## 4 Data Analysis and Methodology
#### Data Collection and Integration
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Variable** & \begin{tabular}{l} **Null** \\ **Values** \\ \end{tabular} & \
\begin{tabular}{l} **Variable** \\ **Values** \\ \end{tabular} &
\begin{tabular}{l} **Null** \\ **Values** \\ \end{tabular} \\ \hline A1 & CLICOD & 0 & A35 & TNOV\_19 & 2229 \\ A2 & CLINOMABR & 0 & A36 & TDIC\_19 & 2041 \\ A3 & CLICLADIR & 0 & A37 & TENE\_20 & 1848 \\ A4 & PROV & 0 & A38 & TFE\_20 & 1603 \\ A5 & CANTON & 0 & A39 & TMAR\_20 & 1394 \\ A6 & AGENCY & 0 & A40 & TABR\_20 & 1242 \\ A7 & CAT\_TARIFF & 0 & A41 & TMAY\_20 & 1097 \\ A8 & TARIFF & 0 & A42 & TUN\_20 & 919 \\ A9 & BRAND & 0 & A43 & TJUL\_20 & 762 \\ A10 & SERIE & 79 & A44 & TAGO\_20 & 721 \\ A11 & SEQUEN & 79 & A45 & TSEP\_20 & 718 \\ A12 & YEAR & 79 & A46 & TOCT\_20 & 695 \\ A13 & DATE\_INSTALLED & 79 & A47 & TNOV\_20 & 639 \\ A14 & AMOUNT/DEBT & 0 & A48 & TDIC\_20 & 558 \\ A15 & MONTHS\_DEBT & 0 & A49 & TENE\_21 & 526 \\ A16 & DATE\_RE-INV & 26,144 & A50 & TFEB\_21 & 522 \\ A17 & P\_FACTOR & 0 & A51 & TMAR\_21 & 517 \\ A18 & TJUN\_18 & 3262 & A52 & TABR\_21 & 508 \\ A19 & TJUL\_18 & 3236 & A53 & TMAY\_21 & 413 \\ A20 & TAGO\_18 & 3222 & A54 & MEDIA & 0 \\ A21 & TSEP\_18 & 3209 & A55 & MEDIANA & 0 \\ A22 & TOCT\_18 & 3191 & A56 & MAX & 0 \\ A23 & TNOV\_18 & 3160 & A57 & MIN & 0 \\ A24 & TDIC\_18 & 3143 & A58 & RANGO & 0 \\ A25 & TENE\_19 & 3136 & A59 & DESV\_EST & 0 \\ A26 & TFEB\_19 & 3062 & A60 & COEF\_VAR & 0 \\ A27 & TMAR\_19 & 2949 & A61 & MEDIATRIZ & 0 \\ A28 & TABR\_19 & 2770 & A62 & NUM\_CEROS & 0 \\ A29 & TMAY\_19 & 2626 & A63 & REG1\_AUM & 0 \\ A30 & TJUN\_19 & 2400 & A64 & REG1\_DIS & 0 \\ A31 & TJUL\_19 & 2400 & A65 & REG\_AUM\_PRO & 0 \\ A32 & TAGO\_19 & 2400 & A66 & REG\_DIS\_PRO & 0 \\ A33 & TSEP\_19 & 2400 & A67 & REG\_AUM\_MED & 0 \\ A34 & TOCT\_19 & 2400 & A68 & REG\_DIS\_MED & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Number of null values in the variables. * **Residential**: Customers who use electricity for residential purposes. It is the tariff with the most customers. * **Commercial**: Customers who use electricity for commercial purposes, such as shops and offices. * **Industrial**: Customers who use electricity to produce products. * **Special**: Use of electricity in government institutions, hospitals, schools, churches, water boards, agricultural industries, universities, etc. * **Others**: Users who do not fall into the previously described categories. In Figure 7, the number of samples per tariff is observed, showing that the largest number of samples belongs to the RD tariff in the residential category. The tariffs found are encoded. The encoding is in Table 7.  
Figure 8 displays a correlation matrix of the principal variables analyzed in this study. In this matrix, correlation values are represented by a color scale, where lighter shades indicate high positive correlation (close to 1), darker shades represent strong negative correlation (close to \(-1\)), and intermediate shades reflect weaker or non-existent relationships (values close to 0). Similarly, the presence of well-defined blocks indicates that some variables may be highly correlated within certain groups, which can have implications for redundancy analysis or model building. A high correlation does not imply causality, but rather a statistical association between variables. Some categorical variables, such as CAT_TARIFF or YEAR, have very low correlations with the numerical variables, indicating that they do not directly influence quantitative trends. Additionally, well-defined blocks are observed in the matrix, indicating that certain variables have a strong relationship with