The original data set included noisy, missing and inconsistent data. Data preprocessing improved the quality of the data and facilitated e±cient data mining tasks. Before the experiment, we prepared data suitable to next operation as following steps: ² Delete or replace missing values; ² Delete redundant properties (columns); ² Data Transformation; ² Data Discretization; ² Export data to a required .ar® or .csv format ¯le [11]. The original and modi¯ed formats of data set are shown in Figure 1 and Figure 2. Data visualization is also a very useful technique because it helps to deter- mine the di±culty of the learning problem. We visualized with Weka single attributes (1-d) and pairs of attributes (2-d). The ¯gure 3 shows the variation of the temperature in time.

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