writing report(2 pages) about Data mining using Weka software

Introduction

This report discusses the programming process which I would developed and used to produce the required data suitable for part two and three. The main measurement that I used to generate data is one region, particularly in two month period of time. This period information is required to generate from particular years 2011, 2012 and 2013. This data contains two different types of information which are climatic conditions recorded and power consumption that are related to that period of time.

Climatic conditions

The program that I developed using C programming related to weather data was focusing on years. What is supposed to do is processing a bunch of dataset containing information that is climatic conditions recorded across various regions. Which means reducing it down to just the data values that are relevant or meaningful to the desired region (Auckland) to be able to get its details on January and February in particular years .The idea is collecting the 2011+2013 desired information and generating it in a separate excel file then so on for 2013.

Power consumption

It is the same idea for power consumption, what I accomplished was using two processes in a huge number of data file to generate a filtered file. Although that huge file contained only the required years, there were unwanted months details that needed to be excluded. The first process was using C codes programming to get the desired two months by printing out the first two months of each year. So, during printing process, it had to be stopped at the end of the second months of each year and jumping on the following year to complete the process. The second process was combining every two rows of the filtered file as each row taken every 5 minutes power consuming recorded but the requirement was ten minutes reading for each row.

After achieving all of that processes and generating the filtered files, we need to use these files information with Weka to undertake a data modelling task. Then using this modelling task in different visualization techniques to see how well the performance of the task predictive is. The following sections show how to use the generated data both the weather data and power consumption in data mining and data visualization.