## 3 Materials and Methods
### Data Analysis
#### 3.1.2 Exploratory Data Analysis  
Exploratory data analysis (EDA) includes the presentation, summarization, and graphical presentation of these analyses in an understandable and easy way in order to obtain relevant information from existing data by utilizing probability and statistical methods. These studies provide the foundations of feature engineering. That is, it is the work of creating, transforming, and inferring features from the dataset so that the model can work in the most efficient way possible. First, a clear exploratory data analysis template is created that extracts and summarizes the most important features of the dataset and focuses on time series. Some common Python libraries 3.8.20, such as Pandas 1.4.4, Matplotlib 3.6.2, Numpy 1.19.2, Seaborn 0.12.2 and Statsmodels 0.13.5, are used for this. The first thing to do when working with time series is to roughly observe and check the type of data, its values, and whether there are any incorrect or missing values. One of the important things we do is plot the data. Thus, many features such as graphs, patterns, unusual observations, changes over time, and relationships between variables can be observed here. The results from these observations should be incorporated into the forecast ing model as much as possible. In addition, some mathematical tools, such as descriptive statistics and time series decomposition, also provide us with great benefits [48; 49]. In the next section, studies and conclusions regarding some of the steps implemented for EDA will be made. Descriptive Statistics, Data Organization, and Time Plot  
The first and last 5 rows of our raw data are shown in Table 1a. Our data consists of two columns: datetime and consumption amount. As can be seen from Table 1a, the values are sorted by years and hours but not by months and days. The data is not ordered from past to future. Moreover, our data consists of 145,366 rows and 2 columns excluding the header row. As a result of the examinations made according to time, it can be observed that 30 lines are missing and 4 lines are repeated. Missing values were added by calculating the mean value. The more appropriate of the redundant lines was selected and the other line was deleted. The data was then sorted from the past to the future. The final version of our data is shown in Table 1b. Figure 1a shows the time-dependent change in consumption according to raw data, and Figure 1b shows the time-dependent change in consumption according to edited data. Here it can be seen that unedited raw data will mislead us. Because incomplete and unordered data will shift both the peak time and the holiday hours. In Figure 1b, there is no major increase/decrease trend over the years and the average consumption remains more stable compared with Figure 1a. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **(a)** & & & **(b)** & \\ \hline
**Line No** & **Date Time** & **PJME\_MW** & **Line No** & **Date Time** & **PJME\_MW** \\ \hline
1 & 2002.12.31 01:00 & 26,498.0 & 1 & 1.01.2002 01:00 & 30,393.0 \\
2 & 2002.12.31 02:00 & 25,147.0 & 2 & 1.01.2002 02:00 & 29,265.0 \\
3 & 2002.12.31 03:00 & 24,574.0 & 3 & 1.01.2002 03:00 & 28,357.0 \\
4 & 2002.12.31 04:00 & 24,393.0 & 4 & 1.01.2002 04:00 & 27,899.0 \\
5 & 2002.12.31 05:00 & 24,860.0 & 5 & 1.01.2002 05:00 & 28,057.0 \\
145362 & 2018.01.1 20:00 & 44,284.0 & 145388 & 2.08.2018 20:00 & 44,057.0 \\
145363 & 2018.01.1 21:00 & 43,751.0 & 145389 & 2.08.2018 21:00 & 43,256.0 \\
145364 & 2018.01.1 22:00 & 42,402.0 & 145390 & 2.08.2018 22:00 & 41,552.0 \\
145365 & 2018.01.1 23:00 & 40,164.0 & 145391 & 2.08.2018 23:00 & 38,500.0 \\
145366 & 2018.01.2 00:00 & 38,608.0 & 145392 & 3.08.2018 00:00 & 35,486.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: (a) First and last 5 rows of unedited raw data, (b) First and last 5 rows of edited data. Figure 1: (a) Unedited raw data time plot, (b) Edited raw data time plot. Figure 4 shows that consumption on weekdays is higher than consumption on weekends. Moreover, the highest difference occurs during daytime hours.