking experiments, Docker for containerization.
Goal Using the data collected from existing customers, build a model that will help the marketing team identify potential customers who are relatively more likely to subscribe term deposit and thus increase their hit ratio
Programming assignments covering fundamentals of machine learning and deep learning. These were completed as part of the Plaksha Tech Leaders Fellowship program.
With the help of a brand new KATS package, we can detect outliers, change points, and build very strong Time Series Analysis models. By inspecting this repository you can get a solid vision of KATS on real Covid-19 data of Azerbaijan.