Predicting a qualitative response for observation can be referred to as classifying that observation since it involves assigning the observation to a category or class. Classification forms the basis for Logistic Regression. Logistic Regression is a supervised algorithm used to predict a dependent variable that is categorical or discrete. Logistic regression models the data using the sigmoid function. Churned Customers are those who have decided to end their relationship with their existing company. In our case study, we will be working on a churn dataset. XYZ is a service-providing company that provides customers with a one-year subscription plan for their product. The company wants to know if the customers will renew the subscription for the coming year or not. # Aim Build a logistics regression learning model on the given dataset to determine whether the customer will churn or not. Tech stack * Language - R * Libraries - * Tidy Models, * Random Forests * Xgboost

Predicting a qualitative response for observation can be referred to as classifying that observation since it involves assigning the observation to a category or class. Classification forms the basis for Logistic Regression. Logistic Regression is a supervised algorithm used to predict a dependent variable that is categorical or discrete. Logistic regression models the data using the sigmoid function. Churned Customers are those who have decided to end their relationship with their existing company. In our case study, we will be working on a churn dataset. XYZ is a service-providing company that provides customers with a one-year subscription plan for their product. The company wants to know if the customers will renew the subscription for the coming year or not. Build a logistics regression learning model on the given dataset to determine whether the customer will churn or not.

Build a ML model to perform focused digital marketing by predicting the potential customers who will convert from liability customers to asset customers

The climate is changing around the world. The impacts of climate change are felt in many different areas, but they are particularly noticeable in their effects on birds. Many bird species are moving north, if they can, to stay in climatic conditions that are suitable for them. Our analysis will use data from the UK Met Office together with records from the Global Biodiversity Information Facility to build our very own species distribution model using machine learning. This model will be able to predict where our bird species of interest is likely to occur in the future - information that is invaluable to conservation organization working on the ground to preserve these species and save them from extinction!

This map is created as part of Peer-graded Assignment: R Markdown and Leaflet for JHU Data Science Specialization Course

This project involves exploring the U.S. National Oceanic and Atmospheric Administration's (NOAA) storm database. This database tracks characteristics of major storms and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage.