Principal Factor Analysis practice

In India, whenever we have festivals in our calendars, our support staff/ helpers expect festival rewards. We roughly calculate this reward in proportion to the monthly salary we pay to them. A 12-hour helper is likely to get more reward amount, while a 1-hour helper would get a smaller reward amount. If you’re heading a large organization, you may like to develop a model to suggest the reward amounts of people in support staff. To do, you decide to collate the data for last ten such festive instances. When you begin to do this, you realize that you remember the last ten reward amounts that were paid but not the respective salaries of the particular staff employed at that time. Use this data to predict the reward amount for the upcoming festival ! The given data set helps you to practice the concepts of Regression The data set consists of the following variables: 1) Instance 2) Years of service 3) Monthly Salary in Rupees 4) Reward Amount

An Entry level R program for understanding the concepts of Linear Regression. The dataset consists of height and weight for students. Develop a regression model to predict the weight of the student using the data.

The dataset contains 9568 data points collected from a Combined Cycle Power Plant over 6 years (2006-2011), when the power plant was set to work with full load. Features consist of hourly average ambient variables Temperature (T), Ambient Pressure (AP), Relative Humidity (RH) and Exhaust Vacuum (V) to predict the net hourly electrical energy output (EP) of the plant. A combined cycle power plant (CCPP) is composed of gas turbines (GT), steam turbines (ST) and heat recovery steam generators. In a CCPP, the electricity is generated by gas and steam turbines, which are combined in one cycle, and is transferred from one turbine to another. While the Vacuum is collected from and has effect on the Steam Turbine, the other three of the ambient variables effect the GT performance.